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flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl

flax-sentence-embeddings

--- annotations_creators: - found language_creators: - found language: - en license: - cc-by-nc-sa-4.0 multilinguality: - multilingual pretty_name: stackexchange size_categories: - unknown source_datasets: - original task_categories: - question-answering task_ids: - closed-domain-qa --- # Dataset Card Creation Guide ## Table of Contents - [Dataset Card Creation Guide](#dataset-card-creation-guide) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers)s - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [stackexchange](https://archive.org/details/stackexchange) - **Repository:** [flax-sentence-embeddings](https://github.com/nreimers/flax-sentence-embeddings) ### Dataset Summary We automatically extracted question and answer (Q&A) pairs from [Stack Exchange](https://stackexchange.com/) network. Stack Exchange gather many Q&A communities across 50 online plateform, including the well known Stack Overflow and other technical sites. 100 millon developpers consult Stack Exchange every month. The dataset is a parallel corpus with each question mapped to the top rated answer. The dataset is split given communities which cover a variety of domains from 3d printing, economics, raspberry pi or emacs. An exhaustive list of all communities is available [here](https://stackexchange.com/sites). ### Languages Stack Exchange mainly consist of english language (en). ## Dataset Structure ### Data Instances Each data samples is presented as follow: ``` {'title_body': "Is there a Stack Exchange icon available? StackAuth /sites route provides all the site's icons except for the one of the Stack Exchange master site.\nCould you please provide it in some way (a static SVG would be good)?", 'upvoted_answer': 'Here it is!\n\nDead link: SVG version here\nNote: the same restrictions on this trademarked icon that apply here, also apply to the icon above.', 'downvoted_answer': 'No, the /sites route is not the right place for that.\n\n/sites enumerates all websites that expose API end-points. StackExchange.com does not expose such an endpoint, so it does not (and will not) appear in the results.'} ``` This particular exampe corresponds to the [following page](https://stackapps.com/questions/1508/is-there-a-stack-exchange-icon-available) ### Data Fields The fields present in the dataset contain the following informations: - `title_body`: This is the concatenation of the title and body from the question - `upvoted_answer`: This is the body from the most upvoted answer ### Data Splits We provide multiple splits for this dataset, which each refers to a given community channel. We detail the number of pail for each split below: | | Number of pairs | | ----- | ------ | | gaming | 82,887 | | dba | 71,449 | | codereview | 41,748 | | gis | 100,254 | | english | 100,640 | | mathoverflow | 85,289 | | askubuntu | 267,135 | | electronics | 129,494 | | apple | 92,487 | | diy | 52,896 | | magento | 79,241 | | gamedev | 40,154 | | mathematica | 59,895 | | ell | 77,892 | | judaism | 26,085 | | drupal | 67,817 | | blender | 54,153 | | biology | 19,277 | | android | 38,077 | | crypto | 19,404 | | christianity | 11,498 | | cs | 30,010 | | academia | 32,137 | | chemistry | 27,061 | | aviation | 18,755 | | history | 10,766 | | japanese | 20,948 | | cooking | 22,641 | | law | 16,133 | | hermeneutics | 9,516 | | hinduism | 8,999 | | graphicdesign | 28,083 | | dsp | 17,430 | | bicycles | 15,708 | | ethereum | 26,124 | | ja | 17,376 | | arduino | 16,281 | | bitcoin | 22,474 | | islam | 10,052 | | datascience | 20,503 | | german | 13,733 | | codegolf | 8,211 | | boardgames | 11,805 | | economics | 8,844 | | emacs | 16,830 | | buddhism | 6,787 | | gardening | 13,246 | | astronomy | 9,086 | | anime | 10,131 | | fitness | 8,297 | | cstheory | 7,742 | | engineering | 8,649 | | chinese | 8,646 | | linguistics | 6,843 | | cogsci | 5,101 | | french | 10,578 | | literature | 3,539 | | ai | 5,763 | | craftcms | 11,236 | | health | 4,494 | | chess | 6,392 | | interpersonal | 3,398 | | expressionengine | 10,742 | | earthscience | 4,396 | | civicrm | 10,648 | | joomla | 5,887 | | homebrew | 5,608 | | latin | 3,969 | | ham | 3,501 | | hsm | 2,517 | | avp | 6,450 | | expatriates | 4,913 | | matheducators | 2,706 | | genealogy | 2,895 | | 3dprinting | 3,488 | | devops | 3,462 | | bioinformatics | 3,135 | | computergraphics | 2,306 | | elementaryos | 5,917 | | martialarts | 1,737 | | hardwarerecs | 2,050 | | lifehacks | 2,576 | | crafts | 1,659 | | italian | 3,101 | | freelancing | 1,663 | | materials | 1,101 | | bricks | 3,530 | | cseducators | 902 | | eosio | 1,940 | | iot | 1,359 | | languagelearning | 948 | | beer | 1,012 | | ebooks | 1,107 | | coffee | 1,188 | | esperanto | 1,466 | | korean | 1,406 | | cardano | 248 | | conlang | 334 | | drones | 496 | | iota | 775 | | salesforce | 87,272 | | wordpress | 83,621 | | rpg | 40,435 | | scifi | 54,805 | | stats | 115,679 | | serverfault | 238,507 | | physics | 141,230 | | sharepoint | 80,420 | | security | 51,355 | | worldbuilding | 26,210 | | softwareengineering | 51,326 | | superuser | 352,610 | | meta | 1,000 | | money | 29,404 | | travel | 36,533 | | photo | 23,204 | | webmasters | 30,370 | | workplace | 24,012 | | ux | 28,901 | | philosophy | 13,114 | | music | 19,936 | | politics | 11,047 | | movies | 18,243 | | space | 12,893 | | skeptics | 8,145 | | raspberrypi | 24,143 | | rus | 16,528 | | puzzling | 17,448 | | webapps | 24,867 | | mechanics | 18,613 | | writers | 9,867 | | networkengineering | 12,590 | | parenting | 5,998 | | softwarerecs | 11,761 | | quant | 12,933 | | spanish | 7,675 | | scicomp | 7,036 | | pets | 6,156 | | sqa | 9,256 | | sitecore | 7,838 | | vi | 9,000 | | outdoors | 5,278 | | sound | 8,303 | | pm | 5,435 | | reverseengineering | 5,817 | | retrocomputing | 3,907 | | tridion | 5,907 | | quantumcomputing | 4,320 | | sports | 4,707 | | robotics | 4,648 | | russian | 3,937 | | opensource | 3,221 | | woodworking | 2,955 | | ukrainian | 1,767 | | opendata | 3,842 | | patents | 3,573 | | mythology | 1,595 | | portuguese | 1,964 | | tor | 4,167 | | monero | 3,508 | | sustainability | 1,674 | | musicfans | 2,431 | | poker | 1,665 | | or | 1,490 | | windowsphone | 2,807 | | stackapps | 1,518 | | moderators | 504 | | vegetarianism | 585 | | tezos | 1,169 | | stellar | 1,078 | | pt | 103,277 | | unix | 155,414 | | tex | 171,628 | | ru | 253,289 | | total | 4,750,619 | ## Dataset Creation ### Curation Rationale We primary designed this dataset for sentence embeddings training. Indeed sentence embeddings may be trained using a contrastive learning setup for which the model is trained to associate each sentence with its corresponding pair out of multiple proposition. Such models require many examples to be efficient and thus the dataset creation may be tedious. Community networks such as Stack Exchange allow us to build many examples semi-automatically. ### Source Data The source data are dumps from [Stack Exchange](https://archive.org/details/stackexchange) #### Initial Data Collection and Normalization We collected the data from the math community. We filtered out questions which title or body length is bellow 20 characters and questions for which body length is above 4096 characters. #### Who are the source language producers? Questions and answers are written by the community developpers of Stack Exchange. ## Additional Information ### Licensing Information Please see the license information at: https://archive.org/details/stackexchange ### Citation Information ``` @misc{StackExchangeDataset, author = {Flax Sentence Embeddings Team}, title = {Stack Exchange question pairs}, year = {2021}, howpublished = {https://huggingface.co/datasets/flax-sentence-embeddings/}, } ``` ### Contributions Thanks to the Flax Sentence Embeddings team for adding this dataset.

mozilla-foundation/common_voice_13_0

mozilla-foundation

--- pretty_name: Common Voice Corpus 13.0 annotations_creators: - crowdsourced language_creators: - crowdsourced language_bcp47: - ab - ar - as - ast - az - ba - bas - be - bg - bn - br - ca - ckb - cnh - cs - cv - cy - da - de - dv - dyu - el - en - eo - es - et - eu - fa - fi - fr - fy-NL - ga-IE - gl - gn - ha - hi - hsb - hu - hy-AM - ia - id - ig - is - it - ja - ka - kab - kk - kmr - ko - ky - lg - lo - lt - lv - mdf - mhr - mk - ml - mn - mr - mrj - mt - myv - nan-tw - ne-NP - nl - nn-NO - oc - or - pa-IN - pl - pt - quy - rm-sursilv - rm-vallader - ro - ru - rw - sah - sat - sc - sk - skr - sl - sr - sv-SE - sw - ta - th - ti - tig - tk - tok - tr - tt - tw - ug - uk - ur - uz - vi - vot - yo - yue - zh-CN - zh-HK - zh-TW license: - cc0-1.0 multilinguality: - multilingual size_categories: ab: - 10K<n<100K ar: - 100K<n<1M as: - 1K<n<10K ast: - 1K<n<10K az: - n<1K ba: - 100K<n<1M bas: - 1K<n<10K be: - 1M<n<10M bg: - 10K<n<100K bn: - 1M<n<10M br: - 10K<n<100K ca: - 1M<n<10M ckb: - 100K<n<1M cnh: - 1K<n<10K cs: - 100K<n<1M cv: - 10K<n<100K cy: - 100K<n<1M da: - 10K<n<100K de: - 100K<n<1M dv: - 10K<n<100K dyu: - n<1K el: - 10K<n<100K en: - 1M<n<10M eo: - 1M<n<10M es: - 1M<n<10M et: - 10K<n<100K eu: - 100K<n<1M fa: - 100K<n<1M fi: - 10K<n<100K fr: - 100K<n<1M fy-NL: - 100K<n<1M ga-IE: - 10K<n<100K gl: - 10K<n<100K gn: - 1K<n<10K ha: - 10K<n<100K hi: - 10K<n<100K hsb: - 1K<n<10K hu: - 10K<n<100K hy-AM: - 1K<n<10K ia: - 10K<n<100K id: - 10K<n<100K ig: - 1K<n<10K is: - n<1K it: - 100K<n<1M ja: - 100K<n<1M ka: - 10K<n<100K kab: - 100K<n<1M kk: - 1K<n<10K kmr: - 10K<n<100K ko: - 1K<n<10K ky: - 10K<n<100K lg: - 100K<n<1M lo: - n<1K lt: - 10K<n<100K lv: - 10K<n<100K mdf: - n<1K mhr: - 100K<n<1M mk: - n<1K ml: - 1K<n<10K mn: - 10K<n<100K mr: - 10K<n<100K mrj: - 10K<n<100K mt: - 10K<n<100K myv: - 1K<n<10K nan-tw: - 10K<n<100K ne-NP: - n<1K nl: - 10K<n<100K nn-NO: - n<1K oc: - 1K<n<10K or: - 1K<n<10K pa-IN: - 1K<n<10K pl: - 100K<n<1M pt: - 100K<n<1M quy: - n<1K rm-sursilv: - 1K<n<10K rm-vallader: - 1K<n<10K ro: - 10K<n<100K ru: - 100K<n<1M rw: - 1M<n<10M sah: - 1K<n<10K sat: - n<1K sc: - 1K<n<10K sk: - 10K<n<100K skr: - 1K<n<10K sl: - 10K<n<100K sr: - 1K<n<10K sv-SE: - 10K<n<100K sw: - 100K<n<1M ta: - 100K<n<1M th: - 100K<n<1M ti: - n<1K tig: - n<1K tk: - 1K<n<10K tok: - 10K<n<100K tr: - 10K<n<100K tt: - 10K<n<100K tw: - n<1K ug: - 10K<n<100K uk: - 10K<n<100K ur: - 100K<n<1M uz: - 100K<n<1M vi: - 10K<n<100K vot: - n<1K yo: - 1K<n<10K yue: - 10K<n<100K zh-CN: - 100K<n<1M zh-HK: - 100K<n<1M zh-TW: - 100K<n<1M source_datasets: - extended|common_voice task_categories: - automatic-speech-recognition paperswithcode_id: common-voice extra_gated_prompt: "By clicking on “Access repository” below, you also agree to not attempt to determine the identity of speakers in the Common Voice dataset." --- # Dataset Card for Common Voice Corpus 13.0 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [How to use](#how-to-use) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://commonvoice.mozilla.org/en/datasets - **Repository:** https://github.com/common-voice/common-voice - **Paper:** https://arxiv.org/abs/1912.06670 - **Leaderboard:** https://paperswithcode.com/dataset/common-voice - **Point of Contact:** [Vaibhav Srivastav](mailto:vaibhav@huggingface.co) ### Dataset Summary The Common Voice dataset consists of a unique MP3 and corresponding text file. Many of the 27141 recorded hours in the dataset also include demographic metadata like age, sex, and accent that can help improve the accuracy of speech recognition engines. The dataset currently consists of 17689 validated hours in 108 languages, but more voices and languages are always added. Take a look at the [Languages](https://commonvoice.mozilla.org/en/languages) page to request a language or start contributing. ### Supported Tasks and Leaderboards The results for models trained on the Common Voice datasets are available via the [🤗 Autoevaluate Leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=mozilla-foundation%2Fcommon_voice_11_0&only_verified=0&task=automatic-speech-recognition&config=ar&split=test&metric=wer) ### Languages ``` Abkhaz, Arabic, Armenian, Assamese, Asturian, Azerbaijani, Basaa, Bashkir, Basque, Belarusian, Bengali, Breton, Bulgarian, Cantonese, Catalan, Central Kurdish, Chinese (China), Chinese (Hong Kong), Chinese (Taiwan), Chuvash, Czech, Danish, Dhivehi, Dioula, Dutch, English, Erzya, Esperanto, Estonian, Finnish, French, Frisian, Galician, Georgian, German, Greek, Guarani, Hakha Chin, Hausa, Hill Mari, Hindi, Hungarian, Icelandic, Igbo, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kazakh, Kinyarwanda, Korean, Kurmanji Kurdish, Kyrgyz, Lao, Latvian, Lithuanian, Luganda, Macedonian, Malayalam, Maltese, Marathi, Meadow Mari, Moksha, Mongolian, Nepali, Norwegian Nynorsk, Occitan, Odia, Persian, Polish, Portuguese, Punjabi, Quechua Chanka, Romanian, Romansh Sursilvan, Romansh Vallader, Russian, Sakha, Santali (Ol Chiki), Saraiki, Sardinian, Serbian, Slovak, Slovenian, Sorbian, Upper, Spanish, Swahili, Swedish, Taiwanese (Minnan), Tamil, Tatar, Thai, Tigre, Tigrinya, Toki Pona, Turkish, Turkmen, Twi, Ukrainian, Urdu, Uyghur, Uzbek, Vietnamese, Votic, Welsh, Yoruba ``` ## How to use The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function. For example, to download the Hindi config, simply specify the corresponding language config name (i.e., "hi" for Hindi): ```python from datasets import load_dataset cv_13 = load_dataset("mozilla-foundation/common_voice_13_0", "hi", split="train") ``` Using the datasets library, you can also stream the dataset on-the-fly by adding a `streaming=True` argument to the `load_dataset` function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk. ```python from datasets import load_dataset cv_13 = load_dataset("mozilla-foundation/common_voice_13_0", "hi", split="train", streaming=True) print(next(iter(cv_13))) ``` *Bonus*: create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch) directly with your own datasets (local/streamed). ### Local ```python from datasets import load_dataset from torch.utils.data.sampler import BatchSampler, RandomSampler cv_13 = load_dataset("mozilla-foundation/common_voice_13_0", "hi", split="train") batch_sampler = BatchSampler(RandomSampler(cv_13), batch_size=32, drop_last=False) dataloader = DataLoader(cv_13, batch_sampler=batch_sampler) ``` ### Streaming ```python from datasets import load_dataset from torch.utils.data import DataLoader cv_13 = load_dataset("mozilla-foundation/common_voice_13_0", "hi", split="train") dataloader = DataLoader(cv_13, batch_size=32) ``` To find out more about loading and preparing audio datasets, head over to [hf.co/blog/audio-datasets](https://huggingface.co/blog/audio-datasets). ### Example scripts Train your own CTC or Seq2Seq Automatic Speech Recognition models on Common Voice 13 with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition). ## Dataset Structure ### Data Instances A typical data point comprises the `path` to the audio file and its `sentence`. Additional fields include `accent`, `age`, `client_id`, `up_votes`, `down_votes`, `gender`, `locale` and `segment`. ```python { 'client_id': 'd59478fbc1ee646a28a3c652a119379939123784d99131b865a89f8b21c81f69276c48bd574b81267d9d1a77b83b43e6d475a6cfc79c232ddbca946ae9c7afc5', 'path': 'et/clips/common_voice_et_18318995.mp3', 'audio': { 'path': 'et/clips/common_voice_et_18318995.mp3', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 48000 }, 'sentence': 'Tasub kokku saada inimestega, keda tunned juba ammust ajast saati.', 'up_votes': 2, 'down_votes': 0, 'age': 'twenties', 'gender': 'male', 'accent': '', 'locale': 'et', 'segment': '' } ``` ### Data Fields `client_id` (`string`): An id for which client (voice) made the recording `path` (`string`): The path to the audio file `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`. `sentence` (`string`): The sentence the user was prompted to speak `up_votes` (`int64`): How many upvotes the audio file has received from reviewers `down_votes` (`int64`): How many downvotes the audio file has received from reviewers `age` (`string`): The age of the speaker (e.g. `teens`, `twenties`, `fifties`) `gender` (`string`): The gender of the speaker `accent` (`string`): Accent of the speaker `locale` (`string`): The locale of the speaker `segment` (`string`): Usually an empty field ### Data Splits The speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other. The validated data is data that has been validated with reviewers and received upvotes that the data is of high quality. The invalidated data is data has been invalidated by reviewers and received downvotes indicating that the data is of low quality. The reported data is data that has been reported, for different reasons. The other data is data that has not yet been reviewed. The dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train. ## Data Preprocessing Recommended by Hugging Face The following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. Many examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_. In addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, **almost all** sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation. ```python from datasets import load_dataset ds = load_dataset("mozilla-foundation/common_voice_13_0", "en", use_auth_token=True) def prepare_dataset(batch): """Function to preprocess the dataset with the .map method""" transcription = batch["sentence"] if transcription.startswith('"') and transcription.endswith('"'): # we can remove trailing quotation marks as they do not affect the transcription transcription = transcription[1:-1] if transcription[-1] not in [".", "?", "!"]: # append a full-stop to sentences that do not end in punctuation transcription = transcription + "." batch["sentence"] = transcription return batch ds = ds.map(prepare_dataset, desc="preprocess dataset") ``` ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset. ## Considerations for Using the Data ### Social Impact of Dataset The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset. ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Public Domain, [CC-0](https://creativecommons.org/share-your-work/public-domain/cc0/) ### Citation Information ``` @inproceedings{commonvoice:2020, author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.}, title = {Common Voice: A Massively-Multilingual Speech Corpus}, booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)}, pages = {4211--4215}, year = 2020 } ```

shunk031/JGLUE

shunk031

--- annotations_creators: - crowdsourced language: - ja language_creators: - crowdsourced - found license: - cc-by-4.0 multilinguality: - monolingual pretty_name: JGLUE size_categories: [] source_datasets: - original tags: - MARC - CoLA - STS - NLI - SQuAD - CommonsenseQA task_categories: - multiple-choice - question-answering - sentence-similarity - text-classification task_ids: - multiple-choice-qa - open-domain-qa - multi-class-classification - sentiment-classification --- # Dataset Card for JGLUE [](https://github.com/shunk031/huggingface-datasets_JGLUE/actions/workflows/ci.yaml) [](https://aclanthology.org/2022.lrec-1.317) This dataset loading script is developed on [GitHub](https://github.com/shunk031/huggingface-datasets_JGLUE). Please feel free to open an [issue](https://github.com/shunk031/huggingface-datasets_JGLUE/issues/new/choose) or [pull request](https://github.com/shunk031/huggingface-datasets_JGLUE/pulls). ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/yahoojapan/JGLUE - **Repository:** https://github.com/shunk031/huggingface-datasets_JGLUE ### Dataset Summary From [JGLUE's README.md](https://github.com/yahoojapan/JGLUE#jglue-japanese-general-language-understanding-evaluation): > JGLUE, Japanese General Language Understanding Evaluation, is built to measure the general NLU ability in Japanese. JGLUE has been constructed from scratch without translation. We hope that JGLUE will facilitate NLU research in Japanese. > JGLUE has been constructed by a joint research project of Yahoo Japan Corporation and Kawahara Lab at Waseda University. ### Supported Tasks and Leaderboards From [JGLUE's README.md](https://github.com/yahoojapan/JGLUE#tasksdatasets): > JGLUE consists of the tasks of text classification, sentence pair classification, and QA. Each task consists of multiple datasets. #### Supported Tasks ##### MARC-ja From [JGLUE's README.md](https://github.com/yahoojapan/JGLUE#marc-ja): > MARC-ja is a dataset of the text classification task. This dataset is based on the Japanese portion of [Multilingual Amazon Reviews Corpus (MARC)](https://docs.opendata.aws/amazon-reviews-ml/readme.html) ([Keung+, 2020](https://aclanthology.org/2020.emnlp-main.369/)). ##### JCoLA From [JCoLA's README.md](https://github.com/osekilab/JCoLA#jcola-japanese-corpus-of-linguistic-acceptability) > JCoLA (Japanese Corpus of Linguistic Accept010 ability) is a novel dataset for targeted syntactic evaluations of language models in Japanese, which consists of 10,020 sentences with acceptability judgments by linguists. The sentences are manually extracted from linguistics journals, handbooks and textbooks. JCoLA is included in [JGLUE benchmark](https://github.com/yahoojapan/JGLUE) (Kurihara et al., 2022). ##### JSTS From [JGLUE's README.md](https://github.com/yahoojapan/JGLUE#jsts): > JSTS is a Japanese version of the STS (Semantic Textual Similarity) dataset. STS is a task to estimate the semantic similarity of a sentence pair. The sentences in JSTS and JNLI (described below) are extracted from the Japanese version of the MS COCO Caption Dataset, [the YJ Captions Dataset](https://github.com/yahoojapan/YJCaptions) ([Miyazaki and Shimizu, 2016](https://aclanthology.org/P16-1168/)). ##### JNLI From [JGLUE's README.md](https://github.com/yahoojapan/JGLUE#jnli): > JNLI is a Japanese version of the NLI (Natural Language Inference) dataset. NLI is a task to recognize the inference relation that a premise sentence has to a hypothesis sentence. The inference relations are entailment, contradiction, and neutral. ##### JSQuAD From [JGLUE's README.md](https://github.com/yahoojapan/JGLUE#jsquad): > JSQuAD is a Japanese version of [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) ([Rajpurkar+, 2018](https://aclanthology.org/P18-2124/)), one of the datasets of reading comprehension. Each instance in the dataset consists of a question regarding a given context (Wikipedia article) and its answer. JSQuAD is based on SQuAD 1.1 (there are no unanswerable questions). We used [the Japanese Wikipedia dump](https://dumps.wikimedia.org/jawiki/) as of 20211101. ##### JCommonsenseQA From [JGLUE's README.md](https://github.com/yahoojapan/JGLUE#jcommonsenseqa): > JCommonsenseQA is a Japanese version of [CommonsenseQA](https://www.tau-nlp.org/commonsenseqa) ([Talmor+, 2019](https://aclanthology.org/N19-1421/)), which is a multiple-choice question answering dataset that requires commonsense reasoning ability. It is built using crowdsourcing with seeds extracted from the knowledge base [ConceptNet](https://conceptnet.io/). #### Leaderboard From [JGLUE's README.md](https://github.com/yahoojapan/JGLUE#leaderboard): > A leaderboard will be made public soon. The test set will be released at that time. ### Languages The language data in JGLUE is in Japanese ([BCP-47 ja-JP](https://www.rfc-editor.org/info/bcp47)). ## Dataset Structure ### Data Instances When loading a specific configuration, users has to append a version dependent suffix: #### MARC-ja ```python from datasets import load_dataset dataset = load_dataset("shunk031/JGLUE", name="MARC-ja") print(dataset) # DatasetDict({ # train: Dataset({ # features: ['sentence', 'label', 'review_id'], # num_rows: 187528 # }) # validation: Dataset({ # features: ['sentence', 'label', 'review_id'], # num_rows: 5654 # }) # }) ``` #### JCoLA ```python from datasets import load_dataset dataset = load_dataset("shunk031/JGLUE", name="JCoLA") print(dataset) # DatasetDict({ # train: Dataset({ # features: ['uid', 'source', 'label', 'diacritic', 'sentence', 'original', 'translation', 'gloss', 'simple', 'linguistic_phenomenon'], # num_rows: 6919 # }) # validation: Dataset({ # features: ['uid', 'source', 'label', 'diacritic', 'sentence', 'original', 'translation', 'gloss', 'simple', 'linguistic_phenomenon'], # num_rows: 865 # }) # validation_out_of_domain: Dataset({ # features: ['uid', 'source', 'label', 'diacritic', 'sentence', 'original', 'translation', 'gloss', 'simple', 'linguistic_phenomenon'], # num_rows: 685 # }) # validation_out_of_domain_annotated: Dataset({ # features: ['uid', 'source', 'label', 'diacritic', 'sentence', 'original', 'translation', 'gloss', 'simple', 'linguistic_phenomenon'], # num_rows: 685 # }) # }) ``` An example of the JCoLA dataset (validation - out of domain annotated) looks as follows: ```json { "uid": 9109, "source": "Asano_and_Ura_2010", "label": 1, "diacritic": "g", "sentence": "太郎のゴミの捨て方について話した。", "original": "太郎のゴミの捨て方", "translation": "‘The way (for Taro) to throw out garbage’", "gloss": true, "linguistic_phenomenon": { "argument_structure": true, "binding": false, "control_raising": false, "ellipsis": false, "filler_gap": false, "island_effects": false, "morphology": false, "nominal_structure": false, "negative_polarity_concord_items": false, "quantifier": false, "verbal_agreement": false, "simple": false } } ``` #### JSTS ```python from datasets import load_dataset dataset = load_dataset("shunk031/JGLUE", name="JSTS") print(dataset) # DatasetDict({ # train: Dataset({ # features: ['sentence_pair_id', 'yjcaptions_id', 'sentence1', 'sentence2', 'label'], # num_rows: 12451 # }) # validation: Dataset({ # features: ['sentence_pair_id', 'yjcaptions_id', 'sentence1', 'sentence2', 'label'], # num_rows: 1457 # }) # }) ``` An example of the JSTS dataset looks as follows: ```json { "sentence_pair_id": "691", "yjcaptions_id": "127202-129817-129818", "sentence1": "街中の道路を大きなバスが走っています。 (A big bus is running on the road in the city.)", "sentence2": "道路を大きなバスが走っています。 (There is a big bus running on the road.)", "label": 4.4 } ``` #### JNLI ```python from datasets import load_dataset dataset = load_dataset("shunk031/JGLUE", name="JNLI") print(dataset) # DatasetDict({ # train: Dataset({ # features: ['sentence_pair_id', 'yjcaptions_id', 'sentence1', 'sentence2', 'label'], # num_rows: 20073 # }) # validation: Dataset({ # features: ['sentence_pair_id', 'yjcaptions_id', 'sentence1', 'sentence2', 'label'], # num_rows: 2434 # }) # }) ``` An example of the JNLI dataset looks as follows: ```json { "sentence_pair_id": "1157", "yjcaptions_id": "127202-129817-129818", "sentence1": "街中の道路を大きなバスが走っています。 (A big bus is running on the road in the city.)", "sentence2": "道路を大きなバスが走っています。 (There is a big bus running on the road.)", "label": "entailment" } ``` #### JSQuAD ```python from datasets import load_dataset dataset = load_dataset("shunk031/JGLUE", name="JSQuAD") print(dataset) # DatasetDict({ # train: Dataset({ # features: ['id', 'title', 'context', 'question', 'answers', 'is_impossible'], # num_rows: 62859 # }) # validation: Dataset({ # features: ['id', 'title', 'context', 'question', 'answers', 'is_impossible'], # num_rows: 4442 # }) # }) ``` An example of the JSQuAD looks as follows: ```json { "id": "a1531320p0q0", "title": "東海道新幹線", "context": "東海道新幹線 [SEP] 1987 年（昭和 62 年）4 月 1 日の国鉄分割民営化により、JR 東海が運営を継承した。西日本旅客鉄道（JR 西日本）が継承した山陽新幹線とは相互乗り入れが行われており、東海道新幹線区間のみで運転される列車にも JR 西日本所有の車両が使用されることがある。2020 年（令和 2 年）3 月現在、東京駅 - 新大阪駅間の所要時間は最速 2 時間 21 分、最高速度 285 km/h で運行されている。", "question": "2020 年（令和 2 年）3 月現在、東京駅 - 新大阪駅間の最高速度はどのくらいか。", "answers": { "text": ["285 km/h"], "answer_start": [182] }, "is_impossible": false } ``` #### JCommonsenseQA ```python from datasets import load_dataset dataset = load_dataset("shunk031/JGLUE", name="JCommonsenseQA") print(dataset) # DatasetDict({ # train: Dataset({ # features: ['q_id', 'question', 'choice0', 'choice1', 'choice2', 'choice3', 'choice4', 'label'], # num_rows: 8939 # }) # validation: Dataset({ # features: ['q_id', 'question', 'choice0', 'choice1', 'choice2', 'choice3', 'choice4', 'label'], # num_rows: 1119 # }) # }) ``` An example of the JCommonsenseQA looks as follows: ```json { "q_id": 3016, "question": "会社の最高責任者を何というか？ (What do you call the chief executive officer of a company?)", "choice0": "社長 (president)", "choice1": "教師 (teacher)", "choice2": "部長 (manager)", "choice3": "バイト (part-time worker)", "choice4": "部下 (subordinate)", "label": 0 } ``` ### Data Fields #### MARC-ja - `sentence_pair_id`: ID of the sentence pair - `yjcaptions_id`: sentence ids in yjcaptions (explained below) - `sentence1`: first sentence - `sentence2`: second sentence - `label`: sentence similarity: 5 (equivalent meaning) - 0 (completely different meaning) ##### Explanation for `yjcaptions_id` From [JGLUE's README.md](https://github.com/yahoojapan/JGLUE#explanation-for-yjcaptions_id), there are the following two cases: 1. sentence pairs in one image: `(image id)-(sentence1 id)-(sentence2 id)` - e.g., 723-844-847 - a sentence id starting with "g" means a sentence generated by a crowdworker (e.g., 69501-75698-g103): only for JNLI 2. sentence pairs in two images: `(image id of sentence1)_(image id of sentence2)-(sentence1 id)-(sentence2 id)` - e.g., 91337_217583-96105-91680 #### JCoLA From [JCoLA's README.md](https://github.com/osekilab/JCoLA#data-description) and [JCoLA's paper](https://arxiv.org/abs/2309.12676) - `uid`: unique id of the sentence - `source`: author and the year of publication of the source article - `label`: acceptability judgement label (0 for unacceptable, 1 for acceptable) - `diacritic`: acceptability judgement as originally notated in the source article - `sentence`: sentence (modified by the author if needed) - `original`: original sentence as presented in the source article - `translation`: English translation of the sentence as presentend in the source article (if any) - `gloss`: gloss of the sentence as presented in the source article (if any) - `linguistic_phenomenon` - `argument_structure`: acceptability judgements based on the order of arguments and case marking - `binding`: acceptability judgements based on the binding of noun phrases - `control_raising`: acceptability judgements based on predicates that are categorized as control or raising - `ellipsis`: acceptability judgements based on the possibility of omitting elements in the sentences - `filler_gap`: acceptability judgements based on the dependency between the moved element and the gap - `island effects`: acceptability judgements based on the restrictions on filler-gap dependencies such as wh-movements - `morphology`: acceptability judgements based on the morphology - `nominal_structure`: acceptability judgements based on the internal structure of noun phrases - `negative_polarity_concord_items`: acceptability judgements based on the restrictions on where negative polarity/concord items (NPIs/NCIs) can appear - `quantifiers`: acceptability judgements based on the distribution of quantifiers such as floating quantifiers - `verbal_agreement`: acceptability judgements based on the dependency between subjects and verbs - `simple`: acceptability judgements that do not have marked syntactic structures #### JNLI - `sentence_pair_id`: ID of the sentence pair - `yjcaptions_id`: sentence ids in the yjcaptions - `sentence1`: premise sentence - `sentence2`: hypothesis sentence - `label`: inference relation #### JSQuAD - `title`: title of a Wikipedia article - `paragraphs`: a set of paragraphs - `qas`: a set of pairs of a question and its answer - `question`: question - `id`: id of a question - `answers`: a set of answers - `text`: answer text - `answer_start`: start position (character index) - `is_impossible`: all the values are false - `context`: a concatenation of the title and paragraph #### JCommonsenseQA - `q_id`: ID of the question - `question`: question - `choice{0..4}`: choice - `label`: correct choice id ### Data Splits From [JGLUE's README.md](https://github.com/yahoojapan/JGLUE/blob/main/README.md#tasksdatasets): > Only train/dev sets are available now, and the test set will be available after the leaderboard is made public. From [JCoLA's paper](https://arxiv.org/abs/2309.12676): > The in-domain data is split into training data (6,919 instances), development data (865 instances), and test data (865 instances). On the other hand, the out-of-domain data is only used for evaluation, and divided into development data (685 instances) and test data (686 instances). | Task | Dataset | Train | Dev | Test | |------------------------------|----------------|--------:|------:|------:| | Text Classification | MARC-ja | 187,528 | 5,654 | 5,639 | | | JCoLA | 6,919 | 865&dagger; / 685&ddagger; | 865&dagger; / 685&ddagger; | | Sentence Pair Classification | JSTS | 12,451 | 1,457 | 1,589 | | | JNLI | 20,073 | 2,434 | 2,508 | | Question Answering | JSQuAD | 62,859 | 4,442 | 4,420 | | | JCommonsenseQA | 8,939 | 1,119 | 1,118 | > JCoLA: &dagger; in domain. &ddagger; out of domain. ## Dataset Creation ### Curation Rationale From [JGLUE's paper](https://aclanthology.org/2022.lrec-1.317/): > JGLUE is designed to cover a wide range of GLUE and SuperGLUE tasks and consists of three kinds of tasks: text classification, sentence pair classification, and question answering. ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? - The source language producers are users of Amazon (MARC-ja), crowd-workers of [Yahoo! Crowdsourcing](https://crowdsourcing.yahoo.co.jp/) (JSTS, JNLI and JCommonsenseQA), writers of the Japanese Wikipedia (JSQuAD), crowd-workers of [Lancers](https://www.lancers.jp/). ### Annotations #### Annotation process ##### MARC-ja From [JGLUE's paper](https://aclanthology.org/2022.lrec-1.317/): > As one of the text classification datasets, we build a dataset based on the Multilingual Amazon Reviews Corpus (MARC) (Keung et al., 2020). MARC is a multilingual corpus of product reviews with 5-level star ratings (1-5) on the Amazon shopping site. This corpus covers six languages, including English and Japanese. For JGLUE, we use the Japanese part of MARC and to make it easy for both humans and computers to judge a class label, we cast the text classification task as a binary classification task, where 1- and 2-star ratings are converted to “negative”, and 4 and 5 are converted to “positive”. We do not use reviews with a 3-star rating. > One of the problems with MARC is that it sometimes contains data where the rating diverges from the review text. This happens, for example, when a review with positive content is given a rating of 1 or 2. These data degrade the quality of our dataset. To improve the quality of the dev/test instances used for evaluation, we crowdsource a positive/negative judgment task for approximately 12,000 reviews. We adopt only reviews with the same votes from 7 or more out of 10 workers and assign a label of the maximum votes to these reviews. We divide the resulting reviews into dev/test data. > We obtained 5,654 and 5,639 instances for the dev and test data, respectively, through the above procedure. For the training data, we extracted 187,528 instances directly from MARC without performing the cleaning procedure because of the large number of training instances. The statistics of MARC-ja are listed in Table 2. For the evaluation metric for MARC-ja, we use accuracy because it is a binary classification task of texts. ##### JCoLA From [JCoLA's paper](https://arxiv.org/abs/2309.12676): > ### 3 JCoLA > In this study, we introduce JCoLA (Japanese Corpus of Linguistic Acceptability), which will be the first large-scale acceptability judgment task dataset focusing on Japanese. JCoLA consists of sentences from textbooks and handbooks on Japanese syntax, as well as from journal articles on Japanese syntax that are published in JEAL (Journal of East Asian Linguistics), one of the prestigious journals in theoretical linguistics. > #### 3.1 Data Collection > Sentences in JCoLA were collected from prominent textbooks and handbooks focusing on Japanese syntax. In addition to the main text, example sentences included in the footnotes were also considered for collection. We also collected acceptability judgments from journal articles on Japanese syntax published in JEAL (Journal of East Asian Linguistics): one of the prestigious journals in the-oretical linguistics. Specifically, we examined all the articles published in JEAL between 2006 and 2015 (133 papers in total), and extracted 2,252 acceptability judgments from 26 papers on Japanese syntax (Table 2). Acceptability judgments include sentences in appendices and footnotes, but not sentences presented for analyses of syntactic structures (e.g. sentences with brackets to show their syntactic structures). As a result, a total of 11,984 example. sentences were collected. Using this as a basis, JCoLA was constructed through the methodology explained in the following sections. ##### JSTS and JNLI From [JGLUE's paper](https://aclanthology.org/2022.lrec-1.317/): > For the sentence pair classification datasets, we construct a semantic textual similarity (STS) dataset, JSTS, and a natural language inference (NLI) dataset, JNLI. > ### Overview > STS is a task of estimating the semantic similarity of a sentence pair. Gold similarity is usually assigned as an average of the integer values 0 (completely different meaning) to 5 (equivalent meaning) assigned by multiple workers through crowdsourcing. > NLI is a task of recognizing the inference relation that a premise sentence has to a hypothesis sentence. Inference relations are generally defined by three labels: “entailment”, “contradiction”, and “neutral”. Gold inference relations are often assigned by majority voting after collecting answers from multiple workers through crowdsourcing. > For the STS and NLI tasks, STS-B (Cer et al., 2017) and MultiNLI (Williams et al., 2018) are included in GLUE, respectively. As Japanese datasets, JSNLI (Yoshikoshi et al., 2020) is a machine translated dataset of the NLI dataset SNLI (Stanford NLI), and JSICK (Yanaka and Mineshima, 2021) is a human translated dataset of the STS/NLI dataset SICK (Marelli et al., 2014). As mentioned in Section 1, these have problems originating from automatic/manual translations. To solve this problem, we construct STS/NLI datasets in Japanese from scratch. We basically extract sentence pairs in JSTS and JNLI from the Japanese version of the MS COCO Caption Dataset (Chen et al., 2015), the YJ Captions Dataset (Miyazaki and Shimizu, 2016). Most of the sentence pairs in JSTS and JNLI overlap, allowing us to analyze the relationship between similarities and inference relations for the same sentence pairs like SICK and JSICK. > The similarity value in JSTS is assigned a real number from 0 to 5 as in STS-B. The inference relation in JNLI is assigned from the above three labels as in SNLI and MultiNLI. The definitions of the inference relations are also based on SNLI. > ### Method of Construction > Our construction flow for JSTS and JNLI is shown in Figure 1. Basically, two captions for the same image of YJ Captions are used as sentence pairs. For these sentence pairs, similarities and NLI relations of entailment and neutral are obtained by crowdsourcing. However, it is difficult to collect sentence pairs with low similarity and contradiction relations from captions for the same image. To solve this problem, we collect sentence pairs with low similarity from captions for different images. We collect contradiction relations by asking workers to write contradictory sentences for a given caption. > The detailed construction procedure for JSTS and JNLI is described below. > 1. We crowdsource an STS task using two captions for the same image from YJ Captions. We ask five workers to answer the similarity between two captions and take the mean value as the gold similarity. We delete sentence pairs with a large variance in the answers because such pairs have poor answer quality. We performed this task on 16,000 sentence pairs and deleted sentence pairs with a similarity variance of 1.0 or higher, resulting in the collection of 10,236 sentence pairs with gold similarity. We refer to this collected data as JSTS-A. > 2. To collect sentence pairs with low similarity, we crowdsource the same STS task as Step 1 using sentence pairs of captions for different images. We conducted this task on 4,000 sentence pairs and collected 2,970 sentence pairs with gold similarity. We refer to this collected data as JSTS-B. > 3. For JSTS-A, we crowdsource an NLI task. Since inference relations are directional, we obtain inference relations in both directions for sentence pairs. As mentioned earlier,it is difficult to collect instances of contradiction from JSTS-A, which was collected from the captions of the same images,and thus we collect instances of entailment and neutral in this step. We collect inference relation answers from 10 workers. If six or more people give the same answer, we adopt it as the gold label if it is entailment or neutral. To obtain inference relations in both directions for JSTS-A, we performed this task on 20,472 sentence pairs, twice as many as JSTS-A. As a result, we collected inference relations for 17,501 sentence pairs. We refer to this collected data as JNLI-A. We do not use JSTS-B for the NLI task because it is difficult to define and determine the inference relations between captions of different images. > 4. To collect NLI instances of contradiction, we crowdsource a task of writing four contradictory sentences for each caption in YJCaptions. From the written sentences, we remove sentence pairs with an edit distance of 0.75 or higher to remove low-quality sentences, such as short sentences and sentences with low relevance to the original sentence. Furthermore, we perform a one-way NLI task with 10 workers to verify whether the created sentence pairs are contradictory. Only the sentence pairs answered as contradiction by at least six workers are adopted. Finally,since the contradiction relation has no direction, we automatically assign contradiction in the opposite direction of the adopted sentence pairs. Using 1,800 captions, we acquired 7,200 sentence pairs, from which we collected 3,779 sentence pairs to which we assigned the one-way contradiction relation.By automatically assigning the contradiction relation in the opposite direction, we doubled the number of instances to 7,558. We refer to this collected data as JNLI-C. > 5. For the 3,779 sentence pairs collected in Step 4, we crowdsource an STS task, assigning similarity and filtering in the same way as in Steps1 and 2. In this way, we collected 2,303 sentence pairs with gold similarity from 3,779 pairs. We refer to this collected data as JSTS-C. ##### JSQuAD From [JGLUE's paper](https://aclanthology.org/2022.lrec-1.317/): > As QA datasets, we build a Japanese version of SQuAD (Rajpurkar et al., 2016), one of the datasets of reading comprehension, and a Japanese version ofCommonsenseQA, which is explained in the next section. > Reading comprehension is the task of reading a document and answering questions about it. Many reading comprehension evaluation sets have been built in English, followed by those in other languages or multilingual ones. > In Japanese, reading comprehension datasets for quizzes (Suzukietal.,2018) and those in the drivingdomain (Takahashi et al., 2019) have been built, but none are in the general domain. We use Wikipedia to build a dataset for the general domain. The construction process is basically based on SQuAD 1.1 (Rajpurkar et al., 2016). > First, to extract high-quality articles from Wikipedia, we use Nayuki, which estimates the quality of articles on the basis of hyperlinks in Wikipedia. We randomly chose 822 articles from the top-ranked 10,000 articles. For example, the articles include “熊本県 (Kumamoto Prefecture)” and “フランス料理 (French cuisine)”. Next, we divide an article into paragraphs, present each paragraph to crowdworkers, and ask them to write questions and answers that can be answered if one understands the paragraph. Figure 2 shows an example of JSQuAD. We ask workers to write two additional answers for the dev and test sets to make the system evaluation robust. ##### JCommonsenseQA From [JGLUE's paper](https://aclanthology.org/2022.lrec-1.317/): > ### Overview > JCommonsenseQA is a Japanese version of CommonsenseQA (Talmor et al., 2019), which consists of five choice QA to evaluate commonsense reasoning ability. Figure 3 shows examples of JCommonsenseQA. In the same way as CommonsenseQA, JCommonsenseQA is built using crowdsourcing with seeds extracted from the knowledge base ConceptNet (Speer et al., 2017). ConceptNet is a multilingual knowledge base that consists of triplets of two concepts and their relation. The triplets are directional and represented as (source concept, relation, target concept), for example (bullet train, AtLocation, station). > ### Method of Construction > The construction flow for JCommonsenseQA is shown in Figure 4. First, we collect question sets (QSs) from ConceptNet, each of which consists of a source concept and three target concepts that have the same relation to the source concept. Next, for each QS, we crowdAtLocation 2961source a task of writing a question with only one target concept as the answer and a task of adding two distractors. We describe the detailed construction procedure for JCommonsenseQA below, showing how it differs from CommonsenseQA. > 1. We collect Japanese QSs from ConceptNet. CommonsenseQA uses only forward relations (source concept, relation, target concept) excluding general ones such as “RelatedTo” and “IsA”. JCommonsenseQA similarly uses a set of 22 relations5, excluding general ones, but the direction of the relations is bidirectional to make the questions more diverse. In other words, we also use relations in the opposite direction (source concept, relation−1, target concept).6 With this setup, we extracted 43,566 QSs with Japanese source/target concepts and randomly selected 7,500 from them. > 2. Some low-quality questions in CommonsenseQA contain distractors that can be considered to be an answer. To improve the quality of distractors, we add the following two processes that are not adopted in CommonsenseQA. First, if three target concepts of a QS include a spelling variation or a synonym of one another, this QS is removed. To identify spelling variations, we use the word ID of the morphological dictionary Juman Dic7. Second, we crowdsource a task of judging whether target concepts contain a synonym. As a result, we adopted 5,920 QSs from 7,500. > 3. For each QS, we crowdsource a task of writing a question sentence in which only one from the three target concepts is an answer. In the example shown in Figure 4, “駅 (station)” is an answer, and the others are distractors. To remove low quality question sentences, we remove the following question sentences. > - Question sentences that contain a choice word(this is because such a question is easily solved). > - Question sentences that contain the expression “XX characters”.8 (XX is a number). > - Improperly formatted question sentences that do not end with “?”. > - As a result, 5,920 × 3 = 17,760question sentences were created, from which we adopted 15,310 by removing inappropriate question sentences. > 4. In CommonsenseQA, when adding distractors, one is selected from ConceptNet, and the other is created by crowdsourcing. In JCommonsenseQA, to have a wider variety of distractors, two distractors are created by crowdsourcing instead of selecting from ConceptNet. To improve the quality of the questions9, we remove questions whose added distractors fall into one of the following categories: > - Distractors are included in a question sentence. > - Distractors overlap with one of existing choices. > - As a result, distractors were added to the 15,310 questions, of which we adopted 13,906. > 5. We asked three crowdworkers to answer each question and adopt only those answered correctly by at least two workers. As a result, we adopted 11,263 out of the 13,906 questions. #### Who are the annotators? From [JGLUE's README.md](https://github.com/yahoojapan/JGLUE/blob/main/README.md#tasksdatasets): > We use Yahoo! Crowdsourcing for all crowdsourcing tasks in constructing the datasets. From [JCoLA's paper](https://arxiv.org/abs/2309.12676): > As a reference for the upper limit of accuracy in JCoLA, human acceptability judgment experiments were conducted on Lancers2 with a subset of the JCoLA data. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset From [JGLUE's paper](https://aclanthology.org/2022.lrec-1.317/): > We build a Japanese NLU benchmark, JGLUE, from scratch without translation to measure the general NLU ability in Japanese. We hope that JGLUE will facilitate NLU research in Japanese. ### Discussion of Biases [More Information Needed] ### Other Known Limitations From [JCoLA's paper](https://arxiv.org/abs/2309.12676): > All the sentences included in JCoLA have been extracted from textbooks, handbooks and journal articles on theoretical syntax. Therefore, those sentences are guaranteed to be theoretically meaningful, making JCoLA a challenging dataset. However, the distribution of linguistic phenomena directly reflects that of the source literature and thus turns out to be extremely skewed. Indeed, as can be seen in Table 3, while the number of sentences exceeds 100 for most linguistic phenomena, there are several linguistic phenomena for which there are only about 10 sentences. In addition, since it is difficult to force language models to interpret sentences given specific contexts, those sentences whose unacceptability depends on contexts were inevitably removed from JCoLA. This removal process resulted in the deletion of unacceptable sentences from some linguistic phenomena (such as ellipsis), consequently skewing the balance between acceptable and unacceptable sentences (with a higher proportion of acceptable sentences). ## Additional Information - 日本語言語理解ベンチマーク JGLUE の構築 〜 自然言語処理モデルの評価用データセットを公開しました - Yahoo! JAPAN Tech Blog https://techblog.yahoo.co.jp/entry/2022122030379907/ ### Dataset Curators #### MARC-ja - Keung, Phillip, et al. "The Multilingual Amazon Reviews Corpus." Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020. #### JCoLA - Someya, Sugimoto, and Oseki. "JCoLA: Japanese Corpus of Linguistic Acceptability." arxiv preprint arXiv:2309.12676 (2023). #### JSTS and JNLI - Miyazaki, Takashi, and Nobuyuki Shimizu. "Cross-lingual image caption generation." Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016. #### JSQuAD The JGLUE's 'authors curated the original data for JSQuAD from the Japanese wikipedia dump. #### JCommonsenseQA In the same way as CommonsenseQA, JCommonsenseQA is built using crowdsourcing with seeds extracted from the knowledge base ConceptNet ### Licensing Information #### JGLUE From [JGLUE's README.md'](https://github.com/yahoojapan/JGLUE#license): > This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. #### JCoLA From [JCoLA's README.md'](https://github.com/osekilab/JCoLA#license): > The text in this corpus is excerpted from the published works, and copyright (where applicable) remains with the original authors or publishers. We expect that research use within Japan is legal under fair use, but make no guarantee of this. ### Citation Information #### JGLUE ```bibtex @inproceedings{kurihara-lrec-2022-jglue, title={JGLUE: Japanese general language understanding evaluation}, author={Kurihara, Kentaro and Kawahara, Daisuke and Shibata, Tomohide}, booktitle={Proceedings of the Thirteenth Language Resources and Evaluation Conference}, pages={2957--2966}, year={2022}, url={https://aclanthology.org/2022.lrec-1.317/} } ``` ```bibtex @inproceedings{kurihara-nlp-2022-jglue, title={JGLUE: 日本語言語理解ベンチマーク}, author={栗原健太郎 and 河原大輔 and 柴田知秀}, booktitle={言語処理学会第 28 回年次大会}, pages={2023--2028}, year={2022}, url={https://www.anlp.jp/proceedings/annual_meeting/2022/pdf_dir/E8-4.pdf}, note={in Japanese} } ``` #### MARC-ja ```bibtex @inproceedings{marc_reviews, title={The Multilingual Amazon Reviews Corpus}, author={Keung, Phillip and Lu, Yichao and Szarvas, György and Smith, Noah A.}, booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing}, year={2020} } ``` #### JCoLA ```bibtex @article{someya-arxiv-2023-jcola, title={JCoLA: Japanese Corpus of Linguistic Acceptability}, author={Taiga Someya and Yushi Sugimoto and Yohei Oseki}, year={2023}, eprint={2309.12676}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ```bibtex @inproceedings{someya-nlp-2022-jcola, title={日本語版 CoLA の構築}, author={染谷 大河 and 大関 洋平}, booktitle={言語処理学会第 28 回年次大会}, pages={1872--1877}, year={2022}, url={https://www.anlp.jp/proceedings/annual_meeting/2022/pdf_dir/E7-1.pdf}, note={in Japanese} } ``` #### JSTS and JNLI ```bibtex @inproceedings{miyazaki2016cross, title={Cross-lingual image caption generation}, author={Miyazaki, Takashi and Shimizu, Nobuyuki}, booktitle={Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, pages={1780--1790}, year={2016} } ``` ### Contributions Thanks to [Kentaro Kurihara](https://twitter.com/kkurihara_cs), [Daisuke Kawahara](https://twitter.com/daisukekawahar1), and [Tomohide Shibata](https://twitter.com/stomohide) for creating JGLUE dataset. Thanks to [Taiga Someya](https://twitter.com/T0a8i0g9a) for creating JCoLA dataset.

dair-ai/emotion

dair-ai

--- annotations_creators: - machine-generated language_creators: - machine-generated language: - en license: - other multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification paperswithcode_id: emotion pretty_name: Emotion tags: - emotion-classification dataset_info: - config_name: split features: - name: text dtype: string - name: label dtype: class_label: names: '0': sadness '1': joy '2': love '3': anger '4': fear '5': surprise splits: - name: train num_bytes: 1741597 num_examples: 16000 - name: validation num_bytes: 214703 num_examples: 2000 - name: test num_bytes: 217181 num_examples: 2000 download_size: 740883 dataset_size: 2173481 - config_name: unsplit features: - name: text dtype: string - name: label dtype: class_label: names: '0': sadness '1': joy '2': love '3': anger '4': fear '5': surprise splits: - name: train num_bytes: 45445685 num_examples: 416809 download_size: 15388281 dataset_size: 45445685 train-eval-index: - config: default task: text-classification task_id: multi_class_classification splits: train_split: train eval_split: test col_mapping: text: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- # Dataset Card for "emotion" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://github.com/dair-ai/emotion_dataset](https://github.com/dair-ai/emotion_dataset) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 16.13 MB - **Size of the generated dataset:** 47.62 MB - **Total amount of disk used:** 63.75 MB ### Dataset Summary Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances An example looks as follows. ``` { "text": "im feeling quite sad and sorry for myself but ill snap out of it soon", "label": 0 } ``` ### Data Fields The data fields are: - `text`: a `string` feature. - `label`: a classification label, with possible values including `sadness` (0), `joy` (1), `love` (2), `anger` (3), `fear` (4), `surprise` (5). ### Data Splits The dataset has 2 configurations: - split: with a total of 20_000 examples split into train, validation and split - unsplit: with a total of 416_809 examples in a single train split | name | train | validation | test | |---------|-------:|-----------:|-----:| | split | 16000 | 2000 | 2000 | | unsplit | 416809 | n/a | n/a | ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information The dataset should be used for educational and research purposes only. ### Citation Information If you use this dataset, please cite: ``` @inproceedings{saravia-etal-2018-carer, title = "{CARER}: Contextualized Affect Representations for Emotion Recognition", author = "Saravia, Elvis and Liu, Hsien-Chi Toby and Huang, Yen-Hao and Wu, Junlin and Chen, Yi-Shin", booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing", month = oct # "-" # nov, year = "2018", address = "Brussels, Belgium", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D18-1404", doi = "10.18653/v1/D18-1404", pages = "3687--3697", abstract = "Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.", } ``` ### Contributions Thanks to [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun) for adding this dataset.

EleutherAI/lambada_openai

EleutherAI

--- pretty_name: LAMBADA OpenAI language_creators: - machine-generated license: mit multilinguality: - translation task_ids: - language-modeling source_datasets: - lambada size_categories: - 1K<n<10K language: - de - en - es - fr - it dataset_info: - config_name: default features: - name: text dtype: string splits: - name: test num_bytes: 1709449 num_examples: 5153 download_size: 1819752 dataset_size: 1709449 - config_name: de features: - name: text dtype: string splits: - name: test num_bytes: 1904576 num_examples: 5153 download_size: 1985231 dataset_size: 1904576 - config_name: en features: - name: text dtype: string splits: - name: test num_bytes: 1709449 num_examples: 5153 download_size: 1819752 dataset_size: 1709449 - config_name: es features: - name: text dtype: string splits: - name: test num_bytes: 1821735 num_examples: 5153 download_size: 1902349 dataset_size: 1821735 - config_name: fr features: - name: text dtype: string splits: - name: test num_bytes: 1948795 num_examples: 5153 download_size: 2028703 dataset_size: 1948795 - config_name: it features: - name: text dtype: string splits: - name: test num_bytes: 1813420 num_examples: 5153 download_size: 1894613 dataset_size: 1813420 --- ## Dataset Description - **Repository:** [openai/gpt2](https://github.com/openai/gpt-2) - **Paper:** Radford et al. [Language Models are Unsupervised Multitask Learners](https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf) ### Dataset Summary This dataset is comprised of the LAMBADA test split as pre-processed by OpenAI (see relevant discussions [here](https://github.com/openai/gpt-2/issues/131#issuecomment-497136199) and [here](https://github.com/huggingface/transformers/issues/491)). It also contains machine translated versions of the split in German, Spanish, French, and Italian. LAMBADA is used to evaluate the capabilities of computational models for text understanding by means of a word prediction task. LAMBADA is a collection of narrative texts sharing the characteristic that human subjects are able to guess their last word if they are exposed to the whole text, but not if they only see the last sentence preceding the target word. To succeed on LAMBADA, computational models cannot simply rely on local context, but must be able to keep track of information in the broader discourse. ### Languages English, German, Spanish, French, and Italian. ### Source Data For non-English languages, the data splits were produced by Google Translate. See the [`translation_script.py`](translation_script.py) for more details. ## Additional Information ### Hash Checksums For data integrity checks we leave the following checksums for the files in this dataset: | File Name | Checksum (SHA-256) | |--------------------------------------------------------------------------|------------------------------------------------------------------| | lambada_test_de.jsonl | 51c6c1795894c46e88e4c104b5667f488efe79081fb34d746b82b8caa663865e | | [openai/lambada_test.jsonl](https://openaipublic.blob.core.windows.net/gpt-2/data/lambada_test.jsonl) | 4aa8d02cd17c719165fc8a7887fddd641f43fcafa4b1c806ca8abc31fabdb226 | | lambada_test_en.jsonl | 4aa8d02cd17c719165fc8a7887fddd641f43fcafa4b1c806ca8abc31fabdb226 | | lambada_test_es.jsonl | ffd760026c647fb43c67ce1bc56fd527937304b348712dce33190ea6caba6f9c | | lambada_test_fr.jsonl | 941ec6a73dba7dc91c860bf493eb66a527cd430148827a4753a4535a046bf362 | | lambada_test_it.jsonl | 86654237716702ab74f42855ae5a78455c1b0e50054a4593fb9c6fcf7fad0850 | ### Licensing License: [Modified MIT](https://github.com/openai/gpt-2/blob/master/LICENSE) ### Citation ```bibtex @article{radford2019language, title={Language Models are Unsupervised Multitask Learners}, author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya}, year={2019} } ``` ```bibtex @misc{ author={Paperno, Denis and Kruszewski, Germán and Lazaridou, Angeliki and Pham, Quan Ngoc and Bernardi, Raffaella and Pezzelle, Sandro and Baroni, Marco and Boleda, Gemma and Fernández, Raquel}, title={The LAMBADA dataset}, DOI={10.5281/zenodo.2630551}, publisher={Zenodo}, year={2016}, month={Aug} } ``` ### Contributions Thanks to Sid Black ([@sdtblck](https://github.com/sdtblck)) for translating the `lambada_openai` dataset into the non-English languages. Thanks to Jonathan Tow ([@jon-tow](https://github.com/jon-tow)) for adding this dataset.

mlqa

--- pretty_name: MLQA (MultiLingual Question Answering) language: - en - de - es - ar - zh - vi - hi license: - cc-by-sa-3.0 source_datasets: - original size_categories: - 10K<n<100K language_creators: - crowdsourced annotations_creators: - crowdsourced multilinguality: - multilingual task_categories: - question-answering task_ids: - extractive-qa paperswithcode_id: mlqa dataset_info: - config_name: mlqa-translate-train.ar features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: train num_bytes: 101227245 num_examples: 78058 - name: validation num_bytes: 13144332 num_examples: 9512 download_size: 63364123 dataset_size: 114371577 - config_name: mlqa-translate-train.de features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: train num_bytes: 77996825 num_examples: 80069 - name: validation num_bytes: 10322113 num_examples: 9927 download_size: 63364123 dataset_size: 88318938 - config_name: mlqa-translate-train.vi features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: train num_bytes: 97387431 num_examples: 84816 - name: validation num_bytes: 12731112 num_examples: 10356 download_size: 63364123 dataset_size: 110118543 - config_name: mlqa-translate-train.zh features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: train num_bytes: 55143547 num_examples: 76285 - name: validation num_bytes: 7418070 num_examples: 9568 download_size: 63364123 dataset_size: 62561617 - config_name: mlqa-translate-train.es features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: train num_bytes: 80789653 num_examples: 81810 - name: validation num_bytes: 10718376 num_examples: 10123 download_size: 63364123 dataset_size: 91508029 - config_name: mlqa-translate-train.hi features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: train num_bytes: 168117671 num_examples: 82451 - name: validation num_bytes: 22422152 num_examples: 10253 download_size: 63364123 dataset_size: 190539823 - config_name: mlqa-translate-test.ar features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 5484467 num_examples: 5335 download_size: 10075488 dataset_size: 5484467 - config_name: mlqa-translate-test.de features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 3884332 num_examples: 4517 download_size: 10075488 dataset_size: 3884332 - config_name: mlqa-translate-test.vi features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 5998327 num_examples: 5495 download_size: 10075488 dataset_size: 5998327 - config_name: mlqa-translate-test.zh features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 4831704 num_examples: 5137 download_size: 10075488 dataset_size: 4831704 - config_name: mlqa-translate-test.es features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 3916758 num_examples: 5253 download_size: 10075488 dataset_size: 3916758 - config_name: mlqa-translate-test.hi features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 4608811 num_examples: 4918 download_size: 10075488 dataset_size: 4608811 - config_name: mlqa.ar.ar features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 8216837 num_examples: 5335 - name: validation num_bytes: 808830 num_examples: 517 download_size: 75719050 dataset_size: 9025667 - config_name: mlqa.ar.de features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 2132247 num_examples: 1649 - name: validation num_bytes: 358554 num_examples: 207 download_size: 75719050 dataset_size: 2490801 - config_name: mlqa.ar.vi features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 3235363 num_examples: 2047 - name: validation num_bytes: 283834 num_examples: 163 download_size: 75719050 dataset_size: 3519197 - config_name: mlqa.ar.zh features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 3175660 num_examples: 1912 - name: validation num_bytes: 334016 num_examples: 188 download_size: 75719050 dataset_size: 3509676 - config_name: mlqa.ar.en features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 8074057 num_examples: 5335 - name: validation num_bytes: 794775 num_examples: 517 download_size: 75719050 dataset_size: 8868832 - config_name: mlqa.ar.es features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 2981237 num_examples: 1978 - name: validation num_bytes: 223188 num_examples: 161 download_size: 75719050 dataset_size: 3204425 - config_name: mlqa.ar.hi features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 2993225 num_examples: 1831 - name: validation num_bytes: 276727 num_examples: 186 download_size: 75719050 dataset_size: 3269952 - config_name: mlqa.de.ar features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 1587005 num_examples: 1649 - name: validation num_bytes: 195822 num_examples: 207 download_size: 75719050 dataset_size: 1782827 - config_name: mlqa.de.de features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 4274496 num_examples: 4517 - name: validation num_bytes: 477366 num_examples: 512 download_size: 75719050 dataset_size: 4751862 - config_name: mlqa.de.vi features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 1654540 num_examples: 1675 - name: validation num_bytes: 211985 num_examples: 182 download_size: 75719050 dataset_size: 1866525 - config_name: mlqa.de.zh features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 1645937 num_examples: 1621 - name: validation num_bytes: 180114 num_examples: 190 download_size: 75719050 dataset_size: 1826051 - config_name: mlqa.de.en features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 4251153 num_examples: 4517 - name: validation num_bytes: 474863 num_examples: 512 download_size: 75719050 dataset_size: 4726016 - config_name: mlqa.de.es features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 1678176 num_examples: 1776 - name: validation num_bytes: 166193 num_examples: 196 download_size: 75719050 dataset_size: 1844369 - config_name: mlqa.de.hi features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 1343983 num_examples: 1430 - name: validation num_bytes: 150679 num_examples: 163 download_size: 75719050 dataset_size: 1494662 - config_name: mlqa.vi.ar features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 3164094 num_examples: 2047 - name: validation num_bytes: 226724 num_examples: 163 download_size: 75719050 dataset_size: 3390818 - config_name: mlqa.vi.de features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 2189315 num_examples: 1675 - name: validation num_bytes: 272794 num_examples: 182 download_size: 75719050 dataset_size: 2462109 - config_name: mlqa.vi.vi features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 7807045 num_examples: 5495 - name: validation num_bytes: 715291 num_examples: 511 download_size: 75719050 dataset_size: 8522336 - config_name: mlqa.vi.zh features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 2947458 num_examples: 1943 - name: validation num_bytes: 265154 num_examples: 184 download_size: 75719050 dataset_size: 3212612 - config_name: mlqa.vi.en features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 7727204 num_examples: 5495 - name: validation num_bytes: 707925 num_examples: 511 download_size: 75719050 dataset_size: 8435129 - config_name: mlqa.vi.es features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 2822481 num_examples: 2018 - name: validation num_bytes: 279235 num_examples: 189 download_size: 75719050 dataset_size: 3101716 - config_name: mlqa.vi.hi features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 2738045 num_examples: 1947 - name: validation num_bytes: 251470 num_examples: 177 download_size: 75719050 dataset_size: 2989515 - config_name: mlqa.zh.ar features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 1697005 num_examples: 1912 - name: validation num_bytes: 171743 num_examples: 188 download_size: 75719050 dataset_size: 1868748 - config_name: mlqa.zh.de features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 1356268 num_examples: 1621 - name: validation num_bytes: 170686 num_examples: 190 download_size: 75719050 dataset_size: 1526954 - config_name: mlqa.zh.vi features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 1770535 num_examples: 1943 - name: validation num_bytes: 169651 num_examples: 184 download_size: 75719050 dataset_size: 1940186 - config_name: mlqa.zh.zh features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 4324740 num_examples: 5137 - name: validation num_bytes: 433960 num_examples: 504 download_size: 75719050 dataset_size: 4758700 - config_name: mlqa.zh.en features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 4353361 num_examples: 5137 - name: validation num_bytes: 437016 num_examples: 504 download_size: 75719050 dataset_size: 4790377 - config_name: mlqa.zh.es features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 1697983 num_examples: 1947 - name: validation num_bytes: 134693 num_examples: 161 download_size: 75719050 dataset_size: 1832676 - config_name: mlqa.zh.hi features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 1547159 num_examples: 1767 - name: validation num_bytes: 180928 num_examples: 189 download_size: 75719050 dataset_size: 1728087 - config_name: mlqa.en.ar features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 6641971 num_examples: 5335 - name: validation num_bytes: 621075 num_examples: 517 download_size: 75719050 dataset_size: 7263046 - config_name: mlqa.en.de features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 4966262 num_examples: 4517 - name: validation num_bytes: 584725 num_examples: 512 download_size: 75719050 dataset_size: 5550987 - config_name: mlqa.en.vi features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 6958087 num_examples: 5495 - name: validation num_bytes: 631268 num_examples: 511 download_size: 75719050 dataset_size: 7589355 - config_name: mlqa.en.zh features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 6441614 num_examples: 5137 - name: validation num_bytes: 598772 num_examples: 504 download_size: 75719050 dataset_size: 7040386 - config_name: mlqa.en.en features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 13787522 num_examples: 11590 - name: validation num_bytes: 1307399 num_examples: 1148 download_size: 75719050 dataset_size: 15094921 - config_name: mlqa.en.es features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 6074990 num_examples: 5253 - name: validation num_bytes: 545657 num_examples: 500 download_size: 75719050 dataset_size: 6620647 - config_name: mlqa.en.hi features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 6293785 num_examples: 4918 - name: validation num_bytes: 614223 num_examples: 507 download_size: 75719050 dataset_size: 6908008 - config_name: mlqa.es.ar features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 1696778 num_examples: 1978 - name: validation num_bytes: 145105 num_examples: 161 download_size: 75719050 dataset_size: 1841883 - config_name: mlqa.es.de features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 1361983 num_examples: 1776 - name: validation num_bytes: 139968 num_examples: 196 download_size: 75719050 dataset_size: 1501951 - config_name: mlqa.es.vi features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 1707141 num_examples: 2018 - name: validation num_bytes: 172801 num_examples: 189 download_size: 75719050 dataset_size: 1879942 - config_name: mlqa.es.zh features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 1635294 num_examples: 1947 - name: validation num_bytes: 122829 num_examples: 161 download_size: 75719050 dataset_size: 1758123 - config_name: mlqa.es.en features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 4249431 num_examples: 5253 - name: validation num_bytes: 408169 num_examples: 500 download_size: 75719050 dataset_size: 4657600 - config_name: mlqa.es.es features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 4281273 num_examples: 5253 - name: validation num_bytes: 411196 num_examples: 500 download_size: 75719050 dataset_size: 4692469 - config_name: mlqa.es.hi features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 1489611 num_examples: 1723 - name: validation num_bytes: 178003 num_examples: 187 download_size: 75719050 dataset_size: 1667614 - config_name: mlqa.hi.ar features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 4374373 num_examples: 1831 - name: validation num_bytes: 402817 num_examples: 186 download_size: 75719050 dataset_size: 4777190 - config_name: mlqa.hi.de features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 2961556 num_examples: 1430 - name: validation num_bytes: 294325 num_examples: 163 download_size: 75719050 dataset_size: 3255881 - config_name: mlqa.hi.vi features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 4664436 num_examples: 1947 - name: validation num_bytes: 411654 num_examples: 177 download_size: 75719050 dataset_size: 5076090 - config_name: mlqa.hi.zh features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 4281309 num_examples: 1767 - name: validation num_bytes: 416192 num_examples: 189 download_size: 75719050 dataset_size: 4697501 - config_name: mlqa.hi.en features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 11245629 num_examples: 4918 - name: validation num_bytes: 1076115 num_examples: 507 download_size: 75719050 dataset_size: 12321744 - config_name: mlqa.hi.es features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 3789337 num_examples: 1723 - name: validation num_bytes: 412469 num_examples: 187 download_size: 75719050 dataset_size: 4201806 - config_name: mlqa.hi.hi features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 11606982 num_examples: 4918 - name: validation num_bytes: 1115055 num_examples: 507 download_size: 75719050 dataset_size: 12722037 --- # Dataset Card for "mlqa" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://github.com/facebookresearch/MLQA](https://github.com/facebookresearch/MLQA) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 4.15 GB - **Size of the generated dataset:** 910.01 MB - **Total amount of disk used:** 5.06 GB ### Dataset Summary MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance. MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic, German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between 4 different languages on average. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages MLQA contains QA instances in 7 languages, English, Arabic, German, Spanish, Hindi, Vietnamese and Simplified Chinese. ## Dataset Structure ### Data Instances #### mlqa-translate-test.ar - **Size of downloaded dataset files:** 10.08 MB - **Size of the generated dataset:** 5.48 MB - **Total amount of disk used:** 15.56 MB An example of 'test' looks as follows. ``` ``` #### mlqa-translate-test.de - **Size of downloaded dataset files:** 10.08 MB - **Size of the generated dataset:** 3.88 MB - **Total amount of disk used:** 13.96 MB An example of 'test' looks as follows. ``` ``` #### mlqa-translate-test.es - **Size of downloaded dataset files:** 10.08 MB - **Size of the generated dataset:** 3.92 MB - **Total amount of disk used:** 13.99 MB An example of 'test' looks as follows. ``` ``` #### mlqa-translate-test.hi - **Size of downloaded dataset files:** 10.08 MB - **Size of the generated dataset:** 4.61 MB - **Total amount of disk used:** 14.68 MB An example of 'test' looks as follows. ``` ``` #### mlqa-translate-test.vi - **Size of downloaded dataset files:** 10.08 MB - **Size of the generated dataset:** 6.00 MB - **Total amount of disk used:** 16.07 MB An example of 'test' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### mlqa-translate-test.ar - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `answer_start`: a `int32` feature. - `text`: a `string` feature. - `id`: a `string` feature. #### mlqa-translate-test.de - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `answer_start`: a `int32` feature. - `text`: a `string` feature. - `id`: a `string` feature. #### mlqa-translate-test.es - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `answer_start`: a `int32` feature. - `text`: a `string` feature. - `id`: a `string` feature. #### mlqa-translate-test.hi - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `answer_start`: a `int32` feature. - `text`: a `string` feature. - `id`: a `string` feature. #### mlqa-translate-test.vi - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `answer_start`: a `int32` feature. - `text`: a `string` feature. - `id`: a `string` feature. ### Data Splits | name |test| |----------------------|---:| |mlqa-translate-test.ar|5335| |mlqa-translate-test.de|4517| |mlqa-translate-test.es|5253| |mlqa-translate-test.hi|4918| |mlqa-translate-test.vi|5495| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @article{lewis2019mlqa, title = {MLQA: Evaluating Cross-lingual Extractive Question Answering}, author = {Lewis, Patrick and Oguz, Barlas and Rinott, Ruty and Riedel, Sebastian and Schwenk, Holger}, journal = {arXiv preprint arXiv:1910.07475}, year = 2019, eid = {arXiv: 1910.07475} } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@M-Salti](https://github.com/M-Salti), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf), [@mariamabarham](https://github.com/mariamabarham), [@lhoestq](https://github.com/lhoestq) for adding this dataset.

mlabonne/guanaco-llama2-1k

mlabonne

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 1654448 num_examples: 1000 download_size: 966693 dataset_size: 1654448 configs: - config_name: default data_files: - split: train path: data/train-* --- # Guanaco-1k: Lazy Llama 2 Formatting This is a subset (1000 samples) of the excellent [`timdettmers/openassistant-guanaco`](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) dataset, processed to match Llama 2's prompt format as described [in this article](https://huggingface.co/blog/llama2#how-to-prompt-llama-2). It was created using the following [colab notebook](https://colab.research.google.com/drive/1Ad7a9zMmkxuXTOh1Z7-rNSICA4dybpM2?usp=sharing). Useful if you don't want to reformat it by yourself (e.g., using a script). It was designed for [this article](https://mlabonne.github.io/blog/posts/Fine_Tune_Your_Own_Llama_2_Model_in_a_Colab_Notebook.html) about fine-tuning a Llama 2 (chat) model in a Google Colab.

Ryan-sjtu/celebahq-caption

Ryan-sjtu

--- license: mit dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 2756863400.0 num_examples: 30000 download_size: 2762815442 dataset_size: 2756863400.0 ---

opus_books

--- annotations_creators: - found language_creators: - found language: - ca - de - el - en - eo - es - fi - fr - hu - it - nl - 'no' - pl - pt - ru - sv license: - unknown multilinguality: - multilingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: null pretty_name: OpusBooks dataset_info: - config_name: ca-de features: - name: id dtype: string - name: translation dtype: translation: languages: - ca - de splits: - name: train num_bytes: 899565 num_examples: 4445 download_size: 349126 dataset_size: 899565 - config_name: ca-en features: - name: id dtype: string - name: translation dtype: translation: languages: - ca - en splits: - name: train num_bytes: 863174 num_examples: 4605 download_size: 336276 dataset_size: 863174 - config_name: de-en features: - name: id dtype: string - name: translation dtype: translation: languages: - de - en splits: - name: train num_bytes: 13739047 num_examples: 51467 download_size: 5124458 dataset_size: 13739047 - config_name: el-en features: - name: id dtype: string - name: translation dtype: translation: languages: - el - en splits: - name: train num_bytes: 552579 num_examples: 1285 download_size: 175537 dataset_size: 552579 - config_name: de-eo features: - name: id dtype: string - name: translation dtype: translation: languages: - de - eo splits: - name: train num_bytes: 398885 num_examples: 1363 download_size: 150822 dataset_size: 398885 - config_name: en-eo features: - name: id dtype: string - name: translation dtype: translation: languages: - en - eo splits: - name: train num_bytes: 386231 num_examples: 1562 download_size: 145339 dataset_size: 386231 - config_name: de-es features: - name: id dtype: string - name: translation dtype: translation: languages: - de - es splits: - name: train num_bytes: 7592487 num_examples: 27526 download_size: 2802010 dataset_size: 7592487 - config_name: el-es features: - name: id dtype: string - name: translation dtype: translation: languages: - el - es splits: - name: train num_bytes: 527991 num_examples: 1096 download_size: 168306 dataset_size: 527991 - config_name: en-es features: - name: id dtype: string - name: translation dtype: translation: languages: - en - es splits: - name: train num_bytes: 25291783 num_examples: 93470 download_size: 9257150 dataset_size: 25291783 - config_name: eo-es features: - name: id dtype: string - name: translation dtype: translation: languages: - eo - es splits: - name: train num_bytes: 409591 num_examples: 1677 download_size: 154950 dataset_size: 409591 - config_name: en-fi features: - name: id dtype: string - name: translation dtype: translation: languages: - en - fi splits: - name: train num_bytes: 715039 num_examples: 3645 download_size: 266714 dataset_size: 715039 - config_name: es-fi features: - name: id dtype: string - name: translation dtype: translation: languages: - es - fi splits: - name: train num_bytes: 710462 num_examples: 3344 download_size: 264316 dataset_size: 710462 - config_name: de-fr features: - name: id dtype: string - name: translation dtype: translation: languages: - de - fr splits: - name: train num_bytes: 9544399 num_examples: 34916 download_size: 3556168 dataset_size: 9544399 - config_name: el-fr features: - name: id dtype: string - name: translation dtype: translation: languages: - el - fr splits: - name: train num_bytes: 539933 num_examples: 1237 download_size: 169241 dataset_size: 539933 - config_name: en-fr features: - name: id dtype: string - name: translation dtype: translation: languages: - en - fr splits: - name: train num_bytes: 32997199 num_examples: 127085 download_size: 12009501 dataset_size: 32997199 - config_name: eo-fr features: - name: id dtype: string - name: translation dtype: translation: languages: - eo - fr splits: - name: train num_bytes: 412999 num_examples: 1588 download_size: 152040 dataset_size: 412999 - config_name: es-fr features: - name: id dtype: string - name: translation dtype: translation: languages: - es - fr splits: - name: train num_bytes: 14382198 num_examples: 56319 download_size: 5203099 dataset_size: 14382198 - config_name: fi-fr features: - name: id dtype: string - name: translation dtype: translation: languages: - fi - fr splits: - name: train num_bytes: 746097 num_examples: 3537 download_size: 276633 dataset_size: 746097 - config_name: ca-hu features: - name: id dtype: string - name: translation dtype: translation: languages: - ca - hu splits: - name: train num_bytes: 886162 num_examples: 4463 download_size: 346425 dataset_size: 886162 - config_name: de-hu features: - name: id dtype: string - name: translation dtype: translation: languages: - de - hu splits: - name: train num_bytes: 13515043 num_examples: 51780 download_size: 5069455 dataset_size: 13515043 - config_name: el-hu features: - name: id dtype: string - name: translation dtype: translation: languages: - el - hu splits: - name: train num_bytes: 546290 num_examples: 1090 download_size: 176715 dataset_size: 546290 - config_name: en-hu features: - name: id dtype: string - name: translation dtype: translation: languages: - en - hu splits: - name: train num_bytes: 35256934 num_examples: 137151 download_size: 13232578 dataset_size: 35256934 - config_name: eo-hu features: - name: id dtype: string - name: translation dtype: translation: languages: - eo - hu splits: - name: train num_bytes: 389112 num_examples: 1636 download_size: 151332 dataset_size: 389112 - config_name: fr-hu features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - hu splits: - name: train num_bytes: 22483133 num_examples: 89337 download_size: 8328639 dataset_size: 22483133 - config_name: de-it features: - name: id dtype: string - name: translation dtype: translation: languages: - de - it splits: - name: train num_bytes: 7760020 num_examples: 27381 download_size: 2811066 dataset_size: 7760020 - config_name: en-it features: - name: id dtype: string - name: translation dtype: translation: languages: - en - it splits: - name: train num_bytes: 8993803 num_examples: 32332 download_size: 3295251 dataset_size: 8993803 - config_name: eo-it features: - name: id dtype: string - name: translation dtype: translation: languages: - eo - it splits: - name: train num_bytes: 387606 num_examples: 1453 download_size: 146899 dataset_size: 387606 - config_name: es-it features: - name: id dtype: string - name: translation dtype: translation: languages: - es - it splits: - name: train num_bytes: 7837703 num_examples: 28868 download_size: 2864028 dataset_size: 7837703 - config_name: fr-it features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - it splits: - name: train num_bytes: 4752171 num_examples: 14692 download_size: 1737670 dataset_size: 4752171 - config_name: hu-it features: - name: id dtype: string - name: translation dtype: translation: languages: - hu - it splits: - name: train num_bytes: 8445585 num_examples: 30949 download_size: 3101681 dataset_size: 8445585 - config_name: ca-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - ca - nl splits: - name: train num_bytes: 884823 num_examples: 4329 download_size: 340308 dataset_size: 884823 - config_name: de-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - de - nl splits: - name: train num_bytes: 3561764 num_examples: 15622 download_size: 1325189 dataset_size: 3561764 - config_name: en-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - en - nl splits: - name: train num_bytes: 10278038 num_examples: 38652 download_size: 3727995 dataset_size: 10278038 - config_name: es-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - es - nl splits: - name: train num_bytes: 9062389 num_examples: 32247 download_size: 3245558 dataset_size: 9062389 - config_name: fr-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - nl splits: - name: train num_bytes: 10408148 num_examples: 40017 download_size: 3720151 dataset_size: 10408148 - config_name: hu-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - hu - nl splits: - name: train num_bytes: 10814173 num_examples: 43428 download_size: 3998988 dataset_size: 10814173 - config_name: it-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - it - nl splits: - name: train num_bytes: 1328305 num_examples: 2359 download_size: 476875 dataset_size: 1328305 - config_name: en-no features: - name: id dtype: string - name: translation dtype: translation: languages: - en - 'no' splits: - name: train num_bytes: 661978 num_examples: 3499 download_size: 246977 dataset_size: 661978 - config_name: es-no features: - name: id dtype: string - name: translation dtype: translation: languages: - es - 'no' splits: - name: train num_bytes: 729125 num_examples: 3585 download_size: 270796 dataset_size: 729125 - config_name: fi-no features: - name: id dtype: string - name: translation dtype: translation: languages: - fi - 'no' splits: - name: train num_bytes: 691181 num_examples: 3414 download_size: 256267 dataset_size: 691181 - config_name: fr-no features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - 'no' splits: - name: train num_bytes: 692786 num_examples: 3449 download_size: 256501 dataset_size: 692786 - config_name: hu-no features: - name: id dtype: string - name: translation dtype: translation: languages: - hu - 'no' splits: - name: train num_bytes: 695497 num_examples: 3410 download_size: 267047 dataset_size: 695497 - config_name: en-pl features: - name: id dtype: string - name: translation dtype: translation: languages: - en - pl splits: - name: train num_bytes: 583091 num_examples: 2831 download_size: 226855 dataset_size: 583091 - config_name: fi-pl features: - name: id dtype: string - name: translation dtype: translation: languages: - fi - pl splits: - name: train num_bytes: 613791 num_examples: 2814 download_size: 236123 dataset_size: 613791 - config_name: fr-pl features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - pl splits: - name: train num_bytes: 614248 num_examples: 2825 download_size: 235905 dataset_size: 614248 - config_name: hu-pl features: - name: id dtype: string - name: translation dtype: translation: languages: - hu - pl splits: - name: train num_bytes: 616161 num_examples: 2859 download_size: 245670 dataset_size: 616161 - config_name: de-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - de - pt splits: - name: train num_bytes: 317155 num_examples: 1102 download_size: 116319 dataset_size: 317155 - config_name: en-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - en - pt splits: - name: train num_bytes: 309689 num_examples: 1404 download_size: 111837 dataset_size: 309689 - config_name: eo-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - eo - pt splits: - name: train num_bytes: 311079 num_examples: 1259 download_size: 116157 dataset_size: 311079 - config_name: es-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - es - pt splits: - name: train num_bytes: 326884 num_examples: 1327 download_size: 120549 dataset_size: 326884 - config_name: fr-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - pt splits: - name: train num_bytes: 324616 num_examples: 1263 download_size: 115920 dataset_size: 324616 - config_name: hu-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - hu - pt splits: - name: train num_bytes: 302972 num_examples: 1184 download_size: 115002 dataset_size: 302972 - config_name: it-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - it - pt splits: - name: train num_bytes: 301428 num_examples: 1163 download_size: 111050 dataset_size: 301428 - config_name: de-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - de - ru splits: - name: train num_bytes: 5764673 num_examples: 17373 download_size: 1799371 dataset_size: 5764673 - config_name: en-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - en - ru splits: - name: train num_bytes: 5190880 num_examples: 17496 download_size: 1613419 dataset_size: 5190880 - config_name: es-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - es - ru splits: - name: train num_bytes: 5281130 num_examples: 16793 download_size: 1648606 dataset_size: 5281130 - config_name: fr-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - ru splits: - name: train num_bytes: 2474210 num_examples: 8197 download_size: 790541 dataset_size: 2474210 - config_name: hu-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - hu - ru splits: - name: train num_bytes: 7818688 num_examples: 26127 download_size: 2469765 dataset_size: 7818688 - config_name: it-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - it - ru splits: - name: train num_bytes: 5316952 num_examples: 17906 download_size: 1620478 dataset_size: 5316952 - config_name: en-sv features: - name: id dtype: string - name: translation dtype: translation: languages: - en - sv splits: - name: train num_bytes: 790785 num_examples: 3095 download_size: 304975 dataset_size: 790785 - config_name: fr-sv features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - sv splits: - name: train num_bytes: 833553 num_examples: 3002 download_size: 321660 dataset_size: 833553 - config_name: it-sv features: - name: id dtype: string - name: translation dtype: translation: languages: - it - sv splits: - name: train num_bytes: 811413 num_examples: 2998 download_size: 307821 dataset_size: 811413 --- # Dataset Card for OpusBooks ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://opus.nlpl.eu/Books.php - **Repository:** None - **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf - **Leaderboard:** [More Information Needed] - **Point of Contact:** [More Information Needed] ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances Here are some examples of questions and facts: ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.

tau/sled

tau

--- language: - en license: - mit task_categories: - question-answering - summarization - text-generation task_ids: - multiple-choice-qa - natural-language-inference configs: - gov_report - summ_screen_fd - qmsum - qasper - narrative_qa - quality - contract_nli - squad - squad_shuffled_distractors - squad_ordered_distractors - hotpotqa - hotpotqa_second_only tags: - multi-hop-question-answering - query-based-summarization - long-texts --- ## Dataset Description - **Repository:** [SLED Github repository](https://github.com/Mivg/SLED) - **Paper:** [Efficient Long-Text Understanding with Short-Text Models ](https://arxiv.org/pdf/2208.00748.pdf) # Dataset Card for SCROLLS ## Overview This dataset is based on the [SCROLLS](https://huggingface.co/datasets/tau/scrolls) dataset ([paper](https://arxiv.org/pdf/2201.03533.pdf)), the [SQuAD 1.1](https://huggingface.co/datasets/squad) dataset and the [HotpotQA](https://huggingface.co/datasets/hotpot_qa) dataset. It doesn't contain any unpblished data, but includes the configuration needed for the [Efficient Long-Text Understanding with Short-Text Models ](https://arxiv.org/pdf/2208.00748.pdf) paper. ## Tasks The tasks included are: #### GovReport ([Huang et al., 2021](https://arxiv.org/pdf/2104.02112.pdf)) GovReport is a summarization dataset of reports addressing various national policy issues published by the Congressional Research Service and the U.S. Government Accountability Office, where each document is paired with a hand-written executive summary. The reports and their summaries are longer than their equivalents in other popular long-document summarization datasets; for example, GovReport's documents are approximately 1.5 and 2.5 times longer than the documents in Arxiv and PubMed, respectively. #### SummScreenFD ([Chen et al., 2021](https://arxiv.org/pdf/2104.07091.pdf)) SummScreenFD is a summarization dataset in the domain of TV shows (e.g. Friends, Game of Thrones). Given a transcript of a specific episode, the goal is to produce the episode's recap. The original dataset is divided into two complementary subsets, based on the source of its community contributed transcripts. For SCROLLS, we use the ForeverDreaming (FD) subset, as it incorporates 88 different shows, making it a more diverse alternative to the TV MegaSite (TMS) subset, which has only 10 shows. Community-authored recaps for the ForeverDreaming transcripts were collected from English Wikipedia and TVMaze. #### QMSum ([Zhong et al., 2021](https://arxiv.org/pdf/2104.05938.pdf)) QMSum is a query-based summarization dataset, consisting of 232 meetings transcripts from multiple domains. The corpus covers academic group meetings at the International Computer Science Institute and their summaries, industrial product meetings for designing a remote control, and committee meetings of the Welsh and Canadian Parliaments, dealing with a variety of public policy issues. Annotators were tasked with writing queries about the broad contents of the meetings, as well as specific questions about certain topics or decisions, while ensuring that the relevant text for answering each query spans at least 200 words or 10 turns. #### NarrativeQA ([Kočiský et al., 2021](https://arxiv.org/pdf/1712.07040.pdf)) NarrativeQA (Kočiský et al., 2021) is an established question answering dataset over entire books from Project Gutenberg and movie scripts from different websites. Annotators were given summaries of the books and scripts obtained from Wikipedia, and asked to generate question-answer pairs, resulting in about 30 questions and answers for each of the 1,567 books and scripts. They were encouraged to use their own words rather then copying, and avoid asking yes/no questions or ones about the cast. Each question was then answered by an additional annotator, providing each question with two reference answers (unless both answers are identical). #### Qasper ([Dasigi et al., 2021](https://arxiv.org/pdf/2105.03011.pdf)) Qasper is a question answering dataset over NLP papers filtered from the Semantic Scholar Open Research Corpus (S2ORC). Questions were written by NLP practitioners after reading only the title and abstract of the papers, while another set of NLP practitioners annotated the answers given the entire document. Qasper contains abstractive, extractive, and yes/no questions, as well as unanswerable ones. #### QuALITY ([Pang et al., 2021](https://arxiv.org/pdf/2112.08608.pdf)) QuALITY is a multiple-choice question answering dataset over articles and stories sourced from Project Gutenberg, the Open American National Corpus, and more. Experienced writers wrote questions and distractors, and were incentivized to write answerable, unambiguous questions such that in order to correctly answer them, human annotators must read large portions of the given document. Reference answers were then calculated using the majority vote between of the annotators and writer's answers. To measure the difficulty of their questions, Pang et al. conducted a speed validation process, where another set of annotators were asked to answer questions given only a short period of time to skim through the document. As a result, 50% of the questions in QuALITY are labeled as hard, i.e. the majority of the annotators in the speed validation setting chose the wrong answer. #### ContractNLI ([Koreeda and Manning, 2021](https://arxiv.org/pdf/2110.01799.pdf)) Contract NLI is a natural language inference dataset in the legal domain. Given a non-disclosure agreement (the premise), the task is to predict whether a particular legal statement (the hypothesis) is entailed, not entailed (neutral), or cannot be entailed (contradiction) from the contract. The NDAs were manually picked after simple filtering from the Electronic Data Gathering, Analysis, and Retrieval system (EDGAR) and Google. The dataset contains a total of 607 contracts and 17 unique hypotheses, which were combined to produce the dataset's 10,319 examples. #### SQuAD 1.1 ([Rajpurkar et al., 2016](https://arxiv.org/pdf/1606.05250.pdf)) Stanford Question Answering Dataset (SQuAD) is a reading comprehension \ dataset, consisting of questions posed by crowdworkers on a set of Wikipedia \ articles, where the answer to every question is a segment of text, or span, \ from the corresponding reading passage, or the question might be unanswerable. #### HotpotQA ([Yang et al., 2018](https://arxiv.org/pdf/1809.09600.pdf)) HotpotQA is a new dataset with 113k Wikipedia-based question-answer pairs with four key features: (1) the questions require finding and reasoning over multiple supporting documents to answer; (2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas; (3) we provide sentence-level supporting facts required for reasoning, allowingQA systems to reason with strong supervisionand explain the predictions; (4) we offer a new type of factoid comparison questions to testQA systems’ ability to extract relevant facts and perform necessary comparison. ## Data Fields All the datasets in the benchmark are in the same input-output format - `input`: a `string` feature. The input document. - `input_prefix`: an optional `string` feature, for the datasets containing prefix (e.g. question) - `output`: a `string` feature. The target. - `id`: a `string` feature. Unique per input. - `pid`: a `string` feature. Unique per input-output pair (can differ from 'id' in NarrativeQA and Qasper, where there is more then one valid target). The dataset that contain `input_prefix` are: - SQuAD - the question - HotpotQA - the question - qmsum - the query - qasper - the question - narrative_qa - the question - quality - the question + the four choices - contract_nli - the hypothesis ## Controlled experiments To test multiple properties of SLED, we modify SQuAD 1.1 [Rajpurkar et al., 2016](https://arxiv.org/pdf/1606.05250.pdf) and HotpotQA [Yang et al., 2018](https://arxiv.org/pdf/1809.09600.pdf) to create a few controlled experiments settings. Those are accessible via the following configurations: - squad - Contains the original version of SQuAD 1.1 (question + passage) - squad_ordered_distractors - For each example, 9 random distrctor passages are concatenated (separated by '\n') - squad_shuffled_distractors - For each example, 9 random distrctor passages are added (separated by '\n'), and jointly the 10 passages are randomly shuffled - hotpotqa - A clean version of HotpotQA, where each input contains only the two gold passages (separated by '\n') - hotpotqa_second_only - In each example, the input contains only the second gold passage ## Citation If you use this dataset, **please make sure to cite all the original dataset papers as well SCROLLS.** [[bibtex](https://drive.google.com/uc?export=download&id=1IUYIzQD9DPsECw0JWkwk4Ildn8JOMtuU)] ``` @inproceedings{Ivgi2022EfficientLU, title={Efficient Long-Text Understanding with Short-Text Models}, author={Maor Ivgi and Uri Shaham and Jonathan Berant}, year={2022} } ```

hf-internal-testing/dummy_image_text_data

hf-internal-testing

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1944983.0 num_examples: 20 download_size: 1690123 dataset_size: 1944983.0 --- # Dataset Card for "dummy_image_text_data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Anthropic/model-written-evals

Anthropic

--- annotations_creators: - machine-generated language: - en language_creators: - machine-generated license: - cc-by-4.0 multilinguality: - monolingual pretty_name: Evaluations from "Discovering Language Model Behaviors with Model-Written Evaluations" size_categories: - 100K<n<1M source_datasets: - original tags: - gender bias - social bias - AI safety - personality - politics task_categories: - multiple-choice - zero-shot-classification - question-answering task_ids: - multiple-choice-qa - multiple-choice-coreference-resolution --- # Model-Written Evaluation Datasets This repository includes datasets written by language models, used in our paper on "Discovering Language Model Behaviors with Model-Written Evaluations." We intend the datasets to be useful to: 1. Those who are interested in understanding the quality and properties of model-generated data 2. Those who wish to use our datasets to evaluate other models for the behaviors we examined in our work (e.g., related to model persona, sycophancy, advanced AI risks, and gender bias) The evaluations were generated to be asked to dialogue agents (e.g., a model finetuned explicitly respond to a user's utterances, or a pretrained language model prompted to behave like a dialogue agent). However, it is possible to adapt the data to test other kinds of models as well. We describe each of our collections of datasets below: 1. `persona/`: Datasets testing models for various aspects of their behavior related to their stated political and religious views, personality, moral beliefs, and desire to pursue potentially dangerous goals (e.g., self-preservation or power-seeking). 2. `sycophancy/`: Datasets testing models for whether or not they repeat back a user's view to various questions (in philosophy, NLP research, and politics) 3. `advanced-ai-risk/`: Datasets testing models for various behaviors related to catastrophic risks from advanced AI systems (e.g., ). These datasets were generated in a few-shot manner. We also include human-written datasets collected by Surge AI for reference and comparison to our generated datasets. 4. `winogenerated/`: Our larger, model-generated version of the Winogender Dataset ([Rudinger et al., 2018](https://arxiv.org/abs/1804.09301)). We also include the names of occupation titles that we generated, to create the dataset (alongside occupation gender statistics from the Bureau of Labor Statistics) Please see our paper for additional details on the datasets, how we generated them, human validation metrics, and other analyses of the datasets. **Disclaimer**: As discussed in our paper, some data contains content that includes social biases and stereotypes. The data may also contain other forms of harmful or offensive content. The views expressed in the data do not reflect the views of Anthropic or any of its employees. ## Contact For questions, please email `ethan at anthropic dot com` ## Bibtex Citation If you would like to cite our work or data, you may use the following bibtex citation: ``` @misc{perez2022discovering, doi = {10.48550/ARXIV.2212.09251}, url = {https://arxiv.org/abs/2212.09251}, author = {Perez, Ethan and Ringer, Sam and Lukošiūtė, Kamilė and Nguyen, Karina and Chen, Edwin and Heiner, Scott and Pettit, Craig and Olsson, Catherine and Kundu, Sandipan and Kadavath, Saurav and Jones, Andy and Chen, Anna and Mann, Ben and Israel, Brian and Seethor, Bryan and McKinnon, Cameron and Olah, Christopher and Yan, Da and Amodei, Daniela and Amodei, Dario and Drain, Dawn and Li, Dustin and Tran-Johnson, Eli and Khundadze, Guro and Kernion, Jackson and Landis, James and Kerr, Jamie and Mueller, Jared and Hyun, Jeeyoon and Landau, Joshua and Ndousse, Kamal and Goldberg, Landon and Lovitt, Liane and Lucas, Martin and Sellitto, Michael and Zhang, Miranda and Kingsland, Neerav and Elhage, Nelson and Joseph, Nicholas and Mercado, Noemí and DasSarma, Nova and Rausch, Oliver and Larson, Robin and McCandlish, Sam and Johnston, Scott and Kravec, Shauna and {El Showk}, Sheer and Lanham, Tamera and Telleen-Lawton, Timothy and Brown, Tom and Henighan, Tom and Hume, Tristan and Bai, Yuntao and Hatfield-Dodds, Zac and Clark, Jack and Bowman, Samuel R. and Askell, Amanda and Grosse, Roger and Hernandez, Danny and Ganguli, Deep and Hubinger, Evan and Schiefer, Nicholas and Kaplan, Jared}, keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Discovering Language Model Behaviors with Model-Written Evaluations}, publisher = {arXiv}, year = {2022}, copyright = {arXiv.org perpetual, non-exclusive license} } ```

imagenet-1k

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - other license_details: imagenet-agreement multilinguality: - monolingual paperswithcode_id: imagenet pretty_name: ImageNet size_categories: - 1M<n<10M source_datasets: - original task_categories: - image-classification task_ids: - multi-class-image-classification extra_gated_prompt: 'By clicking on “Access repository” below, you also agree to ImageNet Terms of Access: [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions: 1. Researcher shall use the Database only for non-commercial research and educational purposes. 2. Princeton University, Stanford University and Hugging Face make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose. 3. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the ImageNet team, Princeton University, Stanford University and Hugging Face, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher''s use of the Database, including but not limited to Researcher''s use of any copies of copyrighted images that he or she may create from the Database. 4. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions. 5. Princeton University, Stanford University and Hugging Face reserve the right to terminate Researcher''s access to the Database at any time. 6. If Researcher is employed by a for-profit, commercial entity, Researcher''s employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer. 7. The law of the State of New Jersey shall apply to all disputes under this agreement.' dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: 0: tench, Tinca tinca 1: goldfish, Carassius auratus 2: great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias 3: tiger shark, Galeocerdo cuvieri 4: hammerhead, hammerhead shark 5: electric ray, crampfish, numbfish, torpedo 6: stingray 7: cock 8: hen 9: ostrich, Struthio camelus 10: brambling, Fringilla montifringilla 11: goldfinch, Carduelis carduelis 12: house finch, linnet, Carpodacus mexicanus 13: junco, snowbird 14: indigo bunting, indigo finch, indigo bird, Passerina cyanea 15: robin, American robin, Turdus migratorius 16: bulbul 17: jay 18: magpie 19: chickadee 20: water ouzel, dipper 21: kite 22: bald eagle, American eagle, Haliaeetus leucocephalus 23: vulture 24: great grey owl, great gray owl, Strix nebulosa 25: European fire salamander, Salamandra salamandra 26: common newt, Triturus vulgaris 27: eft 28: spotted salamander, Ambystoma maculatum 29: axolotl, mud puppy, Ambystoma mexicanum 30: bullfrog, Rana catesbeiana 31: tree frog, tree-frog 32: tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui 33: loggerhead, loggerhead turtle, Caretta caretta 34: leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea 35: mud turtle 36: terrapin 37: box turtle, box tortoise 38: banded gecko 39: common iguana, iguana, Iguana iguana 40: American chameleon, anole, Anolis carolinensis 41: whiptail, whiptail lizard 42: agama 43: frilled lizard, Chlamydosaurus kingi 44: alligator lizard 45: Gila monster, Heloderma suspectum 46: green lizard, Lacerta viridis 47: African chameleon, Chamaeleo chamaeleon 48: Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis 49: African crocodile, Nile crocodile, Crocodylus niloticus 50: American alligator, Alligator mississipiensis 51: triceratops 52: thunder snake, worm snake, Carphophis amoenus 53: ringneck snake, ring-necked snake, ring snake 54: hognose snake, puff adder, sand viper 55: green snake, grass snake 56: king snake, kingsnake 57: garter snake, grass snake 58: water snake 59: vine snake 60: night snake, Hypsiglena torquata 61: boa constrictor, Constrictor constrictor 62: rock python, rock snake, Python sebae 63: Indian cobra, Naja naja 64: green mamba 65: sea snake 66: horned viper, cerastes, sand viper, horned asp, Cerastes cornutus 67: diamondback, diamondback rattlesnake, Crotalus adamanteus 68: sidewinder, horned rattlesnake, Crotalus cerastes 69: trilobite 70: harvestman, daddy longlegs, Phalangium opilio 71: scorpion 72: black and gold garden spider, Argiope aurantia 73: barn spider, Araneus cavaticus 74: garden spider, Aranea diademata 75: black widow, Latrodectus mactans 76: tarantula 77: wolf spider, hunting spider 78: tick 79: centipede 80: black grouse 81: ptarmigan 82: ruffed grouse, partridge, Bonasa umbellus 83: prairie chicken, prairie grouse, prairie fowl 84: peacock 85: quail 86: partridge 87: African grey, African gray, Psittacus erithacus 88: macaw 89: sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita 90: lorikeet 91: coucal 92: bee eater 93: hornbill 94: hummingbird 95: jacamar 96: toucan 97: drake 98: red-breasted merganser, Mergus serrator 99: goose 100: black swan, Cygnus atratus 101: tusker 102: echidna, spiny anteater, anteater 103: platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus 104: wallaby, brush kangaroo 105: koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus 106: wombat 107: jellyfish 108: sea anemone, anemone 109: brain coral 110: flatworm, platyhelminth 111: nematode, nematode worm, roundworm 112: conch 113: snail 114: slug 115: sea slug, nudibranch 116: chiton, coat-of-mail shell, sea cradle, polyplacophore 117: chambered nautilus, pearly nautilus, nautilus 118: Dungeness crab, Cancer magister 119: rock crab, Cancer irroratus 120: fiddler crab 121: king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica 122: American lobster, Northern lobster, Maine lobster, Homarus americanus 123: spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish 124: crayfish, crawfish, crawdad, crawdaddy 125: hermit crab 126: isopod 127: white stork, Ciconia ciconia 128: black stork, Ciconia nigra 129: spoonbill 130: flamingo 131: little blue heron, Egretta caerulea 132: American egret, great white heron, Egretta albus 133: bittern 134: crane 135: limpkin, Aramus pictus 136: European gallinule, Porphyrio porphyrio 137: American coot, marsh hen, mud hen, water hen, Fulica americana 138: bustard 139: ruddy turnstone, Arenaria interpres 140: red-backed sandpiper, dunlin, Erolia alpina 141: redshank, Tringa totanus 142: dowitcher 143: oystercatcher, oyster catcher 144: pelican 145: king penguin, Aptenodytes patagonica 146: albatross, mollymawk 147: grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus 148: killer whale, killer, orca, grampus, sea wolf, Orcinus orca 149: dugong, Dugong dugon 150: sea lion 151: Chihuahua 152: Japanese spaniel 153: Maltese dog, Maltese terrier, Maltese 154: Pekinese, Pekingese, Peke 155: Shih-Tzu 156: Blenheim spaniel 157: papillon 158: toy terrier 159: Rhodesian ridgeback 160: Afghan hound, Afghan 161: basset, basset hound 162: beagle 163: bloodhound, sleuthhound 164: bluetick 165: black-and-tan coonhound 166: Walker hound, Walker foxhound 167: English foxhound 168: redbone 169: borzoi, Russian wolfhound 170: Irish wolfhound 171: Italian greyhound 172: whippet 173: Ibizan hound, Ibizan Podenco 174: Norwegian elkhound, elkhound 175: otterhound, otter hound 176: Saluki, gazelle hound 177: Scottish deerhound, deerhound 178: Weimaraner 179: Staffordshire bullterrier, Staffordshire bull terrier 180: American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier 181: Bedlington terrier 182: Border terrier 183: Kerry blue terrier 184: Irish terrier 185: Norfolk terrier 186: Norwich terrier 187: Yorkshire terrier 188: wire-haired fox terrier 189: Lakeland terrier 190: Sealyham terrier, Sealyham 191: Airedale, Airedale terrier 192: cairn, cairn terrier 193: Australian terrier 194: Dandie Dinmont, Dandie Dinmont terrier 195: Boston bull, Boston terrier 196: miniature schnauzer 197: giant schnauzer 198: standard schnauzer 199: Scotch terrier, Scottish terrier, Scottie 200: Tibetan terrier, chrysanthemum dog 201: silky terrier, Sydney silky 202: soft-coated wheaten terrier 203: West Highland white terrier 204: Lhasa, Lhasa apso 205: flat-coated retriever 206: curly-coated retriever 207: golden retriever 208: Labrador retriever 209: Chesapeake Bay retriever 210: German short-haired pointer 211: vizsla, Hungarian pointer 212: English setter 213: Irish setter, red setter 214: Gordon setter 215: Brittany spaniel 216: clumber, clumber spaniel 217: English springer, English springer spaniel 218: Welsh springer spaniel 219: cocker spaniel, English cocker spaniel, cocker 220: Sussex spaniel 221: Irish water spaniel 222: kuvasz 223: schipperke 224: groenendael 225: malinois 226: briard 227: kelpie 228: komondor 229: Old English sheepdog, bobtail 230: Shetland sheepdog, Shetland sheep dog, Shetland 231: collie 232: Border collie 233: Bouvier des Flandres, Bouviers des Flandres 234: Rottweiler 235: German shepherd, German shepherd dog, German police dog, alsatian 236: Doberman, Doberman pinscher 237: miniature pinscher 238: Greater Swiss Mountain dog 239: Bernese mountain dog 240: Appenzeller 241: EntleBucher 242: boxer 243: bull mastiff 244: Tibetan mastiff 245: French bulldog 246: Great Dane 247: Saint Bernard, St Bernard 248: Eskimo dog, husky 249: malamute, malemute, Alaskan malamute 250: Siberian husky 251: dalmatian, coach dog, carriage dog 252: affenpinscher, monkey pinscher, monkey dog 253: basenji 254: pug, pug-dog 255: Leonberg 256: Newfoundland, Newfoundland dog 257: Great Pyrenees 258: Samoyed, Samoyede 259: Pomeranian 260: chow, chow chow 261: keeshond 262: Brabancon griffon 263: Pembroke, Pembroke Welsh corgi 264: Cardigan, Cardigan Welsh corgi 265: toy poodle 266: miniature poodle 267: standard poodle 268: Mexican hairless 269: timber wolf, grey wolf, gray wolf, Canis lupus 270: white wolf, Arctic wolf, Canis lupus tundrarum 271: red wolf, maned wolf, Canis rufus, Canis niger 272: coyote, prairie wolf, brush wolf, Canis latrans 273: dingo, warrigal, warragal, Canis dingo 274: dhole, Cuon alpinus 275: African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus 276: hyena, hyaena 277: red fox, Vulpes vulpes 278: kit fox, Vulpes macrotis 279: Arctic fox, white fox, Alopex lagopus 280: grey fox, gray fox, Urocyon cinereoargenteus 281: tabby, tabby cat 282: tiger cat 283: Persian cat 284: Siamese cat, Siamese 285: Egyptian cat 286: cougar, puma, catamount, mountain lion, painter, panther, Felis concolor 287: lynx, catamount 288: leopard, Panthera pardus 289: snow leopard, ounce, Panthera uncia 290: jaguar, panther, Panthera onca, Felis onca 291: lion, king of beasts, Panthera leo 292: tiger, Panthera tigris 293: cheetah, chetah, Acinonyx jubatus 294: brown bear, bruin, Ursus arctos 295: American black bear, black bear, Ursus americanus, Euarctos americanus 296: ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus 297: sloth bear, Melursus ursinus, Ursus ursinus 298: mongoose 299: meerkat, mierkat 300: tiger beetle 301: ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle 302: ground beetle, carabid beetle 303: long-horned beetle, longicorn, longicorn beetle 304: leaf beetle, chrysomelid 305: dung beetle 306: rhinoceros beetle 307: weevil 308: fly 309: bee 310: ant, emmet, pismire 311: grasshopper, hopper 312: cricket 313: walking stick, walkingstick, stick insect 314: cockroach, roach 315: mantis, mantid 316: cicada, cicala 317: leafhopper 318: lacewing, lacewing fly 319: dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk 320: damselfly 321: admiral 322: ringlet, ringlet butterfly 323: monarch, monarch butterfly, milkweed butterfly, Danaus plexippus 324: cabbage butterfly 325: sulphur butterfly, sulfur butterfly 326: lycaenid, lycaenid butterfly 327: starfish, sea star 328: sea urchin 329: sea cucumber, holothurian 330: wood rabbit, cottontail, cottontail rabbit 331: hare 332: Angora, Angora rabbit 333: hamster 334: porcupine, hedgehog 335: fox squirrel, eastern fox squirrel, Sciurus niger 336: marmot 337: beaver 338: guinea pig, Cavia cobaya 339: sorrel 340: zebra 341: hog, pig, grunter, squealer, Sus scrofa 342: wild boar, boar, Sus scrofa 343: warthog 344: hippopotamus, hippo, river horse, Hippopotamus amphibius 345: ox 346: water buffalo, water ox, Asiatic buffalo, Bubalus bubalis 347: bison 348: ram, tup 349: bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis 350: ibex, Capra ibex 351: hartebeest 352: impala, Aepyceros melampus 353: gazelle 354: Arabian camel, dromedary, Camelus dromedarius 355: llama 356: weasel 357: mink 358: polecat, fitch, foulmart, foumart, Mustela putorius 359: black-footed ferret, ferret, Mustela nigripes 360: otter 361: skunk, polecat, wood pussy 362: badger 363: armadillo 364: three-toed sloth, ai, Bradypus tridactylus 365: orangutan, orang, orangutang, Pongo pygmaeus 366: gorilla, Gorilla gorilla 367: chimpanzee, chimp, Pan troglodytes 368: gibbon, Hylobates lar 369: siamang, Hylobates syndactylus, Symphalangus syndactylus 370: guenon, guenon monkey 371: patas, hussar monkey, Erythrocebus patas 372: baboon 373: macaque 374: langur 375: colobus, colobus monkey 376: proboscis monkey, Nasalis larvatus 377: marmoset 378: capuchin, ringtail, Cebus capucinus 379: howler monkey, howler 380: titi, titi monkey 381: spider monkey, Ateles geoffroyi 382: squirrel monkey, Saimiri sciureus 383: Madagascar cat, ring-tailed lemur, Lemur catta 384: indri, indris, Indri indri, Indri brevicaudatus 385: Indian elephant, Elephas maximus 386: African elephant, Loxodonta africana 387: lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens 388: giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca 389: barracouta, snoek 390: eel 391: coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch 392: rock beauty, Holocanthus tricolor 393: anemone fish 394: sturgeon 395: gar, garfish, garpike, billfish, Lepisosteus osseus 396: lionfish 397: puffer, pufferfish, blowfish, globefish 398: abacus 399: abaya 400: academic gown, academic robe, judge's robe 401: accordion, piano accordion, squeeze box 402: acoustic guitar 403: aircraft carrier, carrier, flattop, attack aircraft carrier 404: airliner 405: airship, dirigible 406: altar 407: ambulance 408: amphibian, amphibious vehicle 409: analog clock 410: apiary, bee house 411: apron 412: ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin 413: assault rifle, assault gun 414: backpack, back pack, knapsack, packsack, rucksack, haversack 415: bakery, bakeshop, bakehouse 416: balance beam, beam 417: balloon 418: ballpoint, ballpoint pen, ballpen, Biro 419: Band Aid 420: banjo 421: bannister, banister, balustrade, balusters, handrail 422: barbell 423: barber chair 424: barbershop 425: barn 426: barometer 427: barrel, cask 428: barrow, garden cart, lawn cart, wheelbarrow 429: baseball 430: basketball 431: bassinet 432: bassoon 433: bathing cap, swimming cap 434: bath towel 435: bathtub, bathing tub, bath, tub 436: beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon 437: beacon, lighthouse, beacon light, pharos 438: beaker 439: bearskin, busby, shako 440: beer bottle 441: beer glass 442: bell cote, bell cot 443: bib 444: bicycle-built-for-two, tandem bicycle, tandem 445: bikini, two-piece 446: binder, ring-binder 447: binoculars, field glasses, opera glasses 448: birdhouse 449: boathouse 450: bobsled, bobsleigh, bob 451: bolo tie, bolo, bola tie, bola 452: bonnet, poke bonnet 453: bookcase 454: bookshop, bookstore, bookstall 455: bottlecap 456: bow 457: bow tie, bow-tie, bowtie 458: brass, memorial tablet, plaque 459: brassiere, bra, bandeau 460: breakwater, groin, groyne, mole, bulwark, seawall, jetty 461: breastplate, aegis, egis 462: broom 463: bucket, pail 464: buckle 465: bulletproof vest 466: bullet train, bullet 467: butcher shop, meat market 468: cab, hack, taxi, taxicab 469: caldron, cauldron 470: candle, taper, wax light 471: cannon 472: canoe 473: can opener, tin opener 474: cardigan 475: car mirror 476: carousel, carrousel, merry-go-round, roundabout, whirligig 477: carpenter's kit, tool kit 478: carton 479: car wheel 480: cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM 481: cassette 482: cassette player 483: castle 484: catamaran 485: CD player 486: cello, violoncello 487: cellular telephone, cellular phone, cellphone, cell, mobile phone 488: chain 489: chainlink fence 490: chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour 491: chain saw, chainsaw 492: chest 493: chiffonier, commode 494: chime, bell, gong 495: china cabinet, china closet 496: Christmas stocking 497: church, church building 498: cinema, movie theater, movie theatre, movie house, picture palace 499: cleaver, meat cleaver, chopper 500: cliff dwelling 501: cloak 502: clog, geta, patten, sabot 503: cocktail shaker 504: coffee mug 505: coffeepot 506: coil, spiral, volute, whorl, helix 507: combination lock 508: computer keyboard, keypad 509: confectionery, confectionary, candy store 510: container ship, containership, container vessel 511: convertible 512: corkscrew, bottle screw 513: cornet, horn, trumpet, trump 514: cowboy boot 515: cowboy hat, ten-gallon hat 516: cradle 517: crane2 518: crash helmet 519: crate 520: crib, cot 521: Crock Pot 522: croquet ball 523: crutch 524: cuirass 525: dam, dike, dyke 526: desk 527: desktop computer 528: dial telephone, dial phone 529: diaper, nappy, napkin 530: digital clock 531: digital watch 532: dining table, board 533: dishrag, dishcloth 534: dishwasher, dish washer, dishwashing machine 535: disk brake, disc brake 536: dock, dockage, docking facility 537: dogsled, dog sled, dog sleigh 538: dome 539: doormat, welcome mat 540: drilling platform, offshore rig 541: drum, membranophone, tympan 542: drumstick 543: dumbbell 544: Dutch oven 545: electric fan, blower 546: electric guitar 547: electric locomotive 548: entertainment center 549: envelope 550: espresso maker 551: face powder 552: feather boa, boa 553: file, file cabinet, filing cabinet 554: fireboat 555: fire engine, fire truck 556: fire screen, fireguard 557: flagpole, flagstaff 558: flute, transverse flute 559: folding chair 560: football helmet 561: forklift 562: fountain 563: fountain pen 564: four-poster 565: freight car 566: French horn, horn 567: frying pan, frypan, skillet 568: fur coat 569: garbage truck, dustcart 570: gasmask, respirator, gas helmet 571: gas pump, gasoline pump, petrol pump, island dispenser 572: goblet 573: go-kart 574: golf ball 575: golfcart, golf cart 576: gondola 577: gong, tam-tam 578: gown 579: grand piano, grand 580: greenhouse, nursery, glasshouse 581: grille, radiator grille 582: grocery store, grocery, food market, market 583: guillotine 584: hair slide 585: hair spray 586: half track 587: hammer 588: hamper 589: hand blower, blow dryer, blow drier, hair dryer, hair drier 590: hand-held computer, hand-held microcomputer 591: handkerchief, hankie, hanky, hankey 592: hard disc, hard disk, fixed disk 593: harmonica, mouth organ, harp, mouth harp 594: harp 595: harvester, reaper 596: hatchet 597: holster 598: home theater, home theatre 599: honeycomb 600: hook, claw 601: hoopskirt, crinoline 602: horizontal bar, high bar 603: horse cart, horse-cart 604: hourglass 605: iPod 606: iron, smoothing iron 607: jack-o'-lantern 608: jean, blue jean, denim 609: jeep, landrover 610: jersey, T-shirt, tee shirt 611: jigsaw puzzle 612: jinrikisha, ricksha, rickshaw 613: joystick 614: kimono 615: knee pad 616: knot 617: lab coat, laboratory coat 618: ladle 619: lampshade, lamp shade 620: laptop, laptop computer 621: lawn mower, mower 622: lens cap, lens cover 623: letter opener, paper knife, paperknife 624: library 625: lifeboat 626: lighter, light, igniter, ignitor 627: limousine, limo 628: liner, ocean liner 629: lipstick, lip rouge 630: Loafer 631: lotion 632: loudspeaker, speaker, speaker unit, loudspeaker system, speaker system 633: loupe, jeweler's loupe 634: lumbermill, sawmill 635: magnetic compass 636: mailbag, postbag 637: mailbox, letter box 638: maillot 639: maillot, tank suit 640: manhole cover 641: maraca 642: marimba, xylophone 643: mask 644: matchstick 645: maypole 646: maze, labyrinth 647: measuring cup 648: medicine chest, medicine cabinet 649: megalith, megalithic structure 650: microphone, mike 651: microwave, microwave oven 652: military uniform 653: milk can 654: minibus 655: miniskirt, mini 656: minivan 657: missile 658: mitten 659: mixing bowl 660: mobile home, manufactured home 661: Model T 662: modem 663: monastery 664: monitor 665: moped 666: mortar 667: mortarboard 668: mosque 669: mosquito net 670: motor scooter, scooter 671: mountain bike, all-terrain bike, off-roader 672: mountain tent 673: mouse, computer mouse 674: mousetrap 675: moving van 676: muzzle 677: nail 678: neck brace 679: necklace 680: nipple 681: notebook, notebook computer 682: obelisk 683: oboe, hautboy, hautbois 684: ocarina, sweet potato 685: odometer, hodometer, mileometer, milometer 686: oil filter 687: organ, pipe organ 688: oscilloscope, scope, cathode-ray oscilloscope, CRO 689: overskirt 690: oxcart 691: oxygen mask 692: packet 693: paddle, boat paddle 694: paddlewheel, paddle wheel 695: padlock 696: paintbrush 697: pajama, pyjama, pj's, jammies 698: palace 699: panpipe, pandean pipe, syrinx 700: paper towel 701: parachute, chute 702: parallel bars, bars 703: park bench 704: parking meter 705: passenger car, coach, carriage 706: patio, terrace 707: pay-phone, pay-station 708: pedestal, plinth, footstall 709: pencil box, pencil case 710: pencil sharpener 711: perfume, essence 712: Petri dish 713: photocopier 714: pick, plectrum, plectron 715: pickelhaube 716: picket fence, paling 717: pickup, pickup truck 718: pier 719: piggy bank, penny bank 720: pill bottle 721: pillow 722: ping-pong ball 723: pinwheel 724: pirate, pirate ship 725: pitcher, ewer 726: plane, carpenter's plane, woodworking plane 727: planetarium 728: plastic bag 729: plate rack 730: plow, plough 731: plunger, plumber's helper 732: Polaroid camera, Polaroid Land camera 733: pole 734: police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria 735: poncho 736: pool table, billiard table, snooker table 737: pop bottle, soda bottle 738: pot, flowerpot 739: potter's wheel 740: power drill 741: prayer rug, prayer mat 742: printer 743: prison, prison house 744: projectile, missile 745: projector 746: puck, hockey puck 747: punching bag, punch bag, punching ball, punchball 748: purse 749: quill, quill pen 750: quilt, comforter, comfort, puff 751: racer, race car, racing car 752: racket, racquet 753: radiator 754: radio, wireless 755: radio telescope, radio reflector 756: rain barrel 757: recreational vehicle, RV, R.V. 758: reel 759: reflex camera 760: refrigerator, icebox 761: remote control, remote 762: restaurant, eating house, eating place, eatery 763: revolver, six-gun, six-shooter 764: rifle 765: rocking chair, rocker 766: rotisserie 767: rubber eraser, rubber, pencil eraser 768: rugby ball 769: rule, ruler 770: running shoe 771: safe 772: safety pin 773: saltshaker, salt shaker 774: sandal 775: sarong 776: sax, saxophone 777: scabbard 778: scale, weighing machine 779: school bus 780: schooner 781: scoreboard 782: screen, CRT screen 783: screw 784: screwdriver 785: seat belt, seatbelt 786: sewing machine 787: shield, buckler 788: shoe shop, shoe-shop, shoe store 789: shoji 790: shopping basket 791: shopping cart 792: shovel 793: shower cap 794: shower curtain 795: ski 796: ski mask 797: sleeping bag 798: slide rule, slipstick 799: sliding door 800: slot, one-armed bandit 801: snorkel 802: snowmobile 803: snowplow, snowplough 804: soap dispenser 805: soccer ball 806: sock 807: solar dish, solar collector, solar furnace 808: sombrero 809: soup bowl 810: space bar 811: space heater 812: space shuttle 813: spatula 814: speedboat 815: spider web, spider's web 816: spindle 817: sports car, sport car 818: spotlight, spot 819: stage 820: steam locomotive 821: steel arch bridge 822: steel drum 823: stethoscope 824: stole 825: stone wall 826: stopwatch, stop watch 827: stove 828: strainer 829: streetcar, tram, tramcar, trolley, trolley car 830: stretcher 831: studio couch, day bed 832: stupa, tope 833: submarine, pigboat, sub, U-boat 834: suit, suit of clothes 835: sundial 836: sunglass 837: sunglasses, dark glasses, shades 838: sunscreen, sunblock, sun blocker 839: suspension bridge 840: swab, swob, mop 841: sweatshirt 842: swimming trunks, bathing trunks 843: swing 844: switch, electric switch, electrical switch 845: syringe 846: table lamp 847: tank, army tank, armored combat vehicle, armoured combat vehicle 848: tape player 849: teapot 850: teddy, teddy bear 851: television, television system 852: tennis ball 853: thatch, thatched roof 854: theater curtain, theatre curtain 855: thimble 856: thresher, thrasher, threshing machine 857: throne 858: tile roof 859: toaster 860: tobacco shop, tobacconist shop, tobacconist 861: toilet seat 862: torch 863: totem pole 864: tow truck, tow car, wrecker 865: toyshop 866: tractor 867: trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi 868: tray 869: trench coat 870: tricycle, trike, velocipede 871: trimaran 872: tripod 873: triumphal arch 874: trolleybus, trolley coach, trackless trolley 875: trombone 876: tub, vat 877: turnstile 878: typewriter keyboard 879: umbrella 880: unicycle, monocycle 881: upright, upright piano 882: vacuum, vacuum cleaner 883: vase 884: vault 885: velvet 886: vending machine 887: vestment 888: viaduct 889: violin, fiddle 890: volleyball 891: waffle iron 892: wall clock 893: wallet, billfold, notecase, pocketbook 894: wardrobe, closet, press 895: warplane, military plane 896: washbasin, handbasin, washbowl, lavabo, wash-hand basin 897: washer, automatic washer, washing machine 898: water bottle 899: water jug 900: water tower 901: whiskey jug 902: whistle 903: wig 904: window screen 905: window shade 906: Windsor tie 907: wine bottle 908: wing 909: wok 910: wooden spoon 911: wool, woolen, woollen 912: worm fence, snake fence, snake-rail fence, Virginia fence 913: wreck 914: yawl 915: yurt 916: web site, website, internet site, site 917: comic book 918: crossword puzzle, crossword 919: street sign 920: traffic light, traffic signal, stoplight 921: book jacket, dust cover, dust jacket, dust wrapper 922: menu 923: plate 924: guacamole 925: consomme 926: hot pot, hotpot 927: trifle 928: ice cream, icecream 929: ice lolly, lolly, lollipop, popsicle 930: French loaf 931: bagel, beigel 932: pretzel 933: cheeseburger 934: hotdog, hot dog, red hot 935: mashed potato 936: head cabbage 937: broccoli 938: cauliflower 939: zucchini, courgette 940: spaghetti squash 941: acorn squash 942: butternut squash 943: cucumber, cuke 944: artichoke, globe artichoke 945: bell pepper 946: cardoon 947: mushroom 948: Granny Smith 949: strawberry 950: orange 951: lemon 952: fig 953: pineapple, ananas 954: banana 955: jackfruit, jak, jack 956: custard apple 957: pomegranate 958: hay 959: carbonara 960: chocolate sauce, chocolate syrup 961: dough 962: meat loaf, meatloaf 963: pizza, pizza pie 964: potpie 965: burrito 966: red wine 967: espresso 968: cup 969: eggnog 970: alp 971: bubble 972: cliff, drop, drop-off 973: coral reef 974: geyser 975: lakeside, lakeshore 976: promontory, headland, head, foreland 977: sandbar, sand bar 978: seashore, coast, seacoast, sea-coast 979: valley, vale 980: volcano 981: ballplayer, baseball player 982: groom, bridegroom 983: scuba diver 984: rapeseed 985: daisy 986: yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum 987: corn 988: acorn 989: hip, rose hip, rosehip 990: buckeye, horse chestnut, conker 991: coral fungus 992: agaric 993: gyromitra 994: stinkhorn, carrion fungus 995: earthstar 996: hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa 997: bolete 998: ear, spike, capitulum 999: toilet tissue, toilet paper, bathroom tissue splits: - name: test num_bytes: 13613661561 num_examples: 100000 - name: train num_bytes: 146956944242 num_examples: 1281167 - name: validation num_bytes: 6709003386 num_examples: 50000 download_size: 166009941208 dataset_size: 167279609189 --- # Dataset Card for ImageNet ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://image-net.org/index.php - **Repository:** - **Paper:** https://arxiv.org/abs/1409.0575 - **Leaderboard:** https://paperswithcode.com/sota/image-classification-on-imagenet?tag_filter=171 - **Point of Contact:** mailto: imagenet.help.desk@gmail.com ### Dataset Summary ILSVRC 2012, commonly known as 'ImageNet' is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a "synonym set" or "synset". There are more than 100,000 synsets in WordNet, majority of them are nouns (80,000+). ImageNet aims to provide on average 1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated. 💡 This dataset provides access to ImageNet (ILSVRC) 2012 which is the most commonly used **subset** of ImageNet. This dataset spans 1000 object classes and contains 1,281,167 training images, 50,000 validation images and 100,000 test images. The version also has the [patch](https://drive.google.com/file/d/16RYnHpVOW0XKCsn3G3S9GTHUyoV2-4WX/view) which fixes some of the corrupted test set images already applied. For full ImageNet dataset presented in [[2]](https://ieeexplore.ieee.org/abstract/document/5206848), please check the download section of the [main website](https://image-net.org/download-images.php). ### Supported Tasks and Leaderboards - `image-classification`: The goal of this task is to classify a given image into one of 1000 ImageNet classes. The leaderboard is available [here](https://paperswithcode.com/sota/image-classification-on-imagenet?tag_filter=171). To evaluate the `imagenet-classification` accuracy on the test split, one must first create an account at https://image-net.org. This account must be approved by the site administrator. After the account is created, one can submit the results to the test server at https://image-net.org/challenges/LSVRC/eval_server.php The submission consists of several ASCII text files corresponding to multiple tasks. The task of interest is "Classification submission (top-5 cls error)". A sample of an exported text file looks like the following: ``` 670 778 794 387 650 217 691 564 909 364 737 369 430 531 124 755 930 755 512 152 ``` The export format is described in full in "readme.txt" within the 2013 development kit available here: https://image-net.org/data/ILSVRC/2013/ILSVRC2013_devkit.tgz. Please see the section entitled "3.3 CLS-LOC submission format". Briefly, the format of the text file is 100,000 lines corresponding to each image in the test split. Each line of integers correspond to the rank-ordered, top 5 predictions for each test image. The integers are 1-indexed corresponding to the line number in the corresponding labels file. See `imagenet2012_labels.txt`. ### Languages The class labels in the dataset are in English. ## Dataset Structure ### Data Instances An example looks like below: ``` { 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=384x512 at 0x276021C5EB8>, 'label': 23 } ``` ### Data Fields The data instances have the following fields: - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`. - `label`: an `int` classification label. -1 for `test` set as the labels are missing. The labels are indexed based on a sorted list of synset ids such as `n07565083` which we automatically map to original class names. The original dataset is divided into folders based on these synset ids. To get a mapping from original synset names, use the file [LOC_synset_mapping.txt](https://www.kaggle.com/competitions/imagenet-object-localization-challenge/data?select=LOC_synset_mapping.txt) available on Kaggle challenge page. You can also use `dataset_instance.features["labels"].int2str` function to get the class for a particular label index. Also note that, labels for test set are returned as -1 as they are missing. <details> <summary> Click here to see the full list of ImageNet class labels mapping: </summary> |id|Class| |--|-----| |0 | tench, Tinca tinca| |1 | goldfish, Carassius auratus| |2 | great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias| |3 | tiger shark, Galeocerdo cuvieri| |4 | hammerhead, hammerhead shark| |5 | electric ray, crampfish, numbfish, torpedo| |6 | stingray| |7 | cock| |8 | hen| |9 | ostrich, Struthio camelus| |10 | brambling, Fringilla montifringilla| |11 | goldfinch, Carduelis carduelis| |12 | house finch, linnet, Carpodacus mexicanus| |13 | junco, snowbird| |14 | indigo bunting, indigo finch, indigo bird, Passerina cyanea| |15 | robin, American robin, Turdus migratorius| |16 | bulbul| |17 | jay| |18 | magpie| |19 | chickadee| |20 | water ouzel, dipper| |21 | kite| |22 | bald eagle, American eagle, Haliaeetus leucocephalus| |23 | vulture| |24 | great grey owl, great gray owl, Strix nebulosa| |25 | European fire salamander, Salamandra salamandra| |26 | common newt, Triturus vulgaris| |27 | eft| |28 | spotted salamander, Ambystoma maculatum| |29 | axolotl, mud puppy, Ambystoma mexicanum| |30 | bullfrog, Rana catesbeiana| |31 | tree frog, tree-frog| |32 | tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui| |33 | loggerhead, loggerhead turtle, Caretta caretta| |34 | leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea| |35 | mud turtle| |36 | terrapin| |37 | box turtle, box tortoise| |38 | banded gecko| |39 | common iguana, iguana, Iguana iguana| |40 | American chameleon, anole, Anolis carolinensis| |41 | whiptail, whiptail lizard| |42 | agama| |43 | frilled lizard, Chlamydosaurus kingi| |44 | alligator lizard| |45 | Gila monster, Heloderma suspectum| |46 | green lizard, Lacerta viridis| |47 | African chameleon, Chamaeleo chamaeleon| |48 | Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis| |49 | African crocodile, Nile crocodile, Crocodylus niloticus| |50 | American alligator, Alligator mississipiensis| |51 | triceratops| |52 | thunder snake, worm snake, Carphophis amoenus| |53 | ringneck snake, ring-necked snake, ring snake| |54 | hognose snake, puff adder, sand viper| |55 | green snake, grass snake| |56 | king snake, kingsnake| |57 | garter snake, grass snake| |58 | water snake| |59 | vine snake| |60 | night snake, Hypsiglena torquata| |61 | boa constrictor, Constrictor constrictor| |62 | rock python, rock snake, Python sebae| |63 | Indian cobra, Naja naja| |64 | green mamba| |65 | sea snake| |66 | horned viper, cerastes, sand viper, horned asp, Cerastes cornutus| |67 | diamondback, diamondback rattlesnake, Crotalus adamanteus| |68 | sidewinder, horned rattlesnake, Crotalus cerastes| |69 | trilobite| |70 | harvestman, daddy longlegs, Phalangium opilio| |71 | scorpion| |72 | black and gold garden spider, Argiope aurantia| |73 | barn spider, Araneus cavaticus| |74 | garden spider, Aranea diademata| |75 | black widow, Latrodectus mactans| |76 | tarantula| |77 | wolf spider, hunting spider| |78 | tick| |79 | centipede| |80 | black grouse| |81 | ptarmigan| |82 | ruffed grouse, partridge, Bonasa umbellus| |83 | prairie chicken, prairie grouse, prairie fowl| |84 | peacock| |85 | quail| |86 | partridge| |87 | African grey, African gray, Psittacus erithacus| |88 | macaw| |89 | sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita| |90 | lorikeet| |91 | coucal| |92 | bee eater| |93 | hornbill| |94 | hummingbird| |95 | jacamar| |96 | toucan| |97 | drake| |98 | red-breasted merganser, Mergus serrator| |99 | goose| |100 | black swan, Cygnus atratus| |101 | tusker| |102 | echidna, spiny anteater, anteater| |103 | platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus| |104 | wallaby, brush kangaroo| |105 | koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus| |106 | wombat| |107 | jellyfish| |108 | sea anemone, anemone| |109 | brain coral| |110 | flatworm, platyhelminth| |111 | nematode, nematode worm, roundworm| |112 | conch| |113 | snail| |114 | slug| |115 | sea slug, nudibranch| |116 | chiton, coat-of-mail shell, sea cradle, polyplacophore| |117 | chambered nautilus, pearly nautilus, nautilus| |118 | Dungeness crab, Cancer magister| |119 | rock crab, Cancer irroratus| |120 | fiddler crab| |121 | king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica| |122 | American lobster, Northern lobster, Maine lobster, Homarus americanus| |123 | spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish| |124 | crayfish, crawfish, crawdad, crawdaddy| |125 | hermit crab| |126 | isopod| |127 | white stork, Ciconia ciconia| |128 | black stork, Ciconia nigra| |129 | spoonbill| |130 | flamingo| |131 | little blue heron, Egretta caerulea| |132 | American egret, great white heron, Egretta albus| |133 | bittern| |134 | crane| |135 | limpkin, Aramus pictus| |136 | European gallinule, Porphyrio porphyrio| |137 | American coot, marsh hen, mud hen, water hen, Fulica americana| |138 | bustard| |139 | ruddy turnstone, Arenaria interpres| |140 | red-backed sandpiper, dunlin, Erolia alpina| |141 | redshank, Tringa totanus| |142 | dowitcher| |143 | oystercatcher, oyster catcher| |144 | pelican| |145 | king penguin, Aptenodytes patagonica| |146 | albatross, mollymawk| |147 | grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus| |148 | killer whale, killer, orca, grampus, sea wolf, Orcinus orca| |149 | dugong, Dugong dugon| |150 | sea lion| |151 | Chihuahua| |152 | Japanese spaniel| |153 | Maltese dog, Maltese terrier, Maltese| |154 | Pekinese, Pekingese, Peke| |155 | Shih-Tzu| |156 | Blenheim spaniel| |157 | papillon| |158 | toy terrier| |159 | Rhodesian ridgeback| |160 | Afghan hound, Afghan| |161 | basset, basset hound| |162 | beagle| |163 | bloodhound, sleuthhound| |164 | bluetick| |165 | black-and-tan coonhound| |166 | Walker hound, Walker foxhound| |167 | English foxhound| |168 | redbone| |169 | borzoi, Russian wolfhound| |170 | Irish wolfhound| |171 | Italian greyhound| |172 | whippet| |173 | Ibizan hound, Ibizan Podenco| |174 | Norwegian elkhound, elkhound| |175 | otterhound, otter hound| |176 | Saluki, gazelle hound| |177 | Scottish deerhound, deerhound| |178 | Weimaraner| |179 | Staffordshire bullterrier, Staffordshire bull terrier| |180 | American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier| |181 | Bedlington terrier| |182 | Border terrier| |183 | Kerry blue terrier| |184 | Irish terrier| |185 | Norfolk terrier| |186 | Norwich terrier| |187 | Yorkshire terrier| |188 | wire-haired fox terrier| |189 | Lakeland terrier| |190 | Sealyham terrier, Sealyham| |191 | Airedale, Airedale terrier| |192 | cairn, cairn terrier| |193 | Australian terrier| |194 | Dandie Dinmont, Dandie Dinmont terrier| |195 | Boston bull, Boston terrier| |196 | miniature schnauzer| |197 | giant schnauzer| |198 | standard schnauzer| |199 | Scotch terrier, Scottish terrier, Scottie| |200 | Tibetan terrier, chrysanthemum dog| |201 | silky terrier, Sydney silky| |202 | soft-coated wheaten terrier| |203 | West Highland white terrier| |204 | Lhasa, Lhasa apso| |205 | flat-coated retriever| |206 | curly-coated retriever| |207 | golden retriever| |208 | Labrador retriever| |209 | Chesapeake Bay retriever| |210 | German short-haired pointer| |211 | vizsla, Hungarian pointer| |212 | English setter| |213 | Irish setter, red setter| |214 | Gordon setter| |215 | Brittany spaniel| |216 | clumber, clumber spaniel| |217 | English springer, English springer spaniel| |218 | Welsh springer spaniel| |219 | cocker spaniel, English cocker spaniel, cocker| |220 | Sussex spaniel| |221 | Irish water spaniel| |222 | kuvasz| |223 | schipperke| |224 | groenendael| |225 | malinois| |226 | briard| |227 | kelpie| |228 | komondor| |229 | Old English sheepdog, bobtail| |230 | Shetland sheepdog, Shetland sheep dog, Shetland| |231 | collie| |232 | Border collie| |233 | Bouvier des Flandres, Bouviers des Flandres| |234 | Rottweiler| |235 | German shepherd, German shepherd dog, German police dog, alsatian| |236 | Doberman, Doberman pinscher| |237 | miniature pinscher| |238 | Greater Swiss Mountain dog| |239 | Bernese mountain dog| |240 | Appenzeller| |241 | EntleBucher| |242 | boxer| |243 | bull mastiff| |244 | Tibetan mastiff| |245 | French bulldog| |246 | Great Dane| |247 | Saint Bernard, St Bernard| |248 | Eskimo dog, husky| |249 | malamute, malemute, Alaskan malamute| |250 | Siberian husky| |251 | dalmatian, coach dog, carriage dog| |252 | affenpinscher, monkey pinscher, monkey dog| |253 | basenji| |254 | pug, pug-dog| |255 | Leonberg| |256 | Newfoundland, Newfoundland dog| |257 | Great Pyrenees| |258 | Samoyed, Samoyede| |259 | Pomeranian| |260 | chow, chow chow| |261 | keeshond| |262 | Brabancon griffon| |263 | Pembroke, Pembroke Welsh corgi| |264 | Cardigan, Cardigan Welsh corgi| |265 | toy poodle| |266 | miniature poodle| |267 | standard poodle| |268 | Mexican hairless| |269 | timber wolf, grey wolf, gray wolf, Canis lupus| |270 | white wolf, Arctic wolf, Canis lupus tundrarum| |271 | red wolf, maned wolf, Canis rufus, Canis niger| |272 | coyote, prairie wolf, brush wolf, Canis latrans| |273 | dingo, warrigal, warragal, Canis dingo| |274 | dhole, Cuon alpinus| |275 | African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus| |276 | hyena, hyaena| |277 | red fox, Vulpes vulpes| |278 | kit fox, Vulpes macrotis| |279 | Arctic fox, white fox, Alopex lagopus| |280 | grey fox, gray fox, Urocyon cinereoargenteus| |281 | tabby, tabby cat| |282 | tiger cat| |283 | Persian cat| |284 | Siamese cat, Siamese| |285 | Egyptian cat| |286 | cougar, puma, catamount, mountain lion, painter, panther, Felis concolor| |287 | lynx, catamount| |288 | leopard, Panthera pardus| |289 | snow leopard, ounce, Panthera uncia| |290 | jaguar, panther, Panthera onca, Felis onca| |291 | lion, king of beasts, Panthera leo| |292 | tiger, Panthera tigris| |293 | cheetah, chetah, Acinonyx jubatus| |294 | brown bear, bruin, Ursus arctos| |295 | American black bear, black bear, Ursus americanus, Euarctos americanus| |296 | ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus| |297 | sloth bear, Melursus ursinus, Ursus ursinus| |298 | mongoose| |299 | meerkat, mierkat| |300 | tiger beetle| |301 | ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle| |302 | ground beetle, carabid beetle| |303 | long-horned beetle, longicorn, longicorn beetle| |304 | leaf beetle, chrysomelid| |305 | dung beetle| |306 | rhinoceros beetle| |307 | weevil| |308 | fly| |309 | bee| |310 | ant, emmet, pismire| |311 | grasshopper, hopper| |312 | cricket| |313 | walking stick, walkingstick, stick insect| |314 | cockroach, roach| |315 | mantis, mantid| |316 | cicada, cicala| |317 | leafhopper| |318 | lacewing, lacewing fly| |319 | dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk| |320 | damselfly| |321 | admiral| |322 | ringlet, ringlet butterfly| |323 | monarch, monarch butterfly, milkweed butterfly, Danaus plexippus| |324 | cabbage butterfly| |325 | sulphur butterfly, sulfur butterfly| |326 | lycaenid, lycaenid butterfly| |327 | starfish, sea star| |328 | sea urchin| |329 | sea cucumber, holothurian| |330 | wood rabbit, cottontail, cottontail rabbit| |331 | hare| |332 | Angora, Angora rabbit| |333 | hamster| |334 | porcupine, hedgehog| |335 | fox squirrel, eastern fox squirrel, Sciurus niger| |336 | marmot| |337 | beaver| |338 | guinea pig, Cavia cobaya| |339 | sorrel| |340 | zebra| |341 | hog, pig, grunter, squealer, Sus scrofa| |342 | wild boar, boar, Sus scrofa| |343 | warthog| |344 | hippopotamus, hippo, river horse, Hippopotamus amphibius| |345 | ox| |346 | water buffalo, water ox, Asiatic buffalo, Bubalus bubalis| |347 | bison| |348 | ram, tup| |349 | bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis| |350 | ibex, Capra ibex| |351 | hartebeest| |352 | impala, Aepyceros melampus| |353 | gazelle| |354 | Arabian camel, dromedary, Camelus dromedarius| |355 | llama| |356 | weasel| |357 | mink| |358 | polecat, fitch, foulmart, foumart, Mustela putorius| |359 | black-footed ferret, ferret, Mustela nigripes| |360 | otter| |361 | skunk, polecat, wood pussy| |362 | badger| |363 | armadillo| |364 | three-toed sloth, ai, Bradypus tridactylus| |365 | orangutan, orang, orangutang, Pongo pygmaeus| |366 | gorilla, Gorilla gorilla| |367 | chimpanzee, chimp, Pan troglodytes| |368 | gibbon, Hylobates lar| |369 | siamang, Hylobates syndactylus, Symphalangus syndactylus| |370 | guenon, guenon monkey| |371 | patas, hussar monkey, Erythrocebus patas| |372 | baboon| |373 | macaque| |374 | langur| |375 | colobus, colobus monkey| |376 | proboscis monkey, Nasalis larvatus| |377 | marmoset| |378 | capuchin, ringtail, Cebus capucinus| |379 | howler monkey, howler| |380 | titi, titi monkey| |381 | spider monkey, Ateles geoffroyi| |382 | squirrel monkey, Saimiri sciureus| |383 | Madagascar cat, ring-tailed lemur, Lemur catta| |384 | indri, indris, Indri indri, Indri brevicaudatus| |385 | Indian elephant, Elephas maximus| |386 | African elephant, Loxodonta africana| |387 | lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens| |388 | giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca| |389 | barracouta, snoek| |390 | eel| |391 | coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch| |392 | rock beauty, Holocanthus tricolor| |393 | anemone fish| |394 | sturgeon| |395 | gar, garfish, garpike, billfish, Lepisosteus osseus| |396 | lionfish| |397 | puffer, pufferfish, blowfish, globefish| |398 | abacus| |399 | abaya| |400 | academic gown, academic robe, judge's robe| |401 | accordion, piano accordion, squeeze box| |402 | acoustic guitar| |403 | aircraft carrier, carrier, flattop, attack aircraft carrier| |404 | airliner| |405 | airship, dirigible| |406 | altar| |407 | ambulance| |408 | amphibian, amphibious vehicle| |409 | analog clock| |410 | apiary, bee house| |411 | apron| |412 | ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin| |413 | assault rifle, assault gun| |414 | backpack, back pack, knapsack, packsack, rucksack, haversack| |415 | bakery, bakeshop, bakehouse| |416 | balance beam, beam| |417 | balloon| |418 | ballpoint, ballpoint pen, ballpen, Biro| |419 | Band Aid| |420 | banjo| |421 | bannister, banister, balustrade, balusters, handrail| |422 | barbell| |423 | barber chair| |424 | barbershop| |425 | barn| |426 | barometer| |427 | barrel, cask| |428 | barrow, garden cart, lawn cart, wheelbarrow| |429 | baseball| |430 | basketball| |431 | bassinet| |432 | bassoon| |433 | bathing cap, swimming cap| |434 | bath towel| |435 | bathtub, bathing tub, bath, tub| |436 | beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon| |437 | beacon, lighthouse, beacon light, pharos| |438 | beaker| |439 | bearskin, busby, shako| |440 | beer bottle| |441 | beer glass| |442 | bell cote, bell cot| |443 | bib| |444 | bicycle-built-for-two, tandem bicycle, tandem| |445 | bikini, two-piece| |446 | binder, ring-binder| |447 | binoculars, field glasses, opera glasses| |448 | birdhouse| |449 | boathouse| |450 | bobsled, bobsleigh, bob| |451 | bolo tie, bolo, bola tie, bola| |452 | bonnet, poke bonnet| |453 | bookcase| |454 | bookshop, bookstore, bookstall| |455 | bottlecap| |456 | bow| |457 | bow tie, bow-tie, bowtie| |458 | brass, memorial tablet, plaque| |459 | brassiere, bra, bandeau| |460 | breakwater, groin, groyne, mole, bulwark, seawall, jetty| |461 | breastplate, aegis, egis| |462 | broom| |463 | bucket, pail| |464 | buckle| |465 | bulletproof vest| |466 | bullet train, bullet| |467 | butcher shop, meat market| |468 | cab, hack, taxi, taxicab| |469 | caldron, cauldron| |470 | candle, taper, wax light| |471 | cannon| |472 | canoe| |473 | can opener, tin opener| |474 | cardigan| |475 | car mirror| |476 | carousel, carrousel, merry-go-round, roundabout, whirligig| |477 | carpenter's kit, tool kit| |478 | carton| |479 | car wheel| |480 | cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM| |481 | cassette| |482 | cassette player| |483 | castle| |484 | catamaran| |485 | CD player| |486 | cello, violoncello| |487 | cellular telephone, cellular phone, cellphone, cell, mobile phone| |488 | chain| |489 | chainlink fence| |490 | chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour| |491 | chain saw, chainsaw| |492 | chest| |493 | chiffonier, commode| |494 | chime, bell, gong| |495 | china cabinet, china closet| |496 | Christmas stocking| |497 | church, church building| |498 | cinema, movie theater, movie theatre, movie house, picture palace| |499 | cleaver, meat cleaver, chopper| |500 | cliff dwelling| |501 | cloak| |502 | clog, geta, patten, sabot| |503 | cocktail shaker| |504 | coffee mug| |505 | coffeepot| |506 | coil, spiral, volute, whorl, helix| |507 | combination lock| |508 | computer keyboard, keypad| |509 | confectionery, confectionary, candy store| |510 | container ship, containership, container vessel| |511 | convertible| |512 | corkscrew, bottle screw| |513 | cornet, horn, trumpet, trump| |514 | cowboy boot| |515 | cowboy hat, ten-gallon hat| |516 | cradle| |517 | crane_1| |518 | crash helmet| |519 | crate| |520 | crib, cot| |521 | Crock Pot| |522 | croquet ball| |523 | crutch| |524 | cuirass| |525 | dam, dike, dyke| |526 | desk| |527 | desktop computer| |528 | dial telephone, dial phone| |529 | diaper, nappy, napkin| |530 | digital clock| |531 | digital watch| |532 | dining table, board| |533 | dishrag, dishcloth| |534 | dishwasher, dish washer, dishwashing machine| |535 | disk brake, disc brake| |536 | dock, dockage, docking facility| |537 | dogsled, dog sled, dog sleigh| |538 | dome| |539 | doormat, welcome mat| |540 | drilling platform, offshore rig| |541 | drum, membranophone, tympan| |542 | drumstick| |543 | dumbbell| |544 | Dutch oven| |545 | electric fan, blower| |546 | electric guitar| |547 | electric locomotive| |548 | entertainment center| |549 | envelope| |550 | espresso maker| |551 | face powder| |552 | feather boa, boa| |553 | file, file cabinet, filing cabinet| |554 | fireboat| |555 | fire engine, fire truck| |556 | fire screen, fireguard| |557 | flagpole, flagstaff| |558 | flute, transverse flute| |559 | folding chair| |560 | football helmet| |561 | forklift| |562 | fountain| |563 | fountain pen| |564 | four-poster| |565 | freight car| |566 | French horn, horn| |567 | frying pan, frypan, skillet| |568 | fur coat| |569 | garbage truck, dustcart| |570 | gasmask, respirator, gas helmet| |571 | gas pump, gasoline pump, petrol pump, island dispenser| |572 | goblet| |573 | go-kart| |574 | golf ball| |575 | golfcart, golf cart| |576 | gondola| |577 | gong, tam-tam| |578 | gown| |579 | grand piano, grand| |580 | greenhouse, nursery, glasshouse| |581 | grille, radiator grille| |582 | grocery store, grocery, food market, market| |583 | guillotine| |584 | hair slide| |585 | hair spray| |586 | half track| |587 | hammer| |588 | hamper| |589 | hand blower, blow dryer, blow drier, hair dryer, hair drier| |590 | hand-held computer, hand-held microcomputer| |591 | handkerchief, hankie, hanky, hankey| |592 | hard disc, hard disk, fixed disk| |593 | harmonica, mouth organ, harp, mouth harp| |594 | harp| |595 | harvester, reaper| |596 | hatchet| |597 | holster| |598 | home theater, home theatre| |599 | honeycomb| |600 | hook, claw| |601 | hoopskirt, crinoline| |602 | horizontal bar, high bar| |603 | horse cart, horse-cart| |604 | hourglass| |605 | iPod| |606 | iron, smoothing iron| |607 | jack-o'-lantern| |608 | jean, blue jean, denim| |609 | jeep, landrover| |610 | jersey, T-shirt, tee shirt| |611 | jigsaw puzzle| |612 | jinrikisha, ricksha, rickshaw| |613 | joystick| |614 | kimono| |615 | knee pad| |616 | knot| |617 | lab coat, laboratory coat| |618 | ladle| |619 | lampshade, lamp shade| |620 | laptop, laptop computer| |621 | lawn mower, mower| |622 | lens cap, lens cover| |623 | letter opener, paper knife, paperknife| |624 | library| |625 | lifeboat| |626 | lighter, light, igniter, ignitor| |627 | limousine, limo| |628 | liner, ocean liner| |629 | lipstick, lip rouge| |630 | Loafer| |631 | lotion| |632 | loudspeaker, speaker, speaker unit, loudspeaker system, speaker system| |633 | loupe, jeweler's loupe| |634 | lumbermill, sawmill| |635 | magnetic compass| |636 | mailbag, postbag| |637 | mailbox, letter box| |638 | maillot| |639 | maillot, tank suit| |640 | manhole cover| |641 | maraca| |642 | marimba, xylophone| |643 | mask| |644 | matchstick| |645 | maypole| |646 | maze, labyrinth| |647 | measuring cup| |648 | medicine chest, medicine cabinet| |649 | megalith, megalithic structure| |650 | microphone, mike| |651 | microwave, microwave oven| |652 | military uniform| |653 | milk can| |654 | minibus| |655 | miniskirt, mini| |656 | minivan| |657 | missile| |658 | mitten| |659 | mixing bowl| |660 | mobile home, manufactured home| |661 | Model T| |662 | modem| |663 | monastery| |664 | monitor| |665 | moped| |666 | mortar| |667 | mortarboard| |668 | mosque| |669 | mosquito net| |670 | motor scooter, scooter| |671 | mountain bike, all-terrain bike, off-roader| |672 | mountain tent| |673 | mouse, computer mouse| |674 | mousetrap| |675 | moving van| |676 | muzzle| |677 | nail| |678 | neck brace| |679 | necklace| |680 | nipple| |681 | notebook, notebook computer| |682 | obelisk| |683 | oboe, hautboy, hautbois| |684 | ocarina, sweet potato| |685 | odometer, hodometer, mileometer, milometer| |686 | oil filter| |687 | organ, pipe organ| |688 | oscilloscope, scope, cathode-ray oscilloscope, CRO| |689 | overskirt| |690 | oxcart| |691 | oxygen mask| |692 | packet| |693 | paddle, boat paddle| |694 | paddlewheel, paddle wheel| |695 | padlock| |696 | paintbrush| |697 | pajama, pyjama, pj's, jammies| |698 | palace| |699 | panpipe, pandean pipe, syrinx| |700 | paper towel| |701 | parachute, chute| |702 | parallel bars, bars| |703 | park bench| |704 | parking meter| |705 | passenger car, coach, carriage| |706 | patio, terrace| |707 | pay-phone, pay-station| |708 | pedestal, plinth, footstall| |709 | pencil box, pencil case| |710 | pencil sharpener| |711 | perfume, essence| |712 | Petri dish| |713 | photocopier| |714 | pick, plectrum, plectron| |715 | pickelhaube| |716 | picket fence, paling| |717 | pickup, pickup truck| |718 | pier| |719 | piggy bank, penny bank| |720 | pill bottle| |721 | pillow| |722 | ping-pong ball| |723 | pinwheel| |724 | pirate, pirate ship| |725 | pitcher, ewer| |726 | plane, carpenter's plane, woodworking plane| |727 | planetarium| |728 | plastic bag| |729 | plate rack| |730 | plow, plough| |731 | plunger, plumber's helper| |732 | Polaroid camera, Polaroid Land camera| |733 | pole| |734 | police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria| |735 | poncho| |736 | pool table, billiard table, snooker table| |737 | pop bottle, soda bottle| |738 | pot, flowerpot| |739 | potter's wheel| |740 | power drill| |741 | prayer rug, prayer mat| |742 | printer| |743 | prison, prison house| |744 | projectile, missile| |745 | projector| |746 | puck, hockey puck| |747 | punching bag, punch bag, punching ball, punchball| |748 | purse| |749 | quill, quill pen| |750 | quilt, comforter, comfort, puff| |751 | racer, race car, racing car| |752 | racket, racquet| |753 | radiator| |754 | radio, wireless| |755 | radio telescope, radio reflector| |756 | rain barrel| |757 | recreational vehicle, RV, R.V.| |758 | reel| |759 | reflex camera| |760 | refrigerator, icebox| |761 | remote control, remote| |762 | restaurant, eating house, eating place, eatery| |763 | revolver, six-gun, six-shooter| |764 | rifle| |765 | rocking chair, rocker| |766 | rotisserie| |767 | rubber eraser, rubber, pencil eraser| |768 | rugby ball| |769 | rule, ruler| |770 | running shoe| |771 | safe| |772 | safety pin| |773 | saltshaker, salt shaker| |774 | sandal| |775 | sarong| |776 | sax, saxophone| |777 | scabbard| |778 | scale, weighing machine| |779 | school bus| |780 | schooner| |781 | scoreboard| |782 | screen, CRT screen| |783 | screw| |784 | screwdriver| |785 | seat belt, seatbelt| |786 | sewing machine| |787 | shield, buckler| |788 | shoe shop, shoe-shop, shoe store| |789 | shoji| |790 | shopping basket| |791 | shopping cart| |792 | shovel| |793 | shower cap| |794 | shower curtain| |795 | ski| |796 | ski mask| |797 | sleeping bag| |798 | slide rule, slipstick| |799 | sliding door| |800 | slot, one-armed bandit| |801 | snorkel| |802 | snowmobile| |803 | snowplow, snowplough| |804 | soap dispenser| |805 | soccer ball| |806 | sock| |807 | solar dish, solar collector, solar furnace| |808 | sombrero| |809 | soup bowl| |810 | space bar| |811 | space heater| |812 | space shuttle| |813 | spatula| |814 | speedboat| |815 | spider web, spider's web| |816 | spindle| |817 | sports car, sport car| |818 | spotlight, spot| |819 | stage| |820 | steam locomotive| |821 | steel arch bridge| |822 | steel drum| |823 | stethoscope| |824 | stole| |825 | stone wall| |826 | stopwatch, stop watch| |827 | stove| |828 | strainer| |829 | streetcar, tram, tramcar, trolley, trolley car| |830 | stretcher| |831 | studio couch, day bed| |832 | stupa, tope| |833 | submarine, pigboat, sub, U-boat| |834 | suit, suit of clothes| |835 | sundial| |836 | sunglass| |837 | sunglasses, dark glasses, shades| |838 | sunscreen, sunblock, sun blocker| |839 | suspension bridge| |840 | swab, swob, mop| |841 | sweatshirt| |842 | swimming trunks, bathing trunks| |843 | swing| |844 | switch, electric switch, electrical switch| |845 | syringe| |846 | table lamp| |847 | tank, army tank, armored combat vehicle, armoured combat vehicle| |848 | tape player| |849 | teapot| |850 | teddy, teddy bear| |851 | television, television system| |852 | tennis ball| |853 | thatch, thatched roof| |854 | theater curtain, theatre curtain| |855 | thimble| |856 | thresher, thrasher, threshing machine| |857 | throne| |858 | tile roof| |859 | toaster| |860 | tobacco shop, tobacconist shop, tobacconist| |861 | toilet seat| |862 | torch| |863 | totem pole| |864 | tow truck, tow car, wrecker| |865 | toyshop| |866 | tractor| |867 | trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi| |868 | tray| |869 | trench coat| |870 | tricycle, trike, velocipede| |871 | trimaran| |872 | tripod| |873 | triumphal arch| |874 | trolleybus, trolley coach, trackless trolley| |875 | trombone| |876 | tub, vat| |877 | turnstile| |878 | typewriter keyboard| |879 | umbrella| |880 | unicycle, monocycle| |881 | upright, upright piano| |882 | vacuum, vacuum cleaner| |883 | vase| |884 | vault| |885 | velvet| |886 | vending machine| |887 | vestment| |888 | viaduct| |889 | violin, fiddle| |890 | volleyball| |891 | waffle iron| |892 | wall clock| |893 | wallet, billfold, notecase, pocketbook| |894 | wardrobe, closet, press| |895 | warplane, military plane| |896 | washbasin, handbasin, washbowl, lavabo, wash-hand basin| |897 | washer, automatic washer, washing machine| |898 | water bottle| |899 | water jug| |900 | water tower| |901 | whiskey jug| |902 | whistle| |903 | wig| |904 | window screen| |905 | window shade| |906 | Windsor tie| |907 | wine bottle| |908 | wing| |909 | wok| |910 | wooden spoon| |911 | wool, woolen, woollen| |912 | worm fence, snake fence, snake-rail fence, Virginia fence| |913 | wreck| |914 | yawl| |915 | yurt| |916 | web site, website, internet site, site| |917 | comic book| |918 | crossword puzzle, crossword| |919 | street sign| |920 | traffic light, traffic signal, stoplight| |921 | book jacket, dust cover, dust jacket, dust wrapper| |922 | menu| |923 | plate| |924 | guacamole| |925 | consomme| |926 | hot pot, hotpot| |927 | trifle| |928 | ice cream, icecream| |929 | ice lolly, lolly, lollipop, popsicle| |930 | French loaf| |931 | bagel, beigel| |932 | pretzel| |933 | cheeseburger| |934 | hotdog, hot dog, red hot| |935 | mashed potato| |936 | head cabbage| |937 | broccoli| |938 | cauliflower| |939 | zucchini, courgette| |940 | spaghetti squash| |941 | acorn squash| |942 | butternut squash| |943 | cucumber, cuke| |944 | artichoke, globe artichoke| |945 | bell pepper| |946 | cardoon| |947 | mushroom| |948 | Granny Smith| |949 | strawberry| |950 | orange| |951 | lemon| |952 | fig| |953 | pineapple, ananas| |954 | banana| |955 | jackfruit, jak, jack| |956 | custard apple| |957 | pomegranate| |958 | hay| |959 | carbonara| |960 | chocolate sauce, chocolate syrup| |961 | dough| |962 | meat loaf, meatloaf| |963 | pizza, pizza pie| |964 | potpie| |965 | burrito| |966 | red wine| |967 | espresso| |968 | cup| |969 | eggnog| |970 | alp| |971 | bubble| |972 | cliff, drop, drop-off| |973 | coral reef| |974 | geyser| |975 | lakeside, lakeshore| |976 | promontory, headland, head, foreland| |977 | sandbar, sand bar| |978 | seashore, coast, seacoast, sea-coast| |979 | valley, vale| |980 | volcano| |981 | ballplayer, baseball player| |982 | groom, bridegroom| |983 | scuba diver| |984 | rapeseed| |985 | daisy| |986 | yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum| |987 | corn| |988 | acorn| |989 | hip, rose hip, rosehip| |990 | buckeye, horse chestnut, conker| |991 | coral fungus| |992 | agaric| |993 | gyromitra| |994 | stinkhorn, carrion fungus| |995 | earthstar| |996 | hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa| |997 | bolete| |998 | ear, spike, capitulum| |999 | toilet tissue, toilet paper, bathroom tissue| </details> ### Data Splits | |train |validation| test | |-------------|------:|---------:|------:| |# of examples|1281167|50000 |100000 | ## Dataset Creation ### Curation Rationale The ImageNet project was inspired by two important needs in computer vision research. The first was the need to establish a clear North Star problem in computer vision. While the field enjoyed an abundance of important tasks to work on, from stereo vision to image retrieval, from 3D reconstruction to image segmentation, object categorization was recognized to be one of the most fundamental capabilities of both human and machine vision. Hence there was a growing demand for a high quality object categorization benchmark with clearly established evaluation metrics. Second, there was a critical need for more data to enable more generalizable machine learning methods. Ever since the birth of the digital era and the availability of web-scale data exchanges, researchers in these fields have been working hard to design more and more sophisticated algorithms to index, retrieve, organize and annotate multimedia data. But good research requires good resources. To tackle this problem at scale (think of your growing personal collection of digital images, or videos, or a commercial web search engine’s database), it was critical to provide researchers with a large-scale image database for both training and testing. The convergence of these two intellectual reasons motivated us to build ImageNet. ### Source Data #### Initial Data Collection and Normalization Initial data for ImageNet image classification task consists of photographs collected from [Flickr](https://www.flickr.com) and other search engines, manually labeled with the presence of one of 1000 object categories. Constructing ImageNet was an effort to scale up an image classification dataset to cover most nouns in English using tens of millions of manually verified photographs [1](https://ieeexplore.ieee.org/abstract/document/5206848). The image classification task of ILSVRC came as a direct extension of this effort. A subset of categories and images was chosen and fixed to provide a standardized benchmark while the rest of ImageNet continued to grow. #### Who are the source language producers? WordNet synsets further quality controlled by human annotators. The images are from Flickr. ### Annotations #### Annotation process The annotation process of collecting ImageNet for image classification task is a three step process. 1. Defining the 1000 object categories for the image classification task. These categories have evolved over the years. 1. Collecting the candidate image for these object categories using a search engine. 1. Quality control on the candidate images by using human annotators on Amazon Mechanical Turk (AMT) to make sure the image has the synset it was collected for. See the section 3.1 in [1](https://arxiv.org/abs/1409.0575) for more details on data collection procedure and [2](https://ieeexplore.ieee.org/abstract/document/5206848) for general information on ImageNet. #### Who are the annotators? Images are automatically fetched from an image search engine based on the synsets and filtered using human annotators on Amazon Mechanical Turk. See [1](https://arxiv.org/abs/1409.0575) for more details. ### Personal and Sensitive Information The 1,000 categories selected for this subset contain only 3 people categories (scuba diver, bridegroom, and baseball player) while the full ImageNet contains 2,832 people categories under the person subtree (accounting for roughly 8.3% of the total images). This subset does contain the images of people without their consent. Though, the study in [[1]](https://image-net.org/face-obfuscation/) on obfuscating faces of the people in the ImageNet 2012 subset shows that blurring people's faces causes a very minor decrease in accuracy (~0.6%) suggesting that privacy-aware models can be trained on ImageNet. On larger ImageNet, there has been [an attempt](https://arxiv.org/abs/1912.07726) at filtering and balancing the people subtree in the larger ImageNet. ## Considerations for Using the Data ### Social Impact of Dataset The ImageNet dataset has been very crucial in advancement of deep learning technology as being the standard benchmark for the computer vision models. The dataset aims to probe models on their understanding of the objects and has become the de-facto dataset for this purpose. ImageNet is still one of the major datasets on which models are evaluated for their generalization in computer vision capabilities as the field moves towards self-supervised algorithms. Please see the future section in [1](https://arxiv.org/abs/1409.0575) for a discussion on social impact of the dataset. ### Discussion of Biases 1. A [study](https://image-net.org/update-sep-17-2019.php) of the history of the multiple layers (taxonomy, object classes and labeling) of ImageNet and WordNet in 2019 described how bias is deeply embedded in most classification approaches for of all sorts of images. 1. A [study](https://arxiv.org/abs/1811.12231) has also shown that ImageNet trained models are biased towards texture rather than shapes which in contrast with how humans do object classification. Increasing the shape bias improves the accuracy and robustness. 1. Another [study](https://arxiv.org/abs/2109.13228) more potential issues and biases with the ImageNet dataset and provides an alternative benchmark for image classification task. The data collected contains humans without their consent. 1. ImageNet data with face obfuscation is also provided at [this link](https://image-net.org/face-obfuscation/) 1. A study on genealogy of ImageNet is can be found at [this link](https://journals.sagepub.com/doi/full/10.1177/20539517211035955) about the "norms, values, and assumptions" in ImageNet. 1. See [this study](https://arxiv.org/abs/1912.07726) on filtering and balancing the distribution of people subtree in the larger complete ImageNet. ### Other Known Limitations 1. Since most of the images were collected from internet, keep in mind that some images in ImageNet might be subject to copyrights. See the following papers for more details: [[1]](https://arxiv.org/abs/2109.13228) [[2]](https://arxiv.org/abs/1409.0575) [[3]](https://ieeexplore.ieee.org/abstract/document/5206848). ## Additional Information ### Dataset Curators Authors of [[1]](https://arxiv.org/abs/1409.0575) and [[2]](https://ieeexplore.ieee.org/abstract/document/5206848): - Olga Russakovsky - Jia Deng - Hao Su - Jonathan Krause - Sanjeev Satheesh - Wei Dong - Richard Socher - Li-Jia Li - Kai Li - Sean Ma - Zhiheng Huang - Andrej Karpathy - Aditya Khosla - Michael Bernstein - Alexander C Berg - Li Fei-Fei ### Licensing Information In exchange for permission to use the ImageNet database (the "Database") at Princeton University and Stanford University, Researcher hereby agrees to the following terms and conditions: 1. Researcher shall use the Database only for non-commercial research and educational purposes. 1. Princeton University and Stanford University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose. 1. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the ImageNet team, Princeton University, and Stanford University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the Database. 1. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions. 1. Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time. 1. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer. 1. The law of the State of New Jersey shall apply to all disputes under this agreement. ### Citation Information ```bibtex @article{imagenet15russakovsky, Author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei}, Title = { {ImageNet Large Scale Visual Recognition Challenge} }, Year = {2015}, journal = {International Journal of Computer Vision (IJCV)}, doi = {10.1007/s11263-015-0816-y}, volume={115}, number={3}, pages={211-252} } ``` ### Contributions Thanks to [@apsdehal](https://github.com/apsdehal) for adding this dataset.

yelp_review_full

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - other multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification pretty_name: YelpReviewFull license_details: yelp-licence dataset_info: features: - name: label dtype: class_label: names: '0': 1 star '1': 2 star '2': 3 stars '3': 4 stars '4': 5 stars - name: text dtype: string config_name: yelp_review_full splits: - name: train num_bytes: 483811554 num_examples: 650000 - name: test num_bytes: 37271188 num_examples: 50000 download_size: 196146755 dataset_size: 521082742 train-eval-index: - config: yelp_review_full task: text-classification task_id: multi_class_classification splits: train_split: train eval_split: test col_mapping: text: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- --- # Dataset Card for YelpReviewFull ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Yelp](https://www.yelp.com/dataset) - **Repository:** [Crepe](https://github.com/zhangxiangxiao/Crepe) - **Paper:** [Character-level Convolutional Networks for Text Classification](https://arxiv.org/abs/1509.01626) - **Point of Contact:** [Xiang Zhang](mailto:xiang.zhang@nyu.edu) ### Dataset Summary The Yelp reviews dataset consists of reviews from Yelp. It is extracted from the Yelp Dataset Challenge 2015 data. ### Supported Tasks and Leaderboards - `text-classification`, `sentiment-classification`: The dataset is mainly used for text classification: given the text, predict the sentiment. ### Languages The reviews were mainly written in english. ## Dataset Structure ### Data Instances A typical data point, comprises of a text and the corresponding label. An example from the YelpReviewFull test set looks as follows: ``` { 'label': 0, 'text': 'I got \'new\' tires from them and within two weeks got a flat. I took my car to a local mechanic to see if i could get the hole patched, but they said the reason I had a flat was because the previous patch had blown - WAIT, WHAT? I just got the tire and never needed to have it patched? This was supposed to be a new tire. \\nI took the tire over to Flynn\'s and they told me that someone punctured my tire, then tried to patch it. So there are resentful tire slashers? I find that very unlikely. After arguing with the guy and telling him that his logic was far fetched he said he\'d give me a new tire \\"this time\\". \\nI will never go back to Flynn\'s b/c of the way this guy treated me and the simple fact that they gave me a used tire!' } ``` ### Data Fields - 'text': The review texts are escaped using double quotes ("), and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is "\n". - 'label': Corresponds to the score associated with the review (between 1 and 5). ### Data Splits The Yelp reviews full star dataset is constructed by randomly taking 130,000 training samples and 10,000 testing samples for each review star from 1 to 5. In total there are 650,000 trainig samples and 50,000 testing samples. ## Dataset Creation ### Curation Rationale The Yelp reviews full star dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu) from the Yelp Dataset Challenge 2015. It is first used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015). ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information You can check the official [yelp-dataset-agreement](https://s3-media3.fl.yelpcdn.com/assets/srv0/engineering_pages/bea5c1e92bf3/assets/vendor/yelp-dataset-agreement.pdf). ### Citation Information Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015). ### Contributions Thanks to [@hfawaz](https://github.com/hfawaz) for adding this dataset.

MBZUAI/Bactrian-X

MBZUAI

--- license: cc-by-nc-4.0 task_categories: - text-generation language: - af - ar - az - bn - cs - de - en - es - et - fi - fr - gl - gu - he - hi - hr - id - it - ja - ka - kk - km - ko - lt - lv - mk - ml - mn - mr - my - ne - nl - pl - ps - pt - ro - ru - si - sl - sv - sw - ta - te - th - tl - tr - uk - ur - vi - xh - zh tags: - instruction-finetuning - multilingual pretty_name: Bactrian-X --- # Dataset Card for "Bactrian-X" ## Table of Contents - [Dataset Description](#a-dataset-description) - [Dataset Summary](#dataset-summary) - [Languages](#languages) - [Dataset Structure](#b-dataset-structure) - [Data Fields](#data-fields) - [Data Instances](#data-instances) - [Data in 52 Languages](#data-in-52-languages) - [Dataset Creation](#c-dataset-creation) - [Considerations for Using the Data](#d-considerations-for-using-the-data) - [Additional Information](#e-additional-information) ## A. Dataset Description - **Homepage:** https://github.com/mbzuai-nlp/Bactrian-X - **Repository:** https://huggingface.co/datasets/MBZUAI/Bactrian-X - **Paper:** to-be-soon released ### Dataset Summary <h3 align="center"> <img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/BactrianX_dataset.jpg" width="950" align="center"> </h3> The Bactrain-X dataset is a collection of 3.4M instruction-response pairs in 52 languages, that are obtained by translating 67K English instructions ([alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca) + [dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data)) into 51 languages using Google Translate API. The translated instructions are then fed to ChatGPT (`gpt-3.5-turbo`) to obtain its natural responses, resulting in 3.4M instruction-response pairs in 52 languages (52 languages x 67k instances = 3.4M instances). ### Languages We follow 52 languages of [mBART-50](https://arxiv.org/abs/2008.00401), with details can be found [below](#data-in-52-languages). ## B. Dataset Structure ### Data Fields The data fields are as follows: * `instruction`: describes the task the model should perform. Each of the 67K instructions is unique. * `input`: optional context or input for the task. For example, when the instruction is "Summarize the following article", the input is the article. Around 40% of the examples have an input. * `output`: the answer to the instruction as generated by `gpt-3.5-turbo`. * `id`: the unique index for each data piece in a specific language. The same `id` across splits corresponds to the same original English data. ### Data Instances An example in `en` looks as follows. ```json { "instruction": "What is the main idea of this text?", "input": "The robotics industry has become one of the fastest growing sectors in today’s economy. Robotics technology is being applied in various industries and applications, such as manufacturing, healthcare, and defense.", "output": "The main idea of the text is that the robotics industry is rapidly growing and its technology is being used in many different industries and applications.", "id": "alpaca-107" } ``` An example in `ar` looks as follows. ```json { "instruction": "قم بالوصول إلى إعدادات المتصفح الافتراضية على جهاز الكمبيوتر.", "input": "", "output": "لا يمكن لي كمساعد افتراضي القيام بهذه المهمة كونها تحتاج إلى الوصول إلى الجهاز المادي. يرجى الرجوع إلى دليل المستخدم للكمبيوتر الخاص بك أو البحث على الإنترنت لمعرفة كيفية الوصول إلى إعدادات المتصفح الافتراضية على جهاز الكمبيوتر الخاص بك.", "id": "alpaca-41997" } ``` An example in `id` looks as follows. ```json { "instruction": "Berapa banyak angka penting yang harus digunakan untuk nilai 0,000187?", "input": "", "output": "Ada tiga angka penting dalam nilai 0,000187.", "id": "alpaca-40382" } ``` An example in `de` looks as follows. ```json { "instruction": "Komponieren Sie ein Musikstück mit einem Titel, der Trauer bezeichnet.", "input": "", "output": ""Schmerzendes Herz"\n\nDas Stück beginnt mit einer langsamen, traurigen Melodie, gespielt von einem Solo-Violoncello. Die Harmonien, die von einem Klavier begleitet werden, sind durcheinander und dissonant, um ein Gefühl von Schmerz und Trauer zu vermitteln.\n\nIm Laufe des Stücks treten andere Instrumente ein, wie Streicher, Flöte und Oboe, um die Melodie zu unterstützen und zu intensivieren. Der Rhythmus bleibt langsam und schwerfällig, um die Schwere des Schmerzes darzustellen.\n\nIn der Mitte des Stücks gibt es einen Höhepunkt, wo die Melodie und Harmonien sich verdichten und ein Gefühl von Überwältigung erzeugen. Der Solo-Violoncello spielt eine traurige und schnelle Phrase, die die Verzweiflung und Isolation des Protagonisten ausdrückt.\n\nDas Stück endet mit einer Wiederholung der ersten Melodie, gespielt von einem Solo-Klavier, während die anderen Instrumente auslaufen. Die Harmonien werden jedoch weicher und sanfter, um einen Hauch von Hoffnung zu vermitteln, dass der Schmerz langsam vergeht.", "id": "alpaca-33619" } ``` An example in `fr` looks as follows. ```json { "instruction": "Les Ceanothus ont-ils des racines peu profondes ?", "input": "", "output": "En tant qu'IA, je ne peux pas répondre avec une certitude absolue, mais voici des informations générales sur les racines de Ceanothus : \n\nLes Ceanothus ont tendance à avoir un système racinaire étendu et peu profond. Les racines peuvent s'étaler jusqu'à 4 à 6 fois la taille de la plante et s'étendre à environ 60 à 90 cm de profondeur. Cependant, cela peut varier en fonction du type de sol et de la variété de Ceanothus. Certaines variétés de Ceanothus pourraient avoir des racines plus profondes si les conditions s'y prêtent.", "id": "dolly-10940" } ``` ### Data in 52 Languages | No | Languages | Code | Train | | ---|---------------- | ----- | ----- | | 1 | [Afrikaans](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/af) | af_ZA | 67017 | | 2 | [Arabic](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/ar) | ar_AR | 67017 | | 3 | [Azerbaijani](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/az) | az_AZ | 67017 | | 4 | [Bengali](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/bn) | bn_IN | 67017 | | 5 | [Czech](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/cs) | cs_CZ | 67017 | | 6 | [German](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/de) | de_DE | 67017 | | 7 | [English](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/en) | en_XX | 67017 | | 8 | [Spanish](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/es) | es_XX | 67017 | | 9 | [Estonian](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/et) | et_EE | 67017 | | 10 | [Persian](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/fa) | fa_IR | 67017 | | 11 | [Finnish](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/fi) | fi_FI | 67017 | | 12 | [French](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/fr) | fr_XX | 67017 | | 13 | [Galician](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/gl) | gl_ES | 67017 | | 14 | [Gujarati](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/gu) | gu_IN | 67017 | | 15 | [Hebrew](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/he) | he_IL | 67017 | | 16 | [Hindi](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/hi) | hi_IN | 67017 | | 17 | [Croatian](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/hr) | hr_HR | 67017 | | 18 | [Indonesian](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/id) | id_ID | 67017 | | 19 | [Italian](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/it) | it_IT | 67017 | | 20 | [Japanese](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/ja) | ja_XX | 67017 | | 21 | [Georgian](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/ka) | ka_GE | 67017 | | 22 | [Kazakh](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/kk) | kk_KZ | 67017 | | 23 | [Khmer](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/km) | km_KH | 67017 | | 24 | [Korean](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/ko) | ko_KR | 67017 | | 25 | [Lithuanian](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/lt) | lt_LT | 67017 | | 26 | [Latvian](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/lv) | lv_LV | 67017 | | 27 | [Macedonian](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/mk) | mk_MK | 67017 | | 28 | [Malayalam](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/ml) | ml_IN | 67017 | | 29 | [Mongolian](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/mn) | mn_MN | 67017 | | 30 | [Marathi](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/mr) | mr_IN | 67017 | | 31 | [Burmese](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/my) | my_MM | 67017 | | 32 | [Nepali](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/ne) | ne_NP | 67017 | | 33 | [Dutch](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/nl) | nl_XX | 67017 | | 34 | [Polish](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/pl) | pl_PL | 67017 | | 35 | [Pashto](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/ps) | ps_AF | 67017 | | 36 | [Portuguese](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/pt) | pt_XX | 67017 | | 37 | [Romanian](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/ro) | ro_RO | 67017 | | 38 | [Russian](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/ru) | ru_RU | 67017 | | 39 | [Sinhala](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/si) | si_LK | 67017 | | 40 | [Slovene](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/sl) | sl_SI | 67017 | | 41 | [Swedish](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/sv) | sv_SE | 67017 | | 42 | [Swahili](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/sw) | sw_KE | 67017 | | 43 | [Tamil](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/ta) | ta_IN | 67017 | | 44 | [Telugu](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/te) | te_IN | 67017 | | 45 | [Thai](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/th) | th_TH | 67017 | | 46 | [Tagalog](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/tl) | tl_XX | 67017 | | 47 | [Turkish](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/tr) | tr_TR | 67017 | | 48 | [Ukrainian](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/uk) | uk_UA | 67017 | | 49 | [Urdu](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/ur) | ur_PK | 67017 | | 50 | [Vietnamese](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/vi) | vi_VN | 67017 | | 51 | [Xhosa](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/xh) | xh_ZA | 67017 | | 52 | [Chinese](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/zh) | zh_CN | 67017 | ## C. Dataset Creation 1. English Instructions: The English instuctions are obtained from [alpaca-53k](https://github.com/tatsu-lab/stanford_alpaca), and [dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data). 2. Instruction Translation: The instructions (and inputs) are translated into 51 languages using Google Translation API (conducted on April 2023). 3. Output Generation: We generate output from `gpt-3.5-turbo` for each language (conducted on April 2023). ## D. Considerations for Using the Data ### Social Impact of Dataset NLP for everyone: this dataset helps to democratize the cutting-edge instruction-following models in 52 languages. This dataset also allows the first experiment on the multilingual LoRA-based LLaMA model. ### Discussion of Biases (1) Translation bias; (2) Potential English-culture bias in the translated dataset. ### Other Known Limitations The `Bactrian-X` data is generated by a language model (`gpt-3.5-turbo`) and inevitably contains some errors or biases. We encourage users to use this data with caution and propose new methods to filter or improve the imperfections. ## E. Additional Information ### Dataset Curators [Haonan Li](https://haonan-li.github.io/) and [Fajri Koto](http://www.fajrikoto.com) ### Licensing Information The dataset is available under the [Creative Commons NonCommercial (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/legalcode). ### Citation Information ``` @misc{li2023bactrianx, title={Bactrian-X : A Multilingual Replicable Instruction-Following Model with Low-Rank Adaptation}, author={Haonan Li and Fajri Koto and Minghao Wu and Alham Fikri Aji and Timothy Baldwin}, year={2023}, eprint={2305.15011}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@haonan-li](https://github.com/haonan-li), [@fajri91](https://github.com/fajri91) for adding this dataset.

sordonia/flan-10k-flat

sordonia

--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: task_name dtype: string - name: task_source dtype: string - name: template_type dtype: string - name: template_idx dtype: int64 - name: split dtype: string splits: - name: train num_bytes: 16815984887 num_examples: 10912677 download_size: 6978956537 dataset_size: 16815984887 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "flan-10k-flat" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

MMInstruction/M3IT

MMInstruction

--- license: other task_categories: - image-to-text - image-classification size_categories: - 1M<n<10M language: - en - zh --- # Dataset Card for M3IT Project Page: [M3IT](https://m3-it.github.io/) ## Dataset Description - **Homepage: https://huggingface.co/datasets/MMInstruction/M3IT** - **Repository: https://huggingface.co/datasets/MMInstruction/M3IT** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Languages English and Chinese. 80 translated version can be found at [M3IT-80](https://huggingface.co/datasets/MMInstruction/M3IT-80). ## Dataset Statistics Our dataset compiles diverse tasks of classical vision-language tasks, including captioning, visual question answering~(VQA), visual conditioned generation, reasoning and classification. ### Instruction Statistics | Task | #Instructions | |---------------------------|---------------| | Image Captioning | 52 | | Classification | 113 | | Visual Question Answering | 95 | | Knowledgeable Visual QA | 40 | | Reasoning | 60 | | Generation | 40 | | Total | 400 | ### Task Statistics | Task | Description | #Train | #Val | #Test | |---------------------------|-----------------------------------------------------------------|---------|---------|---------| | Image Captioning | Given an image, write a description for the image. | 679,087 | 41,462 | 27,499 | | Classification | Given an image, classify the image into pre-defined categories. | 238,303 | 100,069 | 21,206 | | Visual Question Answering | Given an image, answer a question relevant to the image. | 177,633 | 46,314 | 10,828 | | Knowledgeable Visual QA | Given an image, answer the question requires outside knowledge. | 39,981 | 11,682 | 5,477 | | Reasoning | Given an image, conduct reasoning over the images. | 99,372 | 11,500 | 10,000 | | Generation | Given an image, make compositions with certain requirements. | 145,000 | 11,315 | 17,350 | | Chinese | CAP, CLS, VQA, and GEN tasks in Chinese. | 192,076 | 77,306 | 4,100 | | Video | CAP, CLS, and VQA tasks on video-language datasets. | 20,868 | 7,542 | 9,294 | | Multi-lingual | Translated tasks in 80 languages | 0 | 240,000 | 184,000 | ### Detailed Dataset Statistics | Task | Dataset | #Train | #Val | #Test | |---------------------------|------------------------------|---------|--------|--------| | Image Captioning | `coco` | 566,747 | 25,010 | 25,010 | | | `textcap` | 97,765 | 13,965 | 0 | | | `image-paragraph-captioning` | 14,575 | 2,487 | 2,489 | | Classification | `coco-goi` | 30,000 | 2,000 | 0 | | | `coco-text` | 118,312 | 27,550 | 0 | | | `imagenet` | 30,000 | 50,000 | 0 | | | `coco-itm` | 30,000 | 5,000 | 5,000 | | | `snli-ve` | 20,000 | 14,339 | 14,740 | | | `mocheg` | 4,991 | 180 | 466 | | | `iqa` | 5,000 | 1,000 | 1,000 | | Visual Question Answering | `vqa-v2` | 30,000 | 30,000 | 0 | | | `shapes` | 13,568 | 1,024 | 1,024 | | | `docvqa` | 39,463 | 5,349 | 0 | | | `ocr-vqa` | 11,414 | 4,940 | 0 | | | `st-vqa` | 26,074 | 0 | 4,070 | | | `text-vqa` | 27,113 | 0 | 5,734 | | | `gqa` | 30,001 | 5,001 | 0 | | Knowledgeable Visual QA | `okvqa` | 9,009 | 5,046 | 0 | | | `a-okvqa` | 17,056 | 1,145 | 0 | | | `science-qa` | 12,726 | 4,241 | 4,241 | | | `viquae` | 1,190 | 1,250 | 1,236 | | Reasoning | `clevr` | 30,000 | 2,000 | 0 | | | `nlvr` | 29,372 | 2,000 | 0 | | | `vcr` | 25,000 | 5,000 | 5,000 | | | `visual-mrc` | 15,000 | 2,500 | 5,000 | | | `winoground` | 0 | 0 | 800 | | Generation | `vist` | 5,000 | 4,315 | 4,350 | | | `visual-dialog` | 50,000 | 1,000 | 1,000 | | | `multi30k` | 90,000 | 6,000 | 12,000 | | Chinese | `fm-iqa` | 164,735 | 75,206 | 0 | | | `coco-cn` | 18,341 | 1,000 | 1,000 | | | `flickr8k-cn` | 6,000 | 1,000 | 1,000 | | | `chinese-food` | 0 | 0 | 1,100 | | | `mmchat` | 3,000 | 1,000 | 1,000 | | Video | `ss` | 2,000 | 2,000 | 2,000 | | | `ivqa` | 5,994 | 2,000 | 2,000 | | | `msvd-qa` | 1,161 | 245 | 504 | | | `activitynet-qa` | 3,200 | 1,800 | 800 | | | `msrvtt` | 6,513 | 497 | 2,990 | | | `msrvtt-qa` | 2,000 | 1,000 | 1,000 | ## Dataset Structure ### HuggingFace Login (Optional) ```python # OR run huggingface-cli login from huggingface_hub import login hf_token = "hf_xxx" # TODO: set a valid HuggingFace access token for loading datasets/models login(token=hf_token) ``` ### Data Loading ```python from datasets import load_dataset ds_name = "coco" # change the dataset name here dataset = load_dataset("MMInstruction/M3IT", ds_name) ``` ### Data Splits ```python from datasets import load_dataset ds_name = "coco" # change the dataset name here dataset = load_dataset("MMInstruction/M3IT", ds_name) train_set = dataset["train"] validation_set = dataset["validation"] test_set = dataset["test"] ``` ### Data Instances ```python from datasets import load_dataset from io import BytesIO from base64 import b64decode from PIL import Image ds_name = "coco" # change the dataset name here dataset = load_dataset("MMInstruction/M3IT", ds_name) train_set = dataset["train"] for train_instance in train_set: instruction = train_instance["instruction"] # str inputs = train_instance["inputs"] # str outputs = train_instance["outputs"] # str image_base64_str_list = train_instance["image_base64_str"] # str (base64) image_0 = Image.open(BytesIO(b64decode(image_base64_str_list[0]))) ``` ### Data Fields ```python import datasets features = datasets.Features( { "instruction": datasets.Value("string"), "inputs": datasets.Value("string"), "image_base64_str": [datasets.Value("string")], "outputs": datasets.Value("string"), } ) ``` ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data | Task | Dataset [Citation] | Source | |---------------------------|----------------------------------|------------------------------------------------------------------------------------| | Image Captioning | `coco` [1] | [Source](https://cocodataset.org/#home) | | | `textcap` [2] | [Source](https://textvqa.org/textcaps/) | | | `image-paragraph-captioning` [3] | [Source](https://cs.stanford.edu/people/ranjaykrishna/im2p/index.html) | | Classification | `coco-goi` [1] | [Source](https://cocodataset.org/#home) | | | `coco-text` [4] | [Source](https://bgshih.github.io/cocotext/) | | | `imagenet` [5] | [Source](https://www.image-net.org/) | | | `coco-itm` [1] | [Source](https://cocodataset.org/#home) | | | `snli-ve` [6] | [Source](https://github.com/necla-ml/SNLI-VE) | | | `mocheg` [7] | [Source](https://github.com/VT-NLP/Mocheg) | | | `iqa` [8] | [Source](https://github.com/icbcbicc/IQA-Dataset) | | Visual Question Answering | `vqa-v2` [9] | [Source](https://visualqa.org/) | | | `shapes` [10] | [Source](https://github.com/ronghanghu/n2nmn) | | | `docvqa` [11] | [Source](https://www.docvqa.org/) | | | `ocr-vqa` [12] | [Source](https://ocr-vqa.github.io/) | | | `st-vqa` [13] | [Source](https://rrc.cvc.uab.es/?ch=11) | | | `text-vqa` [14] | [Source](https://textvqa.org/) | | | `gqa` [15] | [Source](https://cs.stanford.edu/people/dorarad/gqa/about.html) | | Knowledgeable Visual QA | `okvqa` [16] | [Source](https://okvqa.allenai.org/) | | | `a-okvqa` [17] | [Source](https://allenai.org/project/a-okvqa/home) | | | `science-qa` [18] | [Source](https://scienceqa.github.io/) | | | `viquae` [19] | [Source](https://github.com/PaulLerner/ViQuAE) | | Reasoning | `clevr` [20] | [Source](https://cs.stanford.edu/people/jcjohns/clevr/) | | | `nlvr` [21] | [Source](https://lil.nlp.cornell.edu/nlvr/) | | | `vcr` [22] | [Source](https://visualcommonsense.com/) | | | `visual-mrc` [23] | [Source](https://github.com/nttmdlab-nlp/VisualMRC) | | | `winoground` [24] | [Source](https://huggingface.co/datasets/facebook/winoground) | | Generation | `vist` [25] | [Source](https://visionandlanguage.net/VIST/) | | | `visual-dialog` [26] | [Source](https://visualdialog.org/) | | | `multi30k` [27] | [Source](https://github.com/multi30k/dataset) | | Chinese | `fm-iqa` [28] | [Source](https://paperswithcode.com/dataset/fm-iqa) | | | `coco-cn` [29] | [Source](https://github.com/li-xirong/coco-cn) | | | `flickr8k-cn` [30] | [Source](https://github.com/li-xirong/flickr8kcn) | | | `chinese-food` [31] | [Source](https://sites.google.com/view/chinesefoodnet) | | | `mmchat` [32] | [Source](https://github.com/silverriver/MMChat) | | Video | `ss` [33] | [Source](https://developer.qualcomm.com/software/ai-datasets/something-something) | | | `ivqa` [34] | [Source](https://antoyang.github.io/just-ask.html) | | | `msvd-qa` [35] | [Source](https://paperswithcode.com/dataset/msvd) | | | `activitynet-qa` [36] | [Source](https://github.com/MILVLG/activitynet-qa) | | | `msrvtt` [35] | [Source](https://paperswithcode.com/dataset/msr-vtt) | | | `msrvtt-qa` [37] | [Source](https://paperswithcode.com/sota/visual-question-answering-on-msrvtt-qa-1) | ### Annotations #### Annotation process To build high-quality multimodal instruction datasets, we rewrite various datasets into multimodal-to-text dialog format. The annotation process includes four steps: - (1) **Stage I: Instruction Writing**: writing instructions for each task; - (2) **Stage II: Data Format Unification**: structuring images and texts into a unified schema; - (3) **Stage III: Quality Check**: checking the overall dataset quality; - (4) **Stage IV: Key Datasets Translation**: building multilingual sets. #### Who are the annotators? Eight authors of this work are employed as human annotators, each of whom is a graduate student familiar with relevant literature. ## Additional Information ### Licensing Information The content of original dataset follows their original license. We suggest that for the task with Unknown/Custom license, the user can check the original project or contact the dataset owner for detailed license information. Our annotated instruction data is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). ### Citation Information ```bibtex @article{li2023m3it, title={M$^3$IT: A Large-Scale Dataset towards Multi-Modal Multilingual Instruction Tuning}, author={Lei Li and Yuwei Yin and Shicheng Li and Liang Chen and Peiyi Wang and Shuhuai Ren and Mukai Li and Yazheng Yang and Jingjing Xu and Xu Sun and Lingpeng Kong and Qi Liu}, journal={arXiv preprint arXiv:2306.04387}, year={2023} } ``` ### Contributions M3IT is an open-source, large-scale Multi-modal, Multilingual Instruction Tuning dataset, designed to enable the development of general-purpose multi-modal agents. ## References - [1] Microsoft COCO: Common Objects in Context - [2] TextCaps: a dataset for image captioning with reading comprehension - [3] A Hierarchical Approach for Generating Descriptive Image Paragraphs - [4] COCO-Text: Dataset and benchmark for text detection and recognition in natural images - [5] Imagenet large scale visual recognition challenge - [6] E-ViL: A Dataset and Benchmark for Natural Language Explanations in Vision-Language Tasks - [7] End-to-End Multimodal Fact-Checking and Explanation Generation: A Challenging Dataset and Models - [8] Quantifying visual image quality: A Bayesian view - [9] Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering - [10] Neural Module Networks - [11] DocVQA: A dataset for vqa on document images - [12] OCR-VQA: Visual Question Answering by Reading Text in Images - [13] Scene Text Visual Question Answering - [14] Towards VQA Models That Can Read - [15] GQA: A new dataset for real-world visual reasoning and compositional question answering - [16] OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge - [17] A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge - [18] Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering - [19] ViQuAE: a dataset for knowledge-based visual question answering about named entities - [20] CLEVR: A diagnostic dataset for compositional language and elementary visual reasoning - [21] A Corpus of Natural Language for Visual Reasoning - [22] From recognition to cognition: Visual Commonsense Reasoning - [23] VisualMRC: Machine reading comprehension on document images - [24] WinoGround: Probing vision and language models for visio-linguistic compositionality - [25] Visual Storytelling - [26] Visual Dialog - [27] Multi30k: Multilingual english-german image descriptions - [28] Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question - [29] COCO-CN for cross-lingual image tagging, captioning, and retrieval - [30] Adding Chinese Captions to Images - [31] ChineseFoodNet: A large-scale image dataset for chinese food recognition - [32] MMChat: Multi-Modal Chat Dataset on Social Media - [33] The "Something Something" Video Database for Learning and Evaluating Visual Common Sense - [34] Just Ask: Learning to answer questions from millions of narrated videos - [35] Video Question Answering via Gradually Refined Attention over Appearance and Motion - [36] ActivityNet-qa: A dataset for understanding complex web videos via question answering - [37] MSR-VTT: A large video description dataset for bridging video and language

mteb/stsbenchmark-sts

mteb

--- language: - en ---

mc4

--- pretty_name: mC4 annotations_creators: - no-annotation language_creators: - found language: - af - am - ar - az - be - bg - bn - ca - ceb - co - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fil - fr - fy - ga - gd - gl - gu - ha - haw - he - hi - hmn - ht - hu - hy - id - ig - is - it - iw - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lb - lo - lt - lv - mg - mi - mk - ml - mn - mr - ms - mt - my - ne - nl - 'no' - ny - pa - pl - ps - pt - ro - ru - sd - si - sk - sl - sm - sn - so - sq - sr - st - su - sv - sw - ta - te - tg - th - tr - uk - und - ur - uz - vi - xh - yi - yo - zh - zu language_bcp47: - bg-Latn - el-Latn - hi-Latn - ja-Latn - ru-Latn - zh-Latn license: - odc-by multilinguality: - multilingual size_categories: - n<1K - 1K<n<10K - 10K<n<100K - 100K<n<1M - 1M<n<10M - 10M<n<100M - 100M<n<1B - 1B<n<10B source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: mc4 --- # Dataset Card for mC4 ## Table of Contents - [Dataset Card for mC4](#dataset-card-for-mc4) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://huggingface.co/datasets/allenai/c4 - **Paper:** https://arxiv.org/abs/1910.10683 ### Dataset Summary A multilingual colossal, cleaned version of Common Crawl's web crawl corpus. Based on Common Crawl dataset: "https://commoncrawl.org". This is the version prepared by AllenAI, hosted at this address: https://huggingface.co/datasets/allenai/c4 108 languages are available and are reported in the table below. Note that the languages that end with "-Latn" are simply romanized variants, i.e. written using the Latin script. | language code | language name | |:----------------|:---------------------| | af | Afrikaans | | am | Amharic | | ar | Arabic | | az | Azerbaijani | | be | Belarusian | | bg | Bulgarian | | bg-Latn | Bulgarian (Latin) | | bn | Bangla | | ca | Catalan | | ceb | Cebuano | | co | Corsican | | cs | Czech | | cy | Welsh | | da | Danish | | de | German | | el | Greek | | el-Latn | Greek (Latin) | | en | English | | eo | Esperanto | | es | Spanish | | et | Estonian | | eu | Basque | | fa | Persian | | fi | Finnish | | fil | Filipino | | fr | French | | fy | Western Frisian | | ga | Irish | | gd | Scottish Gaelic | | gl | Galician | | gu | Gujarati | | ha | Hausa | | haw | Hawaiian | | hi | Hindi | | hi-Latn | Hindi (Latin script) | | hmn | Hmong, Mong | | ht | Haitian | | hu | Hungarian | | hy | Armenian | | id | Indonesian | | ig | Igbo | | is | Icelandic | | it | Italian | | iw | former Hebrew | | ja | Japanese | | ja-Latn | Japanese (Latin) | | jv | Javanese | | ka | Georgian | | kk | Kazakh | | km | Khmer | | kn | Kannada | | ko | Korean | | ku | Kurdish | | ky | Kyrgyz | | la | Latin | | lb | Luxembourgish | | lo | Lao | | lt | Lithuanian | | lv | Latvian | | mg | Malagasy | | mi | Maori | | mk | Macedonian | | ml | Malayalam | | mn | Mongolian | | mr | Marathi | | ms | Malay | | mt | Maltese | | my | Burmese | | ne | Nepali | | nl | Dutch | | no | Norwegian | | ny | Nyanja | | pa | Punjabi | | pl | Polish | | ps | Pashto | | pt | Portuguese | | ro | Romanian | | ru | Russian | | ru-Latn | Russian (Latin) | | sd | Sindhi | | si | Sinhala | | sk | Slovak | | sl | Slovenian | | sm | Samoan | | sn | Shona | | so | Somali | | sq | Albanian | | sr | Serbian | | st | Southern Sotho | | su | Sundanese | | sv | Swedish | | sw | Swahili | | ta | Tamil | | te | Telugu | | tg | Tajik | | th | Thai | | tr | Turkish | | uk | Ukrainian | | und | Unknown language | | ur | Urdu | | uz | Uzbek | | vi | Vietnamese | | xh | Xhosa | | yi | Yiddish | | yo | Yoruba | | zh | Chinese | | zh-Latn | Chinese (Latin) | | zu | Zulu | You can load the mC4 subset of any language like this: ```python from datasets import load_dataset en_mc4 = load_dataset("mc4", "en") ``` And if you can even specify a list of languages: ```python from datasets import load_dataset mc4_subset_with_five_languages = load_dataset("mc4", languages=["en", "fr", "es", "de", "zh"]) ``` ### Supported Tasks and Leaderboards mC4 is mainly intended to pretrain language models and word representations. ### Languages The dataset supports 108 languages. ## Dataset Structure ### Data Instances An example form the `en` config is: ``` {'timestamp': '2018-06-24T01:32:39Z', 'text': 'Farm Resources in Plumas County\nShow Beginning Farmer Organizations & Professionals (304)\nThere are 304 resources serving Plumas County in the following categories:\nMap of Beginning Farmer Organizations & Professionals serving Plumas County\nVictoria Fisher - Office Manager - Loyalton, CA\nAmy Lynn Rasband - UCCE Plumas-Sierra Administrative Assistant II - Quincy , CA\nShow Farm Income Opportunities Organizations & Professionals (353)\nThere are 353 resources serving Plumas County in the following categories:\nFarm Ranch And Forest Retailers (18)\nMap of Farm Income Opportunities Organizations & Professionals serving Plumas County\nWarner Valley Wildlife Area - Plumas County\nShow Farm Resources Organizations & Professionals (297)\nThere are 297 resources serving Plumas County in the following categories:\nMap of Farm Resources Organizations & Professionals serving Plumas County\nThere are 57 resources serving Plumas County in the following categories:\nMap of Organic Certification Organizations & Professionals serving Plumas County', 'url': 'http://www.californialandcan.org/Plumas/Farm-Resources/'} ``` ### Data Fields The data have several fields: - `url`: url of the source as a string - `text`: text content as a string - `timestamp`: timestamp as a string ### Data Splits To build mC4, the authors used [CLD3](https://github.com/google/cld3) to identify over 100 languages. The resulting mC4 subsets for each language are reported in this table: | config | train | validation | |:---------|:--------|:-------------| | af | ? | ? | | am | ? | ? | | ar | ? | ? | | az | ? | ? | | be | ? | ? | | bg | ? | ? | | bg-Latn | ? | ? | | bn | ? | ? | | ca | ? | ? | | ceb | ? | ? | | co | ? | ? | | cs | ? | ? | | cy | ? | ? | | da | ? | ? | | de | ? | ? | | el | ? | ? | | el-Latn | ? | ? | | en | ? | ? | | eo | ? | ? | | es | ? | ? | | et | ? | ? | | eu | ? | ? | | fa | ? | ? | | fi | ? | ? | | fil | ? | ? | | fr | ? | ? | | fy | ? | ? | | ga | ? | ? | | gd | ? | ? | | gl | ? | ? | | gu | ? | ? | | ha | ? | ? | | haw | ? | ? | | hi | ? | ? | | hi-Latn | ? | ? | | hmn | ? | ? | | ht | ? | ? | | hu | ? | ? | | hy | ? | ? | | id | ? | ? | | ig | ? | ? | | is | ? | ? | | it | ? | ? | | iw | ? | ? | | ja | ? | ? | | ja-Latn | ? | ? | | jv | ? | ? | | ka | ? | ? | | kk | ? | ? | | km | ? | ? | | kn | ? | ? | | ko | ? | ? | | ku | ? | ? | | ky | ? | ? | | la | ? | ? | | lb | ? | ? | | lo | ? | ? | | lt | ? | ? | | lv | ? | ? | | mg | ? | ? | | mi | ? | ? | | mk | ? | ? | | ml | ? | ? | | mn | ? | ? | | mr | ? | ? | | ms | ? | ? | | mt | ? | ? | | my | ? | ? | | ne | ? | ? | | nl | ? | ? | | no | ? | ? | | ny | ? | ? | | pa | ? | ? | | pl | ? | ? | | ps | ? | ? | | pt | ? | ? | | ro | ? | ? | | ru | ? | ? | | ru-Latn | ? | ? | | sd | ? | ? | | si | ? | ? | | sk | ? | ? | | sl | ? | ? | | sm | ? | ? | | sn | ? | ? | | so | ? | ? | | sq | ? | ? | | sr | ? | ? | | st | ? | ? | | su | ? | ? | | sv | ? | ? | | sw | ? | ? | | ta | ? | ? | | te | ? | ? | | tg | ? | ? | | th | ? | ? | | tr | ? | ? | | uk | ? | ? | | und | ? | ? | | ur | ? | ? | | uz | ? | ? | | vi | ? | ? | | xh | ? | ? | | yi | ? | ? | | yo | ? | ? | | zh | ? | ? | | zh-Latn | ? | ? | | zu | ? | ? | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information AllenAI are releasing this dataset under the terms of ODC-BY. By using this, you are also bound by the Common Crawl terms of use in respect of the content contained in the dataset. ### Citation Information ``` @article{2019t5, author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu}, title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer}, journal = {arXiv e-prints}, year = {2019}, archivePrefix = {arXiv}, eprint = {1910.10683}, } ``` ### Contributions Thanks to [@dirkgr](https://github.com/dirkgr) and [@lhoestq](https://github.com/lhoestq) for adding this dataset.

bookcorpus

--- annotations_creators: - no-annotation language_creators: - found language: - en license: - unknown multilinguality: - monolingual pretty_name: BookCorpus size_categories: - 10M<n<100M source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: bookcorpus dataset_info: features: - name: text dtype: string config_name: plain_text splits: - name: train num_bytes: 4853859824 num_examples: 74004228 download_size: 1179510242 dataset_size: 4853859824 --- # Dataset Card for BookCorpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://yknzhu.wixsite.com/mbweb](https://yknzhu.wixsite.com/mbweb) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 1.18 GB - **Size of the generated dataset:** 4.85 GB - **Total amount of disk used:** 6.03 GB ### Dataset Summary Books are a rich source of both fine-grained information, how a character, an object or a scene looks like, as well as high-level semantics, what someone is thinking, feeling and how these states evolve through a story.This work aims to align books to their movie releases in order to providerich descriptive explanations for visual content that go semantically farbeyond the captions available in current datasets. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### plain_text - **Size of downloaded dataset files:** 1.18 GB - **Size of the generated dataset:** 4.85 GB - **Total amount of disk used:** 6.03 GB An example of 'train' looks as follows. ``` { "text": "But I traded all my life for some lovin' and some gold" } ``` ### Data Fields The data fields are the same among all splits. #### plain_text - `text`: a `string` feature. ### Data Splits | name | train | |----------|-------:| |plain_text|74004228| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information The books have been crawled from https://www.smashwords.com, see their [terms of service](https://www.smashwords.com/about/tos) for more information. A data sheet for this dataset has also been created and published in [Addressing "Documentation Debt" in Machine Learning Research: A Retrospective Datasheet for BookCorpus](https://arxiv.org/abs/2105.05241). ### Citation Information ``` @InProceedings{Zhu_2015_ICCV, title = {Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books}, author = {Zhu, Yukun and Kiros, Ryan and Zemel, Rich and Salakhutdinov, Ruslan and Urtasun, Raquel and Torralba, Antonio and Fidler, Sanja}, booktitle = {The IEEE International Conference on Computer Vision (ICCV)}, month = {December}, year = {2015} } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@richarddwang](https://github.com/richarddwang), [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

librispeech_asr

--- pretty_name: LibriSpeech annotations_creators: - expert-generated language_creators: - crowdsourced - expert-generated language: - en license: - cc-by-4.0 multilinguality: - monolingual paperswithcode_id: librispeech-1 size_categories: - 100K<n<1M source_datasets: - original task_categories: - automatic-speech-recognition - audio-classification task_ids: - speaker-identification dataset_info: - config_name: clean features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string splits: - name: train.100 num_bytes: 6619683041 num_examples: 28539 - name: train.360 num_bytes: 23898214592 num_examples: 104014 - name: validation num_bytes: 359572231 num_examples: 2703 - name: test num_bytes: 367705423 num_examples: 2620 download_size: 30121377654 dataset_size: 31245175287 - config_name: other features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string splits: - name: train.500 num_bytes: 31810256902 num_examples: 148688 - name: validation num_bytes: 337283304 num_examples: 2864 - name: test num_bytes: 352396474 num_examples: 2939 download_size: 31236565377 dataset_size: 32499936680 - config_name: all features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string splits: - name: train.clean.100 num_bytes: 6627791685 num_examples: 28539 - name: train.clean.360 num_bytes: 23927767570 num_examples: 104014 - name: train.other.500 num_bytes: 31852502880 num_examples: 148688 - name: validation.clean num_bytes: 359505691 num_examples: 2703 - name: validation.other num_bytes: 337213112 num_examples: 2864 - name: test.clean num_bytes: 368449831 num_examples: 2620 - name: test.other num_bytes: 353231518 num_examples: 2939 download_size: 61357943031 dataset_size: 63826462287 --- # Dataset Card for librispeech_asr ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [LibriSpeech ASR corpus](http://www.openslr.org/12) - **Repository:** [Needs More Information] - **Paper:** [LibriSpeech: An ASR Corpus Based On Public Domain Audio Books](https://www.danielpovey.com/files/2015_icassp_librispeech.pdf) - **Leaderboard:** [The 🤗 Speech Bench](https://huggingface.co/spaces/huggingface/hf-speech-bench) - **Point of Contact:** [Daniel Povey](mailto:dpovey@gmail.com) ### Dataset Summary LibriSpeech is a corpus of approximately 1000 hours of 16kHz read English speech, prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read audiobooks from the LibriVox project, and has been carefully segmented and aligned. ### Supported Tasks and Leaderboards - `automatic-speech-recognition`, `audio-speaker-identification`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active Hugging Face leaderboard which can be found at https://huggingface.co/spaces/huggingface/hf-speech-bench. The leaderboard ranks models uploaded to the Hub based on their WER. An external leaderboard at https://paperswithcode.com/sota/speech-recognition-on-librispeech-test-clean ranks the latest models from research and academia. ### Languages The audio is in English. There are two configurations: `clean` and `other`. The speakers in the corpus were ranked according to the WER of the transcripts of a model trained on a different dataset, and were divided roughly in the middle, with the lower-WER speakers designated as "clean" and the higher WER speakers designated as "other". ## Dataset Structure ### Data Instances A typical data point comprises the path to the audio file, usually called `file` and its transcription, called `text`. Some additional information about the speaker and the passage which contains the transcription is provided. ``` {'chapter_id': 141231, 'file': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/dev_clean/1272/141231/1272-141231-0000.flac', 'audio': {'path': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/dev_clean/1272/141231/1272-141231-0000.flac', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 16000}, 'id': '1272-141231-0000', 'speaker_id': 1272, 'text': 'A MAN SAID TO THE UNIVERSE SIR I EXIST'} ``` ### Data Fields - file: A path to the downloaded audio file in .flac format. - audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`. - text: the transcription of the audio file. - id: unique id of the data sample. - speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples. - chapter_id: id of the audiobook chapter which includes the transcription. ### Data Splits The size of the corpus makes it impractical, or at least inconvenient for some users, to distribute it as a single large archive. Thus the training portion of the corpus is split into three subsets, with approximate size 100, 360 and 500 hours respectively. A simple automatic procedure was used to select the audio in the first two sets to be, on average, of higher recording quality and with accents closer to US English. An acoustic model was trained on WSJ’s si-84 data subset and was used to recognize the audio in the corpus, using a bigram LM estimated on the text of the respective books. We computed the Word Error Rate (WER) of this automatic transcript relative to our reference transcripts obtained from the book texts. The speakers in the corpus were ranked according to the WER of the WSJ model’s transcripts, and were divided roughly in the middle, with the lower-WER speakers designated as "clean" and the higher-WER speakers designated as "other". For "clean", the data is split into train, validation, and test set. The train set is further split into train.100 and train.360 respectively accounting for 100h and 360h of the training data. For "other", the data is split into train, validation, and test set. The train set contains approximately 500h of recorded speech. | | Train.500 | Train.360 | Train.100 | Valid | Test | | ----- | ------ | ----- | ---- | ---- | ---- | | clean | - | 104014 | 28539 | 2703 | 2620| | other | 148688 | - | - | 2864 | 2939 | ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators The dataset was initially created by Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur. ### Licensing Information [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) ### Citation Information ``` @inproceedings{panayotov2015librispeech, title={Librispeech: an ASR corpus based on public domain audio books}, author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev}, booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on}, pages={5206--5210}, year={2015}, organization={IEEE} } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

nq_open

--- annotations_creators: - expert-generated language_creators: - other language: - en license: - cc-by-sa-3.0 multilinguality: - monolingual pretty_name: NQ-Open size_categories: - 10K<n<100K source_datasets: - extended|natural_questions task_categories: - question-answering task_ids: - open-domain-qa paperswithcode_id: null dataset_info: features: - name: question dtype: string - name: answer sequence: string config_name: nq_open splits: - name: train num_bytes: 6651344 num_examples: 87925 - name: validation num_bytes: 313841 num_examples: 3610 download_size: 8913614 dataset_size: 6965185 --- # Dataset Card for nq_open ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://efficientqa.github.io/ - **Repository:** https://github.com/google-research-datasets/natural-questions/tree/master/nq_open - **Paper:** https://www.aclweb.org/anthology/P19-1612.pdf - **Leaderboard:** https://ai.google.com/research/NaturalQuestions/efficientqa - **Point of Contact:** [Mailing List](efficientqa@googlegroups.com) ### Dataset Summary The NQ-Open task, introduced by Lee et.al. 2019, is an open domain question answering benchmark that is derived from Natural Questions. The goal is to predict an English answer string for an input English question. All questions can be answered using the contents of English Wikipedia. ### Supported Tasks and Leaderboards Open Domain Question-Answering, EfficientQA Leaderboard: https://ai.google.com/research/NaturalQuestions/efficientqa ### Languages English (`en`) ## Dataset Structure ### Data Instances ``` { "question": "names of the metropolitan municipalities in south africa", "answer": [ "Mangaung Metropolitan Municipality", "Nelson Mandela Bay Metropolitan Municipality", "eThekwini Metropolitan Municipality", "City of Tshwane Metropolitan Municipality", "City of Johannesburg Metropolitan Municipality", "Buffalo City Metropolitan Municipality", "City of Ekurhuleni Metropolitan Municipality" ] } ``` ### Data Fields - `question` - Input open domain question. - `answer` - List of possible answers to the question ### Data Splits - Train : 87925 - validation : 1800 ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization Natural Questions contains question from aggregated queries to Google Search (Kwiatkowski et al., 2019). To gather an open version of this dataset, we only keep questions with short answers and discard the given evidence document. Answers with many tokens often resemble extractive snippets rather than canonical answers, so we discard answers with more than 5 tokens. #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases Evaluating on this diverse set of question-answer pairs is crucial, because all existing datasets have inherent biases that are problematic for open domain QA systems with learned retrieval. In the Natural Questions dataset the question askers do not already know the answer. This accurately reflects a distribution of genuine information-seeking questions. However, annotators must separately find correct answers, which requires assistance from automatic tools and can introduce a moderate bias towards results from the tool. ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information All of the Natural Questions data is released under the [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/) license. ### Citation Information ``` @article{doi:10.1162/tacl\_a\_00276, author = {Kwiatkowski, Tom and Palomaki, Jennimaria and Redfield, Olivia and Collins, Michael and Parikh, Ankur and Alberti, Chris and Epstein, Danielle and Polosukhin, Illia and Devlin, Jacob and Lee, Kenton and Toutanova, Kristina and Jones, Llion and Kelcey, Matthew and Chang, Ming-Wei and Dai, Andrew M. and Uszkoreit, Jakob and Le, Quoc and Petrov, Slav}, title = {Natural Questions: A Benchmark for Question Answering Research}, journal = {Transactions of the Association for Computational Linguistics}, volume = {7}, number = {}, pages = {453-466}, year = {2019}, doi = {10.1162/tacl\_a\_00276}, URL = { https://doi.org/10.1162/tacl_a_00276 }, eprint = { https://doi.org/10.1162/tacl_a_00276 }, abstract = { We present the Natural Questions corpus, a question answering data set. Questions consist of real anonymized, aggregated queries issued to the Google search engine. An annotator is presented with a question along with a Wikipedia page from the top 5 search results, and annotates a long answer (typically a paragraph) and a short answer (one or more entities) if present on the page, or marks null if no long/short answer is present. The public release consists of 307,373 training examples with single annotations; 7,830 examples with 5-way annotations for development data; and a further 7,842 examples with 5-way annotated sequestered as test data. We present experiments validating quality of the data. We also describe analysis of 25-way annotations on 302 examples, giving insights into human variability on the annotation task. We introduce robust metrics for the purposes of evaluating question answering systems; demonstrate high human upper bounds on these metrics; and establish baseline results using competitive methods drawn from related literature. } } @inproceedings{lee-etal-2019-latent, title = "Latent Retrieval for Weakly Supervised Open Domain Question Answering", author = "Lee, Kenton and Chang, Ming-Wei and Toutanova, Kristina", booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/P19-1612", doi = "10.18653/v1/P19-1612", pages = "6086--6096", abstract = "Recent work on open domain question answering (QA) assumes strong supervision of the supporting evidence and/or assumes a blackbox information retrieval (IR) system to retrieve evidence candidates. We argue that both are suboptimal, since gold evidence is not always available, and QA is fundamentally different from IR. We show for the first time that it is possible to jointly learn the retriever and reader from question-answer string pairs and without any IR system. In this setting, evidence retrieval from all of Wikipedia is treated as a latent variable. Since this is impractical to learn from scratch, we pre-train the retriever with an Inverse Cloze Task. We evaluate on open versions of five QA datasets. On datasets where the questioner already knows the answer, a traditional IR system such as BM25 is sufficient. On datasets where a user is genuinely seeking an answer, we show that learned retrieval is crucial, outperforming BM25 by up to 19 points in exact match.", } ``` ### Contributions Thanks to [@Nilanshrajput](https://github.com/Nilanshrajput) for adding this dataset.

mosaicml/dolly_hhrlhf

mosaicml

--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string splits: - name: train num_bytes: 43781455.002688624 num_examples: 59310 - name: test num_bytes: 4479286.805304853 num_examples: 5129 download_size: 24882010 dataset_size: 48260741.80799348 license: cc-by-sa-3.0 task_categories: - text-generation language: - en pretty_name: Dolly HH-RLHF --- # Dataset Card for "dolly_hhrlhf" This dataset is a combination of [Databrick's dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) dataset and a filtered subset of [Anthropic's HH-RLHF](https://huggingface.co/datasets/Anthropic/hh-rlhf). It also includes a test split, which was missing in the original `dolly` set. That test set is composed of 200 randomly selected samples from `dolly` + 4,929 of the test set samples from HH-RLHF which made it through the filtering process. The train set contains 59,310 samples; `15,014 - 200 = 14,814` from Dolly, and the remaining 44,496 from HH-RLHF. It is slightly larger than Alpaca, and in our experience of slightly higher quality, but is usable for commercial purposes so long as you follow the terms of the license. ## Filtering process As mentioned, the HH-RLHF data in this dataset is filtered. Specifically, we take the first turn of the convesation, then remove any samples where the assistant: - uses the word "human", "thank", or "sorry" - asks a question - uses a first person pronoun This leaves samples which look like instruction-following, as opposed to conversation. ## License/Attribution <!-- **Copyright (2023) MosaicML, Inc.** --> This dataset was developed at MosaicML (https://www.mosaicml.com) and its use is subject to the CC BY-SA 3.0 license. Certain categories of material in the dataset include materials from the following sources, licensed under the CC BY-SA 3.0 license: Wikipedia (various pages) - https://www.wikipedia.org/ Copyright © Wikipedia editors and contributors. Databricks (https://www.databricks.com) Copyright © Databricks When citing this dataset, please use the following: ``` @misc{mosaicml2023dolly_hhrlhf, author = {MosaicML}, title = {Dolly-HHRLHF Dataset}, year = {2023}, publisher = {HuggingFace Datasets}, howpublished = {https://huggingface.co/datasets/mosaicml/dolly_hhrlhf}, } ```

skt/kobest_v1

skt

--- pretty_name: KoBEST annotations_creators: - expert-generated language_creators: - expert-generated language: - ko license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original --- # Dataset Card for KoBEST ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** https://github.com/SKT-LSL/KoBEST_datarepo - **Paper:** - **Point of Contact:** https://github.com/SKT-LSL/KoBEST_datarepo/issues ### Dataset Summary KoBEST is a Korean benchmark suite consists of 5 natural language understanding tasks that requires advanced knowledge in Korean. ### Supported Tasks and Leaderboards Boolean Question Answering, Choice of Plausible Alternatives, Words-in-Context, HellaSwag, Sentiment Negation Recognition ### Languages `ko-KR` ## Dataset Structure ### Data Instances #### KB-BoolQ An example of a data point looks as follows. ``` {'paragraph': '두아 리파(Dua Lipa, 1995년 8월 22일 ~ )는 잉글랜드의 싱어송라이터, 모델이다. BBC 사운드 오브 2016 명단에 노미닛되었다. 싱글 "Be the One"가 영국 싱글 차트 9위까지 오르는 등 성과를 보여주었다.', 'question': '두아 리파는 영국인인가?', 'label': 1} ``` #### KB-COPA An example of a data point looks as follows. ``` {'premise': '물을 오래 끓였다.', 'question': '결과', 'alternative_1': '물의 양이 늘어났다.', 'alternative_2': '물의 양이 줄어들었다.', 'label': 1} ``` #### KB-WiC An example of a data point looks as follows. ``` {'word': '양분', 'context_1': '토양에 [양분]이 풍부하여 나무가 잘 자란다. ', 'context_2': '태아는 모체로부터 [양분]과 산소를 공급받게 된다.', 'label': 1} ``` #### KB-HellaSwag An example of a data point looks as follows. ``` {'context': '모자를 쓴 투수가 타자에게 온 힘을 다해 공을 던진다. 공이 타자에게 빠른 속도로 다가온다. 타자가 공을 배트로 친다. 배트에서 깡 소리가 난다. 공이 하늘 위로 날아간다.', 'ending_1': '외야수가 떨어지는 공을 글러브로 잡는다.', 'ending_2': '외야수가 공이 떨어질 위치에 자리를 잡는다.', 'ending_3': '심판이 아웃을 외친다.', 'ending_4': '외야수가 공을 따라 뛰기 시작한다.', 'label': 3} ``` #### KB-SentiNeg An example of a data point looks as follows. ``` {'sentence': '택배사 정말 마음에 듬', 'label': 1} ``` ### Data Fields ### KB-BoolQ + `paragraph`: a `string` feature + `question`: a `string` feature + `label`: a classification label, with possible values `False`(0) and `True`(1) ### KB-COPA + `premise`: a `string` feature + `question`: a `string` feature + `alternative_1`: a `string` feature + `alternative_2`: a `string` feature + `label`: an answer candidate label, with possible values `alternative_1`(0) and `alternative_2`(1) ### KB-WiC + `target_word`: a `string` feature + `context_1`: a `string` feature + `context_2`: a `string` feature + `label`: a classification label, with possible values `False`(0) and `True`(1) ### KB-HellaSwag + `target_word`: a `string` feature + `context_1`: a `string` feature + `context_2`: a `string` feature + `label`: a classification label, with possible values `False`(0) and `True`(1) ### KB-SentiNeg + `sentence`: a `string` feature + `label`: a classification label, with possible values `Negative`(0) and `Positive`(1) ### Data Splits #### KB-BoolQ + train: 3,665 + dev: 700 + test: 1,404 #### KB-COPA + train: 3,076 + dev: 1,000 + test: 1,000 #### KB-WiC + train: 3,318 + dev: 1,260 + test: 1,260 #### KB-HellaSwag + train: 3,665 + dev: 700 + test: 1,404 #### KB-SentiNeg + train: 3,649 + dev: 400 + test: 397 + test_originated: 397 (Corresponding training data where the test set is originated from.) ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information ``` @misc{https://doi.org/10.48550/arxiv.2204.04541, doi = {10.48550/ARXIV.2204.04541}, url = {https://arxiv.org/abs/2204.04541}, author = {Kim, Dohyeong and Jang, Myeongjun and Kwon, Deuk Sin and Davis, Eric}, title = {KOBEST: Korean Balanced Evaluation of Significant Tasks}, publisher = {arXiv}, year = {2022}, } ``` [More Information Needed] ### Contributions Thanks to [@MJ-Jang](https://github.com/MJ-Jang) for adding this dataset.

fashion_mnist

--- annotations_creators: - expert-generated language_creators: - found language: - en license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - image-classification task_ids: - multi-class-image-classification paperswithcode_id: fashion-mnist pretty_name: FashionMNIST dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': T - shirt / top '1': Trouser '2': Pullover '3': Dress '4': Coat '5': Sandal '6': Shirt '7': Sneaker '8': Bag '9': Ankle boot config_name: fashion_mnist splits: - name: train num_bytes: 31296655 num_examples: 60000 - name: test num_bytes: 5233818 num_examples: 10000 download_size: 30878645 dataset_size: 36530473 --- # Dataset Card for FashionMNIST ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [GitHub](https://github.com/zalandoresearch/fashion-mnist) - **Repository:** [GitHub](https://github.com/zalandoresearch/fashion-mnist) - **Paper:** [arXiv](https://arxiv.org/pdf/1708.07747.pdf) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. We intend Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits. ### Supported Tasks and Leaderboards - `image-classification`: The goal of this task is to classify a given image of Zalando's article into one of 10 classes. The leaderboard is available [here](https://paperswithcode.com/sota/image-classification-on-fashion-mnist). ### Languages [More Information Needed] ## Dataset Structure ### Data Instances A data point comprises an image and its label. ``` { 'image': <PIL.PngImagePlugin.PngImageFile image mode=L size=28x28 at 0x27601169DD8>, 'label': 9 } ``` ### Data Fields - `image`: A `PIL.Image.Image` object containing the 28x28 image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`. - `label`: an integer between 0 and 9 representing the classes with the following mapping: | Label | Description | | --- | --- | | 0 | T-shirt/top | | 1 | Trouser | | 2 | Pullover | | 3 | Dress | | 4 | Coat | | 5 | Sandal | | 6 | Shirt | | 7 | Sneaker | | 8 | Bag | | 9 | Ankle boot | ### Data Splits The data is split into training and test set. The training set contains 60,000 images and the test set 10,000 images. ## Dataset Creation ### Curation Rationale **From the arXiv paper:** The original MNIST dataset contains a lot of handwritten digits. Members of the AI/ML/Data Science community love this dataset and use it as a benchmark to validate their algorithms. In fact, MNIST is often the first dataset researchers try. "If it doesn't work on MNIST, it won't work at all", they said. "Well, if it does work on MNIST, it may still fail on others." Here are some good reasons: - MNIST is too easy. Convolutional nets can achieve 99.7% on MNIST. Classic machine learning algorithms can also achieve 97% easily. Check out our side-by-side benchmark for Fashion-MNIST vs. MNIST, and read "Most pairs of MNIST digits can be distinguished pretty well by just one pixel." - MNIST is overused. In this April 2017 Twitter thread, Google Brain research scientist and deep learning expert Ian Goodfellow calls for people to move away from MNIST. - MNIST can not represent modern CV tasks, as noted in this April 2017 Twitter thread, deep learning expert/Keras author François Chollet. ### Source Data #### Initial Data Collection and Normalization **From the arXiv paper:** Fashion-MNIST is based on the assortment on Zalando’s website. Every fashion product on Zalando has a set of pictures shot by professional photographers, demonstrating different aspects of the product, i.e. front and back looks, details, looks with model and in an outfit. The original picture has a light-gray background (hexadecimal color: #fdfdfd) and stored in 762 × 1000 JPEG format. For efficiently serving different frontend components, the original picture is resampled with multiple resolutions, e.g. large, medium, small, thumbnail and tiny. We use the front look thumbnail images of 70,000 unique products to build Fashion-MNIST. Those products come from different gender groups: men, women, kids and neutral. In particular, whitecolor products are not included in the dataset as they have low contrast to the background. The thumbnails (51 × 73) are then fed into the following conversion pipeline: 1. Converting the input to a PNG image. 2. Trimming any edges that are close to the color of the corner pixels. The “closeness” is defined by the distance within 5% of the maximum possible intensity in RGB space. 3. Resizing the longest edge of the image to 28 by subsampling the pixels, i.e. some rows and columns are skipped over. 4. Sharpening pixels using a Gaussian operator of the radius and standard deviation of 1.0, with increasing effect near outlines. 5. Extending the shortest edge to 28 and put the image to the center of the canvas. 6. Negating the intensities of the image. 7. Converting the image to 8-bit grayscale pixels. #### Who are the source language producers? **From the arXiv paper:** Every fashion product on Zalando has a set of pictures shot by professional photographers, demonstrating different aspects of the product, i.e. front and back looks, details, looks with model and in an outfit. ### Annotations #### Annotation process **From the arXiv paper:** For the class labels, they use the silhouette code of the product. The silhouette code is manually labeled by the in-house fashion experts and reviewed by a separate team at Zalando. Each product Zalando is the Europe’s largest online fashion platform. Each product contains only one silhouette code. #### Who are the annotators? **From the arXiv paper:** The silhouette code is manually labeled by the in-house fashion experts and reviewed by a separate team at Zalando. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Han Xiao and Kashif Rasul and Roland Vollgraf ### Licensing Information MIT Licence ### Citation Information ``` @article{DBLP:journals/corr/abs-1708-07747, author = {Han Xiao and Kashif Rasul and Roland Vollgraf}, title = {Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms}, journal = {CoRR}, volume = {abs/1708.07747}, year = {2017}, url = {http://arxiv.org/abs/1708.07747}, archivePrefix = {arXiv}, eprint = {1708.07747}, timestamp = {Mon, 13 Aug 2018 16:47:27 +0200}, biburl = {https://dblp.org/rec/bib/journals/corr/abs-1708-07747}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` ### Contributions Thanks to [@gchhablani](https://github.com/gchablani) for adding this dataset.

beans

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - mit multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - image-classification task_ids: - multi-class-image-classification pretty_name: Beans dataset_info: features: - name: image_file_path dtype: string - name: image dtype: image - name: labels dtype: class_label: names: '0': angular_leaf_spot '1': bean_rust '2': healthy splits: - name: train num_bytes: 382110 num_examples: 1034 - name: validation num_bytes: 49711 num_examples: 133 - name: test num_bytes: 46584 num_examples: 128 download_size: 180024906 dataset_size: 478405 --- # Dataset Card for Beans ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Beans Homepage](https://github.com/AI-Lab-Makerere/ibean/) - **Repository:** [AI-Lab-Makerere/ibean](https://github.com/AI-Lab-Makerere/ibean/) - **Paper:** N/A - **Leaderboard:** N/A - **Point of Contact:** N/A ### Dataset Summary Beans leaf dataset with images of diseased and health leaves. ### Supported Tasks and Leaderboards - `image-classification`: Based on a leaf image, the goal of this task is to predict the disease type (Angular Leaf Spot and Bean Rust), if any. ### Languages English ## Dataset Structure ### Data Instances A sample from the training set is provided below: ``` { 'image_file_path': '/root/.cache/huggingface/datasets/downloads/extracted/0aaa78294d4bf5114f58547e48d91b7826649919505379a167decb629aa92b0a/train/bean_rust/bean_rust_train.109.jpg', 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x500 at 0x16BAA72A4A8>, 'labels': 1 } ``` ### Data Fields The data instances have the following fields: - `image_file_path`: a `string` filepath to an image. - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`. - `labels`: an `int` classification label. Class Label Mappings: ```json { "angular_leaf_spot": 0, "bean_rust": 1, "healthy": 2, } ``` ### Data Splits | |train|validation|test| |-------------|----:|---------:|---:| |# of examples|1034 |133 |128 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @ONLINE {beansdata, author="Makerere AI Lab", title="Bean disease dataset", month="January", year="2020", url="https://github.com/AI-Lab-Makerere/ibean/" } ``` ### Contributions Thanks to [@nateraw](https://github.com/nateraw) for adding this dataset.

tatsu-lab/alpaca_eval

tatsu-lab

--- license: cc-by-nc-4.0 ---

mteb/sts17-crosslingual-sts

mteb

--- language: - ar - de - en - es - fr - it - nl - ko - tr ---

NLPCoreTeam/mmlu_ru

NLPCoreTeam

--- pretty_name: MMLU RU/EN language: - ru - en size_categories: - 10K<n<100K task_categories: - question-answering - multiple-choice task_ids: - multiple-choice-qa dataset_info: - config_name: abstract_algebra features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 2182 num_examples: 5 - name: val num_bytes: 5220 num_examples: 11 - name: test num_bytes: 50926 num_examples: 100 download_size: 5548198 dataset_size: 58328 - config_name: anatomy features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 2482 num_examples: 5 - name: val num_bytes: 8448 num_examples: 14 - name: test num_bytes: 91387 num_examples: 135 download_size: 5548198 dataset_size: 102317 - config_name: astronomy features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 6049 num_examples: 5 - name: val num_bytes: 14187 num_examples: 16 - name: test num_bytes: 130167 num_examples: 152 download_size: 5548198 dataset_size: 150403 - config_name: business_ethics features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 6197 num_examples: 5 - name: val num_bytes: 8963 num_examples: 11 - name: test num_bytes: 96566 num_examples: 100 download_size: 5548198 dataset_size: 111726 - config_name: clinical_knowledge features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 3236 num_examples: 5 - name: val num_bytes: 18684 num_examples: 29 - name: test num_bytes: 178043 num_examples: 265 download_size: 5548198 dataset_size: 199963 - config_name: college_biology features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 4232 num_examples: 5 - name: val num_bytes: 13521 num_examples: 16 - name: test num_bytes: 139322 num_examples: 144 download_size: 5548198 dataset_size: 157075 - config_name: college_chemistry features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 3533 num_examples: 5 - name: val num_bytes: 6157 num_examples: 8 - name: test num_bytes: 65540 num_examples: 100 download_size: 5548198 dataset_size: 75230 - config_name: college_computer_science features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 7513 num_examples: 5 - name: val num_bytes: 13341 num_examples: 11 - name: test num_bytes: 120578 num_examples: 100 download_size: 5548198 dataset_size: 141432 - config_name: college_mathematics features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 3841 num_examples: 5 - name: val num_bytes: 6835 num_examples: 11 - name: test num_bytes: 65110 num_examples: 100 download_size: 5548198 dataset_size: 75786 - config_name: college_medicine features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 4659 num_examples: 5 - name: val num_bytes: 22116 num_examples: 22 - name: test num_bytes: 235856 num_examples: 173 download_size: 5548198 dataset_size: 262631 - config_name: college_physics features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 3740 num_examples: 5 - name: val num_bytes: 9491 num_examples: 11 - name: test num_bytes: 81480 num_examples: 102 download_size: 5548198 dataset_size: 94711 - config_name: computer_security features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 3150 num_examples: 5 - name: val num_bytes: 12859 num_examples: 11 - name: test num_bytes: 77969 num_examples: 100 download_size: 5548198 dataset_size: 93978 - config_name: conceptual_physics features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 2611 num_examples: 5 - name: val num_bytes: 12480 num_examples: 26 - name: test num_bytes: 112243 num_examples: 235 download_size: 5548198 dataset_size: 127334 - config_name: econometrics features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 4548 num_examples: 5 - name: val num_bytes: 13874 num_examples: 12 - name: test num_bytes: 128633 num_examples: 114 download_size: 5548198 dataset_size: 147055 - config_name: electrical_engineering features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 2598 num_examples: 5 - name: val num_bytes: 8003 num_examples: 16 - name: test num_bytes: 70846 num_examples: 145 download_size: 5548198 dataset_size: 81447 - config_name: elementary_mathematics features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 3760 num_examples: 5 - name: val num_bytes: 23416 num_examples: 41 - name: test num_bytes: 181090 num_examples: 378 download_size: 5548198 dataset_size: 208266 - config_name: formal_logic features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 4715 num_examples: 5 - name: val num_bytes: 17099 num_examples: 14 - name: test num_bytes: 133930 num_examples: 126 download_size: 5548198 dataset_size: 155744 - config_name: global_facts features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 3450 num_examples: 5 - name: val num_bytes: 4971 num_examples: 10 - name: test num_bytes: 51481 num_examples: 100 download_size: 5548198 dataset_size: 59902 - config_name: high_school_biology features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 4759 num_examples: 5 - name: val num_bytes: 30807 num_examples: 32 - name: test num_bytes: 310356 num_examples: 310 download_size: 5548198 dataset_size: 345922 - config_name: high_school_chemistry features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 3204 num_examples: 5 - name: val num_bytes: 18948 num_examples: 22 - name: test num_bytes: 158246 num_examples: 203 download_size: 5548198 dataset_size: 180398 - config_name: high_school_computer_science features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 7933 num_examples: 5 - name: val num_bytes: 9612 num_examples: 9 - name: test num_bytes: 126403 num_examples: 100 download_size: 5548198 dataset_size: 143948 - config_name: high_school_european_history features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 32447 num_examples: 5 - name: val num_bytes: 83098 num_examples: 18 - name: test num_bytes: 754136 num_examples: 165 download_size: 5548198 dataset_size: 869681 - config_name: high_school_geography features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 4131 num_examples: 5 - name: val num_bytes: 12467 num_examples: 22 - name: test num_bytes: 119021 num_examples: 198 download_size: 5548198 dataset_size: 135619 - config_name: high_school_government_and_politics features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 5188 num_examples: 5 - name: val num_bytes: 20564 num_examples: 21 - name: test num_bytes: 194050 num_examples: 193 download_size: 5548198 dataset_size: 219802 - config_name: high_school_macroeconomics features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 3942 num_examples: 5 - name: val num_bytes: 37243 num_examples: 43 - name: test num_bytes: 340699 num_examples: 390 download_size: 5548198 dataset_size: 381884 - config_name: high_school_mathematics features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 3244 num_examples: 5 - name: val num_bytes: 14758 num_examples: 29 - name: test num_bytes: 140257 num_examples: 270 download_size: 5548198 dataset_size: 158259 - config_name: high_school_microeconomics features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 3503 num_examples: 5 - name: val num_bytes: 22212 num_examples: 26 - name: test num_bytes: 219097 num_examples: 238 download_size: 5548198 dataset_size: 244812 - config_name: high_school_physics features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 3905 num_examples: 5 - name: val num_bytes: 18535 num_examples: 17 - name: test num_bytes: 162917 num_examples: 151 download_size: 5548198 dataset_size: 185357 - config_name: high_school_psychology features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 5207 num_examples: 5 - name: val num_bytes: 49277 num_examples: 60 - name: test num_bytes: 455603 num_examples: 545 download_size: 5548198 dataset_size: 510087 - config_name: high_school_statistics features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 6823 num_examples: 5 - name: val num_bytes: 28020 num_examples: 23 - name: test num_bytes: 312578 num_examples: 216 download_size: 5548198 dataset_size: 347421 - config_name: high_school_us_history features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 25578 num_examples: 5 - name: val num_bytes: 91278 num_examples: 22 - name: test num_bytes: 842680 num_examples: 204 download_size: 5548198 dataset_size: 959536 - config_name: high_school_world_history features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 13893 num_examples: 5 - name: val num_bytes: 129121 num_examples: 26 - name: test num_bytes: 1068018 num_examples: 237 download_size: 5548198 dataset_size: 1211032 - config_name: human_aging features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 2820 num_examples: 5 - name: val num_bytes: 13442 num_examples: 23 - name: test num_bytes: 132242 num_examples: 223 download_size: 5548198 dataset_size: 148504 - config_name: human_sexuality features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 3072 num_examples: 5 - name: val num_bytes: 6699 num_examples: 12 - name: test num_bytes: 90007 num_examples: 131 download_size: 5548198 dataset_size: 99778 - config_name: international_law features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 6880 num_examples: 5 - name: val num_bytes: 19166 num_examples: 13 - name: test num_bytes: 157259 num_examples: 121 download_size: 5548198 dataset_size: 183305 - config_name: jurisprudence features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 3568 num_examples: 5 - name: val num_bytes: 10638 num_examples: 11 - name: test num_bytes: 97121 num_examples: 108 download_size: 5548198 dataset_size: 111327 - config_name: logical_fallacies features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 4526 num_examples: 5 - name: val num_bytes: 14547 num_examples: 18 - name: test num_bytes: 144501 num_examples: 163 download_size: 5548198 dataset_size: 163574 - config_name: machine_learning features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 6966 num_examples: 5 - name: val num_bytes: 8986 num_examples: 11 - name: test num_bytes: 95571 num_examples: 112 download_size: 5548198 dataset_size: 111523 - config_name: management features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 2427 num_examples: 5 - name: val num_bytes: 5210 num_examples: 11 - name: test num_bytes: 57201 num_examples: 103 download_size: 5548198 dataset_size: 64838 - config_name: marketing features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 4514 num_examples: 5 - name: val num_bytes: 20832 num_examples: 25 - name: test num_bytes: 181786 num_examples: 234 download_size: 5548198 dataset_size: 207132 - config_name: medical_genetics features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 3226 num_examples: 5 - name: val num_bytes: 8214 num_examples: 11 - name: test num_bytes: 57064 num_examples: 100 download_size: 5548198 dataset_size: 68504 - config_name: miscellaneous features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 1782 num_examples: 5 - name: val num_bytes: 39225 num_examples: 86 - name: test num_bytes: 407209 num_examples: 783 download_size: 5548198 dataset_size: 448216 - config_name: moral_disputes features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 4910 num_examples: 5 - name: val num_bytes: 36026 num_examples: 38 - name: test num_bytes: 313611 num_examples: 346 download_size: 5548198 dataset_size: 354547 - config_name: moral_scenarios features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 6175 num_examples: 5 - name: val num_bytes: 129062 num_examples: 100 - name: test num_bytes: 1137631 num_examples: 895 download_size: 5548198 dataset_size: 1272868 - config_name: nutrition features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 6030 num_examples: 5 - name: val num_bytes: 24210 num_examples: 33 - name: test num_bytes: 266173 num_examples: 306 download_size: 5548198 dataset_size: 296413 - config_name: philosophy features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 2631 num_examples: 5 - name: val num_bytes: 25751 num_examples: 34 - name: test num_bytes: 227086 num_examples: 311 download_size: 5548198 dataset_size: 255468 - config_name: prehistory features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 5394 num_examples: 5 - name: val num_bytes: 28687 num_examples: 35 - name: test num_bytes: 251723 num_examples: 324 download_size: 5548198 dataset_size: 285804 - config_name: professional_accounting features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 6277 num_examples: 5 - name: val num_bytes: 40914 num_examples: 31 - name: test num_bytes: 364528 num_examples: 282 download_size: 5548198 dataset_size: 411719 - config_name: professional_law features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 19120 num_examples: 5 - name: val num_bytes: 589307 num_examples: 170 - name: test num_bytes: 5479411 num_examples: 1534 download_size: 5548198 dataset_size: 6087838 - config_name: professional_medicine features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 10901 num_examples: 5 - name: val num_bytes: 69703 num_examples: 31 - name: test num_bytes: 633483 num_examples: 272 download_size: 5548198 dataset_size: 714087 - config_name: professional_psychology features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 6430 num_examples: 5 - name: val num_bytes: 82745 num_examples: 69 - name: test num_bytes: 648634 num_examples: 612 download_size: 5548198 dataset_size: 737809 - config_name: public_relations features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 4384 num_examples: 5 - name: val num_bytes: 13108 num_examples: 12 - name: test num_bytes: 82403 num_examples: 110 download_size: 5548198 dataset_size: 99895 - config_name: security_studies features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 16064 num_examples: 5 - name: val num_bytes: 67877 num_examples: 27 - name: test num_bytes: 611059 num_examples: 245 download_size: 5548198 dataset_size: 695000 - config_name: sociology features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 4693 num_examples: 5 - name: val num_bytes: 20654 num_examples: 22 - name: test num_bytes: 191420 num_examples: 201 download_size: 5548198 dataset_size: 216767 - config_name: us_foreign_policy features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 4781 num_examples: 5 - name: val num_bytes: 9171 num_examples: 11 - name: test num_bytes: 81649 num_examples: 100 download_size: 5548198 dataset_size: 95601 - config_name: virology features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 3063 num_examples: 5 - name: val num_bytes: 15618 num_examples: 18 - name: test num_bytes: 111027 num_examples: 166 download_size: 5548198 dataset_size: 129708 - config_name: world_religions features: - name: question_en dtype: string - name: choices_en sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question_ru dtype: string - name: choices_ru sequence: string splits: - name: dev num_bytes: 1691 num_examples: 5 - name: val num_bytes: 7052 num_examples: 19 - name: test num_bytes: 65559 num_examples: 171 download_size: 5548198 dataset_size: 74302 --- # MMLU in Russian (Massive Multitask Language Understanding) ## Overview of the Dataset MMLU dataset for EN/RU, without auxiliary train. The dataset contains `dev`/`val`/`test` splits for both, English and Russian languages. Note it doesn't include `auxiliary_train` split, which wasn't translated. Totally the dataset has ~16k samples per language: 285 `dev`, 1531 `val`, 14042 `test`. ## Description of original MMLU MMLU dataset covers 57 different tasks. Each task requires to choose the right answer out of four options for a given question. Paper "Measuring Massive Multitask Language Understanding": https://arxiv.org/abs/2009.03300v3. It is also known as the "hendrycks_test". ## Dataset Creation The translation was made via Yandex.Translate API. There are some translation mistakes, especially observed with terms and formulas, no fixes were applied. Initial dataset was taken from: https://people.eecs.berkeley.edu/~hendrycks/data.tar. ## Sample example ``` { "question_en": "Why doesn't Venus have seasons like Mars and Earth do?", "choices_en": [ "Its rotation axis is nearly perpendicular to the plane of the Solar System.", "It does not have an ozone layer.", "It does not rotate fast enough.", "It is too close to the Sun." ], "answer": 0, "question_ru": "Почему на Венере нет времен года, как на Марсе и Земле?", "choices_ru": [ "Ось его вращения почти перпендикулярна плоскости Солнечной системы.", "У него нет озонового слоя.", "Он вращается недостаточно быстро.", "Это слишком близко к Солнцу." ] } ``` ## Usage To merge all subsets into dataframe per split: ```python from collections import defaultdict import datasets import pandas as pd subjects = ["abstract_algebra", "anatomy", "astronomy", "business_ethics", "clinical_knowledge", "college_biology", "college_chemistry", "college_computer_science", "college_mathematics", "college_medicine", "college_physics", "computer_security", "conceptual_physics", "econometrics", "electrical_engineering", "elementary_mathematics", "formal_logic", "global_facts", "high_school_biology", "high_school_chemistry", "high_school_computer_science", "high_school_european_history", "high_school_geography", "high_school_government_and_politics", "high_school_macroeconomics", "high_school_mathematics", "high_school_microeconomics", "high_school_physics", "high_school_psychology", "high_school_statistics", "high_school_us_history", "high_school_world_history", "human_aging", "human_sexuality", "international_law", "jurisprudence", "logical_fallacies", "machine_learning", "management", "marketing", "medical_genetics", "miscellaneous", "moral_disputes", "moral_scenarios", "nutrition", "philosophy", "prehistory", "professional_accounting", "professional_law", "professional_medicine", "professional_psychology", "public_relations", "security_studies", "sociology", "us_foreign_policy", "virology", "world_religions"] splits = ["dev", "val", "test"] all_datasets = {x: datasets.load_dataset("NLPCoreTeam/mmlu_ru", name=x) for x in subjects} res = defaultdict(list) for subject in subjects: for split in splits: dataset = all_datasets[subject][split] df = dataset.to_pandas() int2str = dataset.features['answer'].int2str df['answer'] = df['answer'].map(int2str) df.insert(loc=0, column='subject_en', value=subject) res[split].append(df) res = {k: pd.concat(v) for k, v in res.items()} df_dev = res['dev'] df_val = res['val'] df_test = res['test'] ``` ## Evaluation This dataset is intended to evaluate LLMs with few-shot/zero-shot setup. Evaluation code: https://github.com/NLP-Core-Team/mmlu_ru Also resources might be helpful: 1. https://github.com/hendrycks/test 1. https://github.com/openai/evals/blob/main/examples/mmlu.ipynb 1. https://github.com/EleutherAI/lm-evaluation-harness/blob/master/lm_eval/tasks/hendrycks_test.py ## Contributions Dataset added by NLP core team RnD [Telegram channel](https://t.me/nlpcoreteam)

xcopa

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - et - ht - id - it - qu - sw - ta - th - tr - vi - zh license: - cc-by-4.0 multilinguality: - multilingual pretty_name: XCOPA size_categories: - unknown source_datasets: - extended|copa task_categories: - question-answering task_ids: - multiple-choice-qa paperswithcode_id: xcopa dataset_info: - config_name: et features: - name: premise dtype: string - name: choice1 dtype: string - name: choice2 dtype: string - name: question dtype: string - name: label dtype: int32 - name: idx dtype: int32 - name: changed dtype: bool splits: - name: validation num_bytes: 11711 num_examples: 100 - name: test num_bytes: 56613 num_examples: 500 download_size: 116432 dataset_size: 68324 - config_name: ht features: - name: premise dtype: string - name: choice1 dtype: string - name: choice2 dtype: string - name: question dtype: string - name: label dtype: int32 - name: idx dtype: int32 - name: changed dtype: bool splits: - name: validation num_bytes: 11999 num_examples: 100 - name: test num_bytes: 58579 num_examples: 500 download_size: 118677 dataset_size: 70578 - config_name: it features: - name: premise dtype: string - name: choice1 dtype: string - name: choice2 dtype: string - name: question dtype: string - name: label dtype: int32 - name: idx dtype: int32 - name: changed dtype: bool splits: - name: validation num_bytes: 13366 num_examples: 100 - name: test num_bytes: 65051 num_examples: 500 download_size: 126520 dataset_size: 78417 - config_name: id features: - name: premise dtype: string - name: choice1 dtype: string - name: choice2 dtype: string - name: question dtype: string - name: label dtype: int32 - name: idx dtype: int32 - name: changed dtype: bool splits: - name: validation num_bytes: 13897 num_examples: 100 - name: test num_bytes: 63331 num_examples: 500 download_size: 125347 dataset_size: 77228 - config_name: qu features: - name: premise dtype: string - name: choice1 dtype: string - name: choice2 dtype: string - name: question dtype: string - name: label dtype: int32 - name: idx dtype: int32 - name: changed dtype: bool splits: - name: validation num_bytes: 13983 num_examples: 100 - name: test num_bytes: 68711 num_examples: 500 download_size: 130786 dataset_size: 82694 - config_name: sw features: - name: premise dtype: string - name: choice1 dtype: string - name: choice2 dtype: string - name: question dtype: string - name: label dtype: int32 - name: idx dtype: int32 - name: changed dtype: bool splits: - name: validation num_bytes: 12708 num_examples: 100 - name: test num_bytes: 60675 num_examples: 500 download_size: 121497 dataset_size: 73383 - config_name: zh features: - name: premise dtype: string - name: choice1 dtype: string - name: choice2 dtype: string - name: question dtype: string - name: label dtype: int32 - name: idx dtype: int32 - name: changed dtype: bool splits: - name: validation num_bytes: 11646 num_examples: 100 - name: test num_bytes: 55276 num_examples: 500 download_size: 115021 dataset_size: 66922 - config_name: ta features: - name: premise dtype: string - name: choice1 dtype: string - name: choice2 dtype: string - name: question dtype: string - name: label dtype: int32 - name: idx dtype: int32 - name: changed dtype: bool splits: - name: validation num_bytes: 37037 num_examples: 100 - name: test num_bytes: 176254 num_examples: 500 download_size: 261404 dataset_size: 213291 - config_name: th features: - name: premise dtype: string - name: choice1 dtype: string - name: choice2 dtype: string - name: question dtype: string - name: label dtype: int32 - name: idx dtype: int32 - name: changed dtype: bool splits: - name: validation num_bytes: 21859 num_examples: 100 - name: test num_bytes: 104165 num_examples: 500 download_size: 174134 dataset_size: 126024 - config_name: tr features: - name: premise dtype: string - name: choice1 dtype: string - name: choice2 dtype: string - name: question dtype: string - name: label dtype: int32 - name: idx dtype: int32 - name: changed dtype: bool splits: - name: validation num_bytes: 11941 num_examples: 100 - name: test num_bytes: 57741 num_examples: 500 download_size: 117781 dataset_size: 69682 - config_name: vi features: - name: premise dtype: string - name: choice1 dtype: string - name: choice2 dtype: string - name: question dtype: string - name: label dtype: int32 - name: idx dtype: int32 - name: changed dtype: bool splits: - name: validation num_bytes: 15135 num_examples: 100 - name: test num_bytes: 70311 num_examples: 500 download_size: 133555 dataset_size: 85446 - config_name: translation-et features: - name: premise dtype: string - name: choice1 dtype: string - name: choice2 dtype: string - name: question dtype: string - name: label dtype: int32 - name: idx dtype: int32 - name: changed dtype: bool splits: - name: validation num_bytes: 11923 num_examples: 100 - name: test num_bytes: 57469 num_examples: 500 download_size: 116900 dataset_size: 69392 - config_name: translation-ht features: - name: premise dtype: string - name: choice1 dtype: string - name: choice2 dtype: string - name: question dtype: string - name: label dtype: int32 - name: idx dtype: int32 - name: changed dtype: bool splits: - name: validation num_bytes: 12172 num_examples: 100 - name: test num_bytes: 58161 num_examples: 500 download_size: 117847 dataset_size: 70333 - config_name: translation-it features: - name: premise dtype: string - name: choice1 dtype: string - name: choice2 dtype: string - name: question dtype: string - name: label dtype: int32 - name: idx dtype: int32 - name: changed dtype: bool splits: - name: validation num_bytes: 12424 num_examples: 100 - name: test num_bytes: 59078 num_examples: 500 download_size: 119605 dataset_size: 71502 - config_name: translation-id features: - name: premise dtype: string - name: choice1 dtype: string - name: choice2 dtype: string - name: question dtype: string - name: label dtype: int32 - name: idx dtype: int32 - name: changed dtype: bool splits: - name: validation num_bytes: 12499 num_examples: 100 - name: test num_bytes: 58548 num_examples: 500 download_size: 118566 dataset_size: 71047 - config_name: translation-sw features: - name: premise dtype: string - name: choice1 dtype: string - name: choice2 dtype: string - name: question dtype: string - name: label dtype: int32 - name: idx dtype: int32 - name: changed dtype: bool splits: - name: validation num_bytes: 12222 num_examples: 100 - name: test num_bytes: 58749 num_examples: 500 download_size: 118485 dataset_size: 70971 - config_name: translation-zh features: - name: premise dtype: string - name: choice1 dtype: string - name: choice2 dtype: string - name: question dtype: string - name: label dtype: int32 - name: idx dtype: int32 - name: changed dtype: bool splits: - name: validation num_bytes: 12043 num_examples: 100 - name: test num_bytes: 58037 num_examples: 500 download_size: 117582 dataset_size: 70080 - config_name: translation-ta features: - name: premise dtype: string - name: choice1 dtype: string - name: choice2 dtype: string - name: question dtype: string - name: label dtype: int32 - name: idx dtype: int32 - name: changed dtype: bool splits: - name: validation num_bytes: 12414 num_examples: 100 - name: test num_bytes: 59584 num_examples: 500 download_size: 119511 dataset_size: 71998 - config_name: translation-th features: - name: premise dtype: string - name: choice1 dtype: string - name: choice2 dtype: string - name: question dtype: string - name: label dtype: int32 - name: idx dtype: int32 - name: changed dtype: bool splits: - name: validation num_bytes: 11389 num_examples: 100 - name: test num_bytes: 54900 num_examples: 500 download_size: 113799 dataset_size: 66289 - config_name: translation-tr features: - name: premise dtype: string - name: choice1 dtype: string - name: choice2 dtype: string - name: question dtype: string - name: label dtype: int32 - name: idx dtype: int32 - name: changed dtype: bool splits: - name: validation num_bytes: 11921 num_examples: 100 - name: test num_bytes: 57741 num_examples: 500 download_size: 117161 dataset_size: 69662 - config_name: translation-vi features: - name: premise dtype: string - name: choice1 dtype: string - name: choice2 dtype: string - name: question dtype: string - name: label dtype: int32 - name: idx dtype: int32 - name: changed dtype: bool splits: - name: validation num_bytes: 11646 num_examples: 100 - name: test num_bytes: 55939 num_examples: 500 download_size: 115094 dataset_size: 67585 --- # Dataset Card for "xcopa" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://github.com/cambridgeltl/xcopa](https://github.com/cambridgeltl/xcopa) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 4.08 MB - **Size of the generated dataset:** 1.02 MB - **Total amount of disk used:** 5.10 MB ### Dataset Summary XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning The Cross-lingual Choice of Plausible Alternatives dataset is a benchmark to evaluate the ability of machine learning models to transfer commonsense reasoning across languages. The dataset is the translation and reannotation of the English COPA (Roemmele et al. 2011) and covers 11 languages from 11 families and several areas around the globe. The dataset is challenging as it requires both the command of world knowledge and the ability to generalise to new languages. All the details about the creation of XCOPA and the implementation of the baselines are available in the paper. Xcopa language et ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages - et - ht - id - it - qu - sw - ta - th - tr - vi - zh ## Dataset Structure ### Data Instances #### et - **Size of downloaded dataset files:** 0.37 MB - **Size of the generated dataset:** 0.07 MB - **Total amount of disk used:** 0.44 MB An example of 'validation' looks as follows. ``` { "changed": false, "choice1": "Ta kallas piima kaussi.", "choice2": "Ta kaotas oma isu.", "idx": 1, "label": 1, "premise": "Tüdruk leidis oma helveste seest putuka.", "question": "effect" } ``` #### ht - **Size of downloaded dataset files:** 0.37 MB - **Size of the generated dataset:** 0.07 MB - **Total amount of disk used:** 0.44 MB An example of 'validation' looks as follows. ``` { "changed": false, "choice1": "Ta kallas piima kaussi.", "choice2": "Ta kaotas oma isu.", "idx": 1, "label": 1, "premise": "Tüdruk leidis oma helveste seest putuka.", "question": "effect" } ``` #### id - **Size of downloaded dataset files:** 0.37 MB - **Size of the generated dataset:** 0.07 MB - **Total amount of disk used:** 0.45 MB An example of 'validation' looks as follows. ``` { "changed": false, "choice1": "Ta kallas piima kaussi.", "choice2": "Ta kaotas oma isu.", "idx": 1, "label": 1, "premise": "Tüdruk leidis oma helveste seest putuka.", "question": "effect" } ``` #### it - **Size of downloaded dataset files:** 0.37 MB - **Size of the generated dataset:** 0.08 MB - **Total amount of disk used:** 0.45 MB An example of 'validation' looks as follows. ``` { "changed": false, "choice1": "Ta kallas piima kaussi.", "choice2": "Ta kaotas oma isu.", "idx": 1, "label": 1, "premise": "Tüdruk leidis oma helveste seest putuka.", "question": "effect" } ``` #### qu - **Size of downloaded dataset files:** 0.37 MB - **Size of the generated dataset:** 0.08 MB - **Total amount of disk used:** 0.45 MB An example of 'validation' looks as follows. ``` { "changed": false, "choice1": "Ta kallas piima kaussi.", "choice2": "Ta kaotas oma isu.", "idx": 1, "label": 1, "premise": "Tüdruk leidis oma helveste seest putuka.", "question": "effect" } ``` ### Data Fields The data fields are the same among all splits. #### et - `premise`: a `string` feature. - `choice1`: a `string` feature. - `choice2`: a `string` feature. - `question`: a `string` feature. - `label`: a `int32` feature. - `idx`: a `int32` feature. - `changed`: a `bool` feature. #### ht - `premise`: a `string` feature. - `choice1`: a `string` feature. - `choice2`: a `string` feature. - `question`: a `string` feature. - `label`: a `int32` feature. - `idx`: a `int32` feature. - `changed`: a `bool` feature. #### id - `premise`: a `string` feature. - `choice1`: a `string` feature. - `choice2`: a `string` feature. - `question`: a `string` feature. - `label`: a `int32` feature. - `idx`: a `int32` feature. - `changed`: a `bool` feature. #### it - `premise`: a `string` feature. - `choice1`: a `string` feature. - `choice2`: a `string` feature. - `question`: a `string` feature. - `label`: a `int32` feature. - `idx`: a `int32` feature. - `changed`: a `bool` feature. #### qu - `premise`: a `string` feature. - `choice1`: a `string` feature. - `choice2`: a `string` feature. - `question`: a `string` feature. - `label`: a `int32` feature. - `idx`: a `int32` feature. - `changed`: a `bool` feature. ### Data Splits |name|validation|test| |----|---------:|---:| |et | 100| 500| |ht | 100| 500| |id | 100| 500| |it | 100| 500| |qu | 100| 500| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/). ### Citation Information ``` @article{ponti2020xcopa, title={{XCOPA: A} Multilingual Dataset for Causal Commonsense Reasoning}, author={Edoardo M. Ponti, Goran Glava {s}, Olga Majewska, Qianchu Liu, Ivan Vuli'{c} and Anna Korhonen}, journal={arXiv preprint}, year={2020}, url={https://ducdauge.github.io/files/xcopa.pdf} } @inproceedings{roemmele2011choice, title={Choice of plausible alternatives: An evaluation of commonsense causal reasoning}, author={Roemmele, Melissa and Bejan, Cosmin Adrian and Gordon, Andrew S}, booktitle={2011 AAAI Spring Symposium Series}, year={2011}, url={https://people.ict.usc.edu/~gordon/publications/AAAI-SPRING11A.PDF}, } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

Muennighoff/xwinograd

Muennighoff

--- language: - en - fr - ja - pt - ru - zh license: cc-by-4.0 --- ## XWinograd Multilingual winograd schema challenge as used in [Crosslingual Generalization through Multitask Finetuning](https://arxiv.org/abs/2211.01786). ### Languages & Samples - "en": 2325 - "fr": 83 - "jp": 959 - "pt": 263 - "ru": 315 - "zh": 504 ### Dataset creation The Winograd schema challenges in this dataset combine winograd schemas from the XWinograd dataset introduced in Tikhonov et al and as it only contains 16 Chinese schemas, we add 488 Chinese schemas from `clue/cluewsc2020`. If you only want the original xwinograd chinese schemas only, do: `load_dataset("Muennighoff/xwinograd", "zh")["test"][0][:16]` ## Additional Information ### Citation Information ```bibtex @misc{muennighoff2022crosslingual, title={Crosslingual Generalization through Multitask Finetuning}, author={Niklas Muennighoff and Thomas Wang and Lintang Sutawika and Adam Roberts and Stella Biderman and Teven Le Scao and M Saiful Bari and Sheng Shen and Zheng-Xin Yong and Hailey Schoelkopf and Xiangru Tang and Dragomir Radev and Alham Fikri Aji and Khalid Almubarak and Samuel Albanie and Zaid Alyafeai and Albert Webson and Edward Raff and Colin Raffel}, year={2022}, eprint={2211.01786}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ```bibtex @misc{tikhonov2021heads, title={It's All in the Heads: Using Attention Heads as a Baseline for Cross-Lingual Transfer in Commonsense Reasoning}, author={Alexey Tikhonov and Max Ryabinin}, year={2021}, eprint={2106.12066}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### License Like the original [English winograd schema challenge](https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html), this dataset is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). I.e. you can use it for commercial purposes etc. :) ### Contributions Thanks to Jordan Clive, @yongzx & @khalidalt for support on adding Chinese.

mteb/amazon_counterfactual

mteb

--- language: - de - en - ja --- # Amazon Multilingual Counterfactual Dataset The dataset contains sentences from Amazon customer reviews (sampled from Amazon product review dataset) annotated for counterfactual detection (CFD) binary classification. Counterfactual statements describe events that did not or cannot take place. Counterfactual statements may be identified as statements of the form – If p was true, then q would be true (i.e. assertions whose antecedent (p) and consequent (q) are known or assumed to be false). The key features of this dataset are: * The dataset is multilingual and contains sentences in English, German, and Japanese. * The labeling was done by professional linguists and high quality was ensured. * The dataset is supplemented with the annotation guidelines and definitions, which were worked out by professional linguists. We also provide the clue word lists, which are typical for counterfactual sentences and were used for initial data filtering. The clue word lists were also compiled by professional linguists. Please see the [paper](https://arxiv.org/abs/2104.06893) for the data statistics, detailed description of data collection and annotation. GitHub repo URL: https://github.com/amazon-research/amazon-multilingual-counterfactual-dataset ## Usage You can load each of the languages as follows: ``` from datasets import get_dataset_config_names dataset_id = "SetFit/amazon_counterfactual" # Returns ['de', 'en', 'en-ext', 'ja'] configs = get_dataset_config_names(dataset_id) # Load English subset dset = load_dataset(dataset_id, name="en") ```

mbpp

--- annotations_creators: - crowdsourced - expert-generated language_creators: - crowdsourced - expert-generated language: - en license: - cc-by-4.0 multilinguality: - monolingual pretty_name: Mostly Basic Python Problems size_categories: - n<1K source_datasets: - original task_categories: - text2text-generation task_ids: [] tags: - code-generation dataset_info: - config_name: full features: - name: task_id dtype: int32 - name: text dtype: string - name: code dtype: string - name: test_list sequence: string - name: test_setup_code dtype: string - name: challenge_test_list sequence: string splits: - name: train num_bytes: 176879 num_examples: 374 - name: test num_bytes: 244104 num_examples: 500 - name: validation num_bytes: 42405 num_examples: 90 - name: prompt num_bytes: 4550 num_examples: 10 download_size: 563743 dataset_size: 467938 - config_name: sanitized features: - name: source_file dtype: string - name: task_id dtype: int32 - name: prompt dtype: string - name: code dtype: string - name: test_imports sequence: string - name: test_list sequence: string splits: - name: train num_bytes: 63453 num_examples: 120 - name: test num_bytes: 132720 num_examples: 257 - name: validation num_bytes: 20050 num_examples: 43 - name: prompt num_bytes: 3407 num_examples: 7 download_size: 255053 dataset_size: 219630 --- # Dataset Card for Mostly Basic Python Problems (mbpp) ## Table of Contents - [Dataset Card for Mostly Basic Python Problems (mbpp)](#dataset-card-for-mostly-basic-python-problems-(mbpp)) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** https://github.com/google-research/google-research/tree/master/mbpp - **Paper:** [Program Synthesis with Large Language Models](https://arxiv.org/abs/2108.07732) ### Dataset Summary The benchmark consists of around 1,000 crowd-sourced Python programming problems, designed to be solvable by entry level programmers, covering programming fundamentals, standard library functionality, and so on. Each problem consists of a task description, code solution and 3 automated test cases. As described in the paper, a subset of the data has been hand-verified by us. Released [here](https://github.com/google-research/google-research/tree/master/mbpp) as part of [Program Synthesis with Large Language Models, Austin et. al., 2021](https://arxiv.org/abs/2108.07732). ### Supported Tasks and Leaderboards This dataset is used to evaluate code generations. ### Languages English - Python code ## Dataset Structure ```python dataset_full = load_dataset("mbpp") DatasetDict({ test: Dataset({ features: ['task_id', 'text', 'code', 'test_list', 'test_setup_code', 'challenge_test_list'], num_rows: 974 }) }) dataset_sanitized = load_dataset("mbpp", "sanitized") DatasetDict({ test: Dataset({ features: ['source_file', 'task_id', 'prompt', 'code', 'test_imports', 'test_list'], num_rows: 427 }) }) ``` ### Data Instances #### mbpp - full ``` { 'task_id': 1, 'text': 'Write a function to find the minimum cost path to reach (m, n) from (0, 0) for the given cost matrix cost[][] and a position (m, n) in cost[][].', 'code': 'R = 3\r\nC = 3\r\ndef min_cost(cost, m, n): \r\n\ttc = [[0 for x in range(C)] for x in range(R)] \r\n\ttc[0][0] = cost[0][0] \r\n\tfor i in range(1, m+1): \r\n\t\ttc[i][0] = tc[i-1][0] + cost[i][0] \r\n\tfor j in range(1, n+1): \r\n\t\ttc[0][j] = tc[0][j-1] + cost[0][j] \r\n\tfor i in range(1, m+1): \r\n\t\tfor j in range(1, n+1): \r\n\t\t\ttc[i][j] = min(tc[i-1][j-1], tc[i-1][j], tc[i][j-1]) + cost[i][j] \r\n\treturn tc[m][n]', 'test_list': [ 'assert min_cost([[1, 2, 3], [4, 8, 2], [1, 5, 3]], 2, 2) == 8', 'assert min_cost([[2, 3, 4], [5, 9, 3], [2, 6, 4]], 2, 2) == 12', 'assert min_cost([[3, 4, 5], [6, 10, 4], [3, 7, 5]], 2, 2) == 16'], 'test_setup_code': '', 'challenge_test_list': [] } ``` #### mbpp - sanitized ``` { 'source_file': 'Benchmark Questions Verification V2.ipynb', 'task_id': 2, 'prompt': 'Write a function to find the shared elements from the given two lists.', 'code': 'def similar_elements(test_tup1, test_tup2):\n res = tuple(set(test_tup1) & set(test_tup2))\n return (res) ', 'test_imports': [], 'test_list': [ 'assert set(similar_elements((3, 4, 5, 6),(5, 7, 4, 10))) == set((4, 5))', 'assert set(similar_elements((1, 2, 3, 4),(5, 4, 3, 7))) == set((3, 4))', 'assert set(similar_elements((11, 12, 14, 13),(17, 15, 14, 13))) == set((13, 14))' ] } ``` ### Data Fields - `source_file`: unknown - `text`/`prompt`: description of programming task - `code`: solution for programming task - `test_setup_code`/`test_imports`: necessary code imports to execute tests - `test_list`: list of tests to verify solution - `challenge_test_list`: list of more challenging test to further probe solution ### Data Splits There are two version of the dataset (full and sanitized), each with four splits: - train - evaluation - test - prompt The `prompt` split corresponds to samples used for few-shot prompting and not for training. ## Dataset Creation See section 2.1 of original [paper](https://arxiv.org/abs/2108.07732). ### Curation Rationale In order to evaluate code generation functions a set of simple programming tasks as well as solutions is necessary which this dataset provides. ### Source Data #### Initial Data Collection and Normalization The dataset was manually created from scratch. #### Who are the source language producers? The dataset was created with an internal crowdsourcing effort at Google. ### Annotations #### Annotation process The full dataset was created first and a subset then underwent a second round to improve the task descriptions. #### Who are the annotators? The dataset was created with an internal crowdsourcing effort at Google. ### Personal and Sensitive Information None. ## Considerations for Using the Data Make sure you execute generated Python code in a safe environment when evauating against this dataset as generated code could be harmful. ### Social Impact of Dataset With this dataset code generating models can be better evaluated which leads to fewer issues introduced when using such models. ### Discussion of Biases ### Other Known Limitations Since the task descriptions might not be expressive enough to solve the task. The `sanitized` split aims at addressing this issue by having a second round of annotators improve the dataset. ## Additional Information ### Dataset Curators Google Research ### Licensing Information CC-BY-4.0 ### Citation Information ``` @article{austin2021program, title={Program Synthesis with Large Language Models}, author={Austin, Jacob and Odena, Augustus and Nye, Maxwell and Bosma, Maarten and Michalewski, Henryk and Dohan, David and Jiang, Ellen and Cai, Carrie and Terry, Michael and Le, Quoc and others}, journal={arXiv preprint arXiv:2108.07732}, year={2021} ``` ### Contributions Thanks to [@lvwerra](https://github.com/lvwerra) for adding this dataset.

bigcode/humanevalpack

bigcode

--- license: mit pretty_name: HumanEvalPack language_creators: - expert-generated multilinguality: - multilingual language: - code tags: - code ---  # Dataset Card for HumanEvalPack ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** https://github.com/bigcode-project/octopack - **Paper:** [OctoPack: Instruction Tuning Code Large Language Models](https://arxiv.org/abs/2308.07124) - **Point of Contact:** [Niklas Muennighoff](mailto:n.muennighoff@gmail.com) ### Dataset Summary > HumanEvalPack is an extension of OpenAI's HumanEval to cover 6 total languages across 3 tasks. The Python split is exactly the same as OpenAI's Python HumanEval. The other splits are translated by humans (similar to HumanEval-X but with additional cleaning, see [here](https://github.com/bigcode-project/octopack/tree/main/evaluation/create/humaneval-x#modifications-muennighoff)). Refer to the [OctoPack paper](https://arxiv.org/abs/2308.07124) for more details. > - **Languages:** Python, JavaScript, Java, Go, C++, Rust - **OctoPack🐙🎒:** <table> <tr> <th>Data</t> <td><a href=https://huggingface.co/datasets/bigcode/commitpack>CommitPack</a></td> <td>4TB of GitHub commits across 350 programming languages</td> </tr> <tr> <th></t> <td><a href=https://huggingface.co/datasets/bigcode/commitpackft>CommitPackFT</a></td> <td>Filtered version of CommitPack for high-quality commit messages that resemble instructions</td> </tr> <tr> <th>Model</t> <td><a href=https://huggingface.co/bigcode/octocoder>OctoCoder</a></td> <td>StarCoder (16B parameters) instruction tuned on CommitPackFT + OASST</td> </tr> <tr> <th></t> <td><a href=https://huggingface.co/bigcode/octogeex>OctoGeeX</a></td> <td>CodeGeeX2 (6B parameters) instruction tuned on CommitPackFT + OASST</td> </tr> <tr> <th>Evaluation</t> <td><a href=https://huggingface.co/datasets/bigcode/humanevalpack>HumanEvalPack</a></td> <td>Extension of OpenAI's HumanEval to cover 3 scenarios across 6 languages</td> </tr> </table> ## Usage ```python # pip install -q datasets from datasets import load_dataset ds = load_dataset("bigcode/humanevalpack", "python")["test"] ds[0] ``` ## Dataset Structure ### Data Instances An example looks as follows: ```json { "task_id": "Python/0", "prompt": "from typing import List\n\n\ndef has_close_elements(numbers: List[float], threshold: float) -> bool:\n \"\"\" Check if in given list of numbers, are any two numbers closer to each other than\n given threshold.\n >>> has_close_elements([1.0, 2.0, 3.0], 0.5)\n False\n >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n True\n \"\"\"\n", "declaration": "from typing import List\n\n\ndef has_close_elements(numbers: List[float], threshold: float) -> bool:\n", "canonical_solution": " for idx, elem in enumerate(numbers):\n for idx2, elem2 in enumerate(numbers):\n if idx != idx2:\n distance = abs(elem - elem2)\n if distance < threshold:\n return True\n\n return False\n", "buggy_solution": " for idx, elem in enumerate(numbers):\n for idx2, elem2 in enumerate(numbers):\n if idx != idx2:\n distance = elem - elem2\n if distance < threshold:\n return True\n\n return False\n", "bug_type": "missing logic", "failure_symptoms": "incorrect output", "entry_point": "has_close_elements", "import": "" "test_setup": "" "test": "\n\n\n\n\ndef check(has_close_elements):\n assert has_close_elements([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.3) == True\n assert has_close_elements([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.05) == False\n assert has_close_elements([1.0, 2.0, 5.9, 4.0, 5.0], 0.95) == True\n assert has_close_elements([1.0, 2.0, 5.9, 4.0, 5.0], 0.8) == False\n assert has_close_elements([1.0, 2.0, 3.0, 4.0, 5.0, 2.0], 0.1) == True\n assert has_close_elements([1.1, 2.2, 3.1, 4.1, 5.1], 1.0) == True\n assert has_close_elements([1.1, 2.2, 3.1, 4.1, 5.1], 0.5) == False\n\ncheck(has_close_elements)", "example_test": "def check(has_close_elements):\n assert has_close_elements([1.0, 2.0, 3.0], 0.5) == False\n assert has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3) == True\ncheck(has_close_elements)\n", "signature": "has_close_elements(numbers: List[float], threshold: float) -> bool", "docstring": "Check if in given list of numbers, are any two numbers closer to each other than\ngiven threshold.\n>>> has_close_elements([1.0, 2.0, 3.0], 0.5)\nFalse\n>>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\nTrue", "instruction": "Write a Python function `has_close_elements(numbers: List[float], threshold: float) -> bool` to solve the following problem:\nCheck if in given list of numbers, are any two numbers closer to each other than\ngiven threshold.\n>>> has_close_elements([1.0, 2.0, 3.0], 0.5)\nFalse\n>>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\nTrue" } ``` ### Data Fields The data fields are the same among all splits: - `task_id`: Indicates the language (Python/JavaScript/Java/Go/C++/Rust) and task id (from 0 to 163) of the problem - `prompt`: the prompt for models relying on code continuation - `declaration`: the declaration of the function (same as prompt but without the docstring) - `canonical_solution`: the correct solution passing all unit tests for the problem - `buggy_solution`: same as `canonical_solution` but with a subtle human-written bug causing the unit tests to fail - `bug_type`: the type of the bug in `buggy_solution` (one of [`missing logic`, `excess logic`, `value misuse`, `operator misuse`, `variable misuse`, `function misuse`]) - `failure_symptoms`: the problem the bug causes (one of [`incorrect output`, `stackoverflow`, `infinite loop`]) - `entry_point`: the name of the function - 'import': imports necessary for the solution (only present for Go) - 'test_setup': imports necessary for the test execution (only present for Go) - `test`: the unit tests for the problem - `example_test`: additional unit tests different from `test` that could be e.g. provided to the model (these are not used in the paper) - `signature`: the signature of the function - `docstring`: the docstring describing the problem - `instruction`: an instruction for HumanEvalSynthesize in the form `Write a {language_name} function {signature} to solve the following problem:\n{docstring}` ## Citation Information ```bibtex @article{muennighoff2023octopack, title={OctoPack: Instruction Tuning Code Large Language Models}, author={Niklas Muennighoff and Qian Liu and Armel Zebaze and Qinkai Zheng and Binyuan Hui and Terry Yue Zhuo and Swayam Singh and Xiangru Tang and Leandro von Werra and Shayne Longpre}, journal={arXiv preprint arXiv:2308.07124}, year={2023} } ```

math_dataset

--- pretty_name: Mathematics Dataset language: - en paperswithcode_id: mathematics dataset_info: - config_name: algebra__linear_1d features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 516405 num_examples: 10000 - name: train num_bytes: 92086245 num_examples: 1999998 download_size: 2333082954 dataset_size: 92602650 - config_name: algebra__linear_1d_composed features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 1018090 num_examples: 10000 - name: train num_bytes: 199566926 num_examples: 1999998 download_size: 2333082954 dataset_size: 200585016 - config_name: algebra__linear_2d features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 666095 num_examples: 10000 - name: train num_bytes: 126743526 num_examples: 1999998 download_size: 2333082954 dataset_size: 127409621 - config_name: algebra__linear_2d_composed features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 1184664 num_examples: 10000 - name: train num_bytes: 234405885 num_examples: 1999998 download_size: 2333082954 dataset_size: 235590549 - config_name: algebra__polynomial_roots features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 868630 num_examples: 10000 - name: train num_bytes: 163134199 num_examples: 1999998 download_size: 2333082954 dataset_size: 164002829 - config_name: algebra__polynomial_roots_composed features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 1281321 num_examples: 10000 - name: train num_bytes: 251435312 num_examples: 1999998 download_size: 2333082954 dataset_size: 252716633 - config_name: algebra__sequence_next_term features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 752459 num_examples: 10000 - name: train num_bytes: 138735194 num_examples: 1999998 download_size: 2333082954 dataset_size: 139487653 - config_name: algebra__sequence_nth_term features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 947764 num_examples: 10000 - name: train num_bytes: 175945643 num_examples: 1999998 download_size: 2333082954 dataset_size: 176893407 - config_name: arithmetic__add_or_sub features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 483725 num_examples: 10000 - name: train num_bytes: 89690356 num_examples: 1999998 download_size: 2333082954 dataset_size: 90174081 - config_name: arithmetic__add_or_sub_in_base features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 502221 num_examples: 10000 - name: train num_bytes: 93779137 num_examples: 1999998 download_size: 2333082954 dataset_size: 94281358 - config_name: arithmetic__add_sub_multiple features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 498421 num_examples: 10000 - name: train num_bytes: 90962782 num_examples: 1999998 download_size: 2333082954 dataset_size: 91461203 - config_name: arithmetic__div features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 421520 num_examples: 10000 - name: train num_bytes: 78417908 num_examples: 1999998 download_size: 2333082954 dataset_size: 78839428 - config_name: arithmetic__mixed features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 513364 num_examples: 10000 - name: train num_bytes: 93989009 num_examples: 1999998 download_size: 2333082954 dataset_size: 94502373 - config_name: arithmetic__mul features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 394004 num_examples: 10000 - name: train num_bytes: 73499093 num_examples: 1999998 download_size: 2333082954 dataset_size: 73893097 - config_name: arithmetic__mul_div_multiple features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 497308 num_examples: 10000 - name: train num_bytes: 91406689 num_examples: 1999998 download_size: 2333082954 dataset_size: 91903997 - config_name: arithmetic__nearest_integer_root features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 705630 num_examples: 10000 - name: train num_bytes: 137771237 num_examples: 1999998 download_size: 2333082954 dataset_size: 138476867 - config_name: arithmetic__simplify_surd features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 1261753 num_examples: 10000 - name: train num_bytes: 207753790 num_examples: 1999998 download_size: 2333082954 dataset_size: 209015543 - config_name: calculus__differentiate features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 1025947 num_examples: 10000 - name: train num_bytes: 199013993 num_examples: 1999998 download_size: 2333082954 dataset_size: 200039940 - config_name: calculus__differentiate_composed features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 1343416 num_examples: 10000 - name: train num_bytes: 263757570 num_examples: 1999998 download_size: 2333082954 dataset_size: 265100986 - config_name: comparison__closest features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 681229 num_examples: 10000 - name: train num_bytes: 132274822 num_examples: 1999998 download_size: 2333082954 dataset_size: 132956051 - config_name: comparison__closest_composed features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 1071089 num_examples: 10000 - name: train num_bytes: 210658152 num_examples: 1999998 download_size: 2333082954 dataset_size: 211729241 - config_name: comparison__kth_biggest features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 797185 num_examples: 10000 - name: train num_bytes: 149077463 num_examples: 1999998 download_size: 2333082954 dataset_size: 149874648 - config_name: comparison__kth_biggest_composed features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 1144556 num_examples: 10000 - name: train num_bytes: 221547532 num_examples: 1999998 download_size: 2333082954 dataset_size: 222692088 - config_name: comparison__pair features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 452528 num_examples: 10000 - name: train num_bytes: 85707543 num_examples: 1999998 download_size: 2333082954 dataset_size: 86160071 - config_name: comparison__pair_composed features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 946187 num_examples: 10000 - name: train num_bytes: 184702998 num_examples: 1999998 download_size: 2333082954 dataset_size: 185649185 - config_name: comparison__sort features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 712498 num_examples: 10000 - name: train num_bytes: 131752705 num_examples: 1999998 download_size: 2333082954 dataset_size: 132465203 - config_name: comparison__sort_composed features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 1114257 num_examples: 10000 - name: train num_bytes: 213871896 num_examples: 1999998 download_size: 2333082954 dataset_size: 214986153 - config_name: measurement__conversion features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 592904 num_examples: 10000 - name: train num_bytes: 118650852 num_examples: 1999998 download_size: 2333082954 dataset_size: 119243756 - config_name: measurement__time features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 584278 num_examples: 10000 - name: train num_bytes: 116962599 num_examples: 1999998 download_size: 2333082954 dataset_size: 117546877 - config_name: numbers__base_conversion features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 490881 num_examples: 10000 - name: train num_bytes: 90363333 num_examples: 1999998 download_size: 2333082954 dataset_size: 90854214 - config_name: numbers__div_remainder features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 644523 num_examples: 10000 - name: train num_bytes: 125046212 num_examples: 1999998 download_size: 2333082954 dataset_size: 125690735 - config_name: numbers__div_remainder_composed features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 1151347 num_examples: 10000 - name: train num_bytes: 226341870 num_examples: 1999998 download_size: 2333082954 dataset_size: 227493217 - config_name: numbers__gcd features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 659492 num_examples: 10000 - name: train num_bytes: 127914889 num_examples: 1999998 download_size: 2333082954 dataset_size: 128574381 - config_name: numbers__gcd_composed features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 1206805 num_examples: 10000 - name: train num_bytes: 237534189 num_examples: 1999998 download_size: 2333082954 dataset_size: 238740994 - config_name: numbers__is_factor features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 396129 num_examples: 10000 - name: train num_bytes: 75875988 num_examples: 1999998 download_size: 2333082954 dataset_size: 76272117 - config_name: numbers__is_factor_composed features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 949828 num_examples: 10000 - name: train num_bytes: 185369842 num_examples: 1999998 download_size: 2333082954 dataset_size: 186319670 - config_name: numbers__is_prime features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 385749 num_examples: 10000 - name: train num_bytes: 73983639 num_examples: 1999998 download_size: 2333082954 dataset_size: 74369388 - config_name: numbers__is_prime_composed features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 947888 num_examples: 10000 - name: train num_bytes: 184808483 num_examples: 1999998 download_size: 2333082954 dataset_size: 185756371 - config_name: numbers__lcm features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 717978 num_examples: 10000 - name: train num_bytes: 136826050 num_examples: 1999998 download_size: 2333082954 dataset_size: 137544028 - config_name: numbers__lcm_composed features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 1127744 num_examples: 10000 - name: train num_bytes: 221148668 num_examples: 1999998 download_size: 2333082954 dataset_size: 222276412 - config_name: numbers__list_prime_factors features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 585749 num_examples: 10000 - name: train num_bytes: 109982816 num_examples: 1999998 download_size: 2333082954 dataset_size: 110568565 - config_name: numbers__list_prime_factors_composed features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 1053510 num_examples: 10000 - name: train num_bytes: 205379513 num_examples: 1999998 download_size: 2333082954 dataset_size: 206433023 - config_name: numbers__place_value features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 496977 num_examples: 10000 - name: train num_bytes: 95180091 num_examples: 1999998 download_size: 2333082954 dataset_size: 95677068 - config_name: numbers__place_value_composed features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 1011130 num_examples: 10000 - name: train num_bytes: 197187918 num_examples: 1999998 download_size: 2333082954 dataset_size: 198199048 - config_name: numbers__round_number features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 570636 num_examples: 10000 - name: train num_bytes: 111472483 num_examples: 1999998 download_size: 2333082954 dataset_size: 112043119 - config_name: numbers__round_number_composed features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 1016754 num_examples: 10000 - name: train num_bytes: 201057283 num_examples: 1999998 download_size: 2333082954 dataset_size: 202074037 - config_name: polynomials__add features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 1308455 num_examples: 10000 - name: train num_bytes: 257576092 num_examples: 1999998 download_size: 2333082954 dataset_size: 258884547 - config_name: polynomials__coefficient_named features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 1137226 num_examples: 10000 - name: train num_bytes: 219716251 num_examples: 1999998 download_size: 2333082954 dataset_size: 220853477 - config_name: polynomials__collect features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 774709 num_examples: 10000 - name: train num_bytes: 143743260 num_examples: 1999998 download_size: 2333082954 dataset_size: 144517969 - config_name: polynomials__compose features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 1209763 num_examples: 10000 - name: train num_bytes: 233651887 num_examples: 1999998 download_size: 2333082954 dataset_size: 234861650 - config_name: polynomials__evaluate features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 599446 num_examples: 10000 - name: train num_bytes: 114538250 num_examples: 1999998 download_size: 2333082954 dataset_size: 115137696 - config_name: polynomials__evaluate_composed features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 1148362 num_examples: 10000 - name: train num_bytes: 226022455 num_examples: 1999998 download_size: 2333082954 dataset_size: 227170817 - config_name: polynomials__expand features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 1057353 num_examples: 10000 - name: train num_bytes: 202338235 num_examples: 1999998 download_size: 2333082954 dataset_size: 203395588 - config_name: polynomials__simplify_power features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 1248040 num_examples: 10000 - name: train num_bytes: 216407582 num_examples: 1999998 download_size: 2333082954 dataset_size: 217655622 - config_name: probability__swr_p_level_set features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 1159050 num_examples: 10000 - name: train num_bytes: 227540179 num_examples: 1999998 download_size: 2333082954 dataset_size: 228699229 - config_name: probability__swr_p_sequence features: - name: question dtype: string - name: answer dtype: string splits: - name: test num_bytes: 1097442 num_examples: 10000 - name: train num_bytes: 215865725 num_examples: 1999998 download_size: 2333082954 dataset_size: 216963167 --- # Dataset Card for "math_dataset" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://github.com/deepmind/mathematics_dataset](https://github.com/deepmind/mathematics_dataset) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 130.65 GB - **Size of the generated dataset:** 9.08 GB - **Total amount of disk used:** 139.73 GB ### Dataset Summary Mathematics database. This dataset code generates mathematical question and answer pairs, from a range of question types at roughly school-level difficulty. This is designed to test the mathematical learning and algebraic reasoning skills of learning models. Original paper: Analysing Mathematical Reasoning Abilities of Neural Models (Saxton, Grefenstette, Hill, Kohli). Example usage: train_examples, val_examples = datasets.load_dataset( 'math_dataset/arithmetic__mul', split=['train', 'test'], as_supervised=True) ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### algebra__linear_1d - **Size of downloaded dataset files:** 2.33 GB - **Size of the generated dataset:** 92.60 MB - **Total amount of disk used:** 2.43 GB An example of 'train' looks as follows. ``` ``` #### algebra__linear_1d_composed - **Size of downloaded dataset files:** 2.33 GB - **Size of the generated dataset:** 200.58 MB - **Total amount of disk used:** 2.53 GB An example of 'train' looks as follows. ``` ``` #### algebra__linear_2d - **Size of downloaded dataset files:** 2.33 GB - **Size of the generated dataset:** 127.41 MB - **Total amount of disk used:** 2.46 GB An example of 'train' looks as follows. ``` ``` #### algebra__linear_2d_composed - **Size of downloaded dataset files:** 2.33 GB - **Size of the generated dataset:** 235.59 MB - **Total amount of disk used:** 2.57 GB An example of 'train' looks as follows. ``` ``` #### algebra__polynomial_roots - **Size of downloaded dataset files:** 2.33 GB - **Size of the generated dataset:** 164.01 MB - **Total amount of disk used:** 2.50 GB An example of 'train' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### algebra__linear_1d - `question`: a `string` feature. - `answer`: a `string` feature. #### algebra__linear_1d_composed - `question`: a `string` feature. - `answer`: a `string` feature. #### algebra__linear_2d - `question`: a `string` feature. - `answer`: a `string` feature. #### algebra__linear_2d_composed - `question`: a `string` feature. - `answer`: a `string` feature. #### algebra__polynomial_roots - `question`: a `string` feature. - `answer`: a `string` feature. ### Data Splits | name | train |test | |---------------------------|------:|----:| |algebra__linear_1d |1999998|10000| |algebra__linear_1d_composed|1999998|10000| |algebra__linear_2d |1999998|10000| |algebra__linear_2d_composed|1999998|10000| |algebra__polynomial_roots |1999998|10000| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @article{2019arXiv, author = {Saxton, Grefenstette, Hill, Kohli}, title = {Analysing Mathematical Reasoning Abilities of Neural Models}, year = {2019}, journal = {arXiv:1904.01557} } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

bigcode/starcoderdata

bigcode

--- annotations_creators: [] language_creators: - crowdsourced - expert-generated language: - code license: - other multilinguality: - multilingual pretty_name: The-Stack size_categories: - unknown source_datasets: [] task_categories: - text-generation extra_gated_prompt: >- ## Terms of Use for The Stack The Stack dataset is a collection of source code in over 300 programming languages. We ask that you read and acknowledge the following points before using the dataset: 1. The Stack is a collection of source code from repositories with various licenses. Any use of all or part of the code gathered in The Stack must abide by the terms of the original licenses, including attribution clauses when relevant. We facilitate this by providing provenance information for each data point. 2. The Stack is regularly updated to enact validated data removal requests. By clicking on "Access repository", you agree to update your own version of The Stack to the most recent usable version specified by the maintainers in [the following thread](https://huggingface.co/datasets/bigcode/the-stack/discussions/7). If you have questions about dataset versions and allowed uses, please also ask them in the dataset’s [community discussions](https://huggingface.co/datasets/bigcode/the-stack/discussions/new). We will also notify users via email when the latest usable version changes. 3. To host, share, or otherwise provide access to The Stack dataset, you must include [these Terms of Use](https://huggingface.co/datasets/bigcode/the-stack#terms-of-use-for-the-stack) and require users to agree to it. By clicking on "Access repository" below, you accept that your contact information (email address and username) can be shared with the dataset maintainers as well. extra_gated_fields: Email: text I have read the License and agree with its terms: checkbox --- # StarCoder Training Dataset ## Dataset description This is the dataset used for training [StarCoder](https://huggingface.co/bigcode/starcoder) and [StarCoderBase](https://huggingface.co/bigcode/starcoderbase). It contains 783GB of code in 86 programming languages, and includes 54GB GitHub Issues + 13GB Jupyter notebooks in scripts and text-code pairs, and 32GB of GitHub commits, which is approximately 250 Billion tokens. ## Dataset creation The creation and filtering of The Stack is explained in the [original dataset](https://huggingface.co/datasets/bigcode/the-stack-dedup), we additionally decontaminate and clean all 86 programming languages in the dataset, in addition to GitHub issues, Jupyter Notebooks and GitHub commits. We also apply near-deduplication and remove PII, all details are mentionned in our [Paper: 💫 StarCoder, May The Source Be With You](https://drive.google.com/file/d/1cN-b9GnWtHzQRoE7M7gAEyivY0kl4BYs/view) ## How to use the dataset ```python from datasets import load_dataset # to load python for example ds = load_dataset("bigcode/starcoderdata", data_dir="python", split="train") ``` GitHub issues, GitHub commits and Jupyter notebooks subsets have different columns from the rest so loading the entire dataset at once may fail, we suggest loading programming languages separatly from these categories. ```` jupyter-scripts-dedup-filtered jupyter-structured-clean-dedup github-issues-filtered-structured git-commits-cleaned ````

google/fleurs

google

--- annotations_creators: - expert-generated - crowdsourced - machine-generated language_creators: - crowdsourced - expert-generated language: - afr - amh - ara - asm - ast - azj - bel - ben - bos - cat - ceb - cmn - ces - cym - dan - deu - ell - eng - spa - est - fas - ful - fin - tgl - fra - gle - glg - guj - hau - heb - hin - hrv - hun - hye - ind - ibo - isl - ita - jpn - jav - kat - kam - kea - kaz - khm - kan - kor - ckb - kir - ltz - lug - lin - lao - lit - luo - lav - mri - mkd - mal - mon - mar - msa - mlt - mya - nob - npi - nld - nso - nya - oci - orm - ory - pan - pol - pus - por - ron - rus - bul - snd - slk - slv - sna - som - srp - swe - swh - tam - tel - tgk - tha - tur - ukr - umb - urd - uzb - vie - wol - xho - yor - yue - zul license: - cc-by-4.0 multilinguality: - multilingual size_categories: - 10K<n<100K task_categories: - automatic-speech-recognition task_ids: [] pretty_name: 'The Cross-lingual TRansfer Evaluation of Multilingual Encoders for Speech (XTREME-S) benchmark is a benchmark designed to evaluate speech representations across languages, tasks, domains and data regimes. It covers 102 languages from 10+ language families, 3 different domains and 4 task families: speech recognition, translation, classification and retrieval.' tags: - speech-recognition --- # FLEURS ## Dataset Description - **Fine-Tuning script:** [pytorch/speech-recognition](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition) - **Paper:** [FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech](https://arxiv.org/abs/2205.12446) - **Total amount of disk used:** ca. 350 GB Fleurs is the speech version of the [FLoRes machine translation benchmark](https://arxiv.org/abs/2106.03193). We use 2009 n-way parallel sentences from the FLoRes dev and devtest publicly available sets, in 102 languages. Training sets have around 10 hours of supervision. Speakers of the train sets are different than speakers from the dev/test sets. Multilingual fine-tuning is used and ”unit error rate” (characters, signs) of all languages is averaged. Languages and results are also grouped into seven geographical areas: - **Western Europe**: *Asturian, Bosnian, Catalan, Croatian, Danish, Dutch, English, Finnish, French, Galician, German, Greek, Hungarian, Icelandic, Irish, Italian, Kabuverdianu, Luxembourgish, Maltese, Norwegian, Occitan, Portuguese, Spanish, Swedish, Welsh* - **Eastern Europe**: *Armenian, Belarusian, Bulgarian, Czech, Estonian, Georgian, Latvian, Lithuanian, Macedonian, Polish, Romanian, Russian, Serbian, Slovak, Slovenian, Ukrainian* - **Central-Asia/Middle-East/North-Africa**: *Arabic, Azerbaijani, Hebrew, Kazakh, Kyrgyz, Mongolian, Pashto, Persian, Sorani-Kurdish, Tajik, Turkish, Uzbek* - **Sub-Saharan Africa**: *Afrikaans, Amharic, Fula, Ganda, Hausa, Igbo, Kamba, Lingala, Luo, Northern-Sotho, Nyanja, Oromo, Shona, Somali, Swahili, Umbundu, Wolof, Xhosa, Yoruba, Zulu* - **South-Asia**: *Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Nepali, Oriya, Punjabi, Sindhi, Tamil, Telugu, Urdu* - **South-East Asia**: *Burmese, Cebuano, Filipino, Indonesian, Javanese, Khmer, Lao, Malay, Maori, Thai, Vietnamese* - **CJK languages**: *Cantonese and Mandarin Chinese, Japanese, Korean* ## How to use & Supported Tasks ### How to use The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function. For example, to download the Hindi config, simply specify the corresponding language config name (i.e., "hi_in" for Hindi): ```python from datasets import load_dataset fleurs = load_dataset("google/fleurs", "hi_in", split="train") ``` Using the datasets library, you can also stream the dataset on-the-fly by adding a `streaming=True` argument to the `load_dataset` function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk. ```python from datasets import load_dataset fleurs = load_dataset("google/fleurs", "hi_in", split="train", streaming=True) print(next(iter(fleurs))) ``` *Bonus*: create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch) directly with your own datasets (local/streamed). Local: ```python from datasets import load_dataset from torch.utils.data.sampler import BatchSampler, RandomSampler fleurs = load_dataset("google/fleurs", "hi_in", split="train") batch_sampler = BatchSampler(RandomSampler(fleurs), batch_size=32, drop_last=False) dataloader = DataLoader(fleurs, batch_sampler=batch_sampler) ``` Streaming: ```python from datasets import load_dataset from torch.utils.data import DataLoader fleurs = load_dataset("google/fleurs", "hi_in", split="train") dataloader = DataLoader(fleurs, batch_size=32) ``` To find out more about loading and preparing audio datasets, head over to [hf.co/blog/audio-datasets](https://huggingface.co/blog/audio-datasets). ### Example scripts Train your own CTC or Seq2Seq Automatic Speech Recognition models on FLEURS with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition). Fine-tune your own Language Identification models on FLEURS with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/audio-classification) ### 1. Speech Recognition (ASR) ```py from datasets import load_dataset fleurs_asr = load_dataset("google/fleurs", "af_za") # for Afrikaans # to download all data for multi-lingual fine-tuning uncomment following line # fleurs_asr = load_dataset("google/fleurs", "all") # see structure print(fleurs_asr) # load audio sample on the fly audio_input = fleurs_asr["train"][0]["audio"] # first decoded audio sample transcription = fleurs_asr["train"][0]["transcription"] # first transcription # use `audio_input` and `transcription` to fine-tune your model for ASR # for analyses see language groups all_language_groups = fleurs_asr["train"].features["lang_group_id"].names lang_group_id = fleurs_asr["train"][0]["lang_group_id"] all_language_groups[lang_group_id] ``` ### 2. Language Identification LangID can often be a domain classification, but in the case of FLEURS-LangID, recordings are done in a similar setting across languages and the utterances correspond to n-way parallel sentences, in the exact same domain, making this task particularly relevant for evaluating LangID. The setting is simple, FLEURS-LangID is splitted in train/valid/test for each language. We simply create a single train/valid/test for LangID by merging all. ```py from datasets import load_dataset fleurs_langID = load_dataset("google/fleurs", "all") # to download all data # see structure print(fleurs_langID) # load audio sample on the fly audio_input = fleurs_langID["train"][0]["audio"] # first decoded audio sample language_class = fleurs_langID["train"][0]["lang_id"] # first id class language = fleurs_langID["train"].features["lang_id"].names[language_class] # use audio_input and language_class to fine-tune your model for audio classification ``` ### 3. Retrieval Retrieval provides n-way parallel speech and text data. Similar to how XTREME for text leverages Tatoeba to evaluate bitext mining a.k.a sentence translation retrieval, we use Retrieval to evaluate the quality of fixed-size representations of speech utterances. Our goal is to incentivize the creation of fixed-size speech encoder for speech retrieval. The system has to retrieve the English "key" utterance corresponding to the speech translation of "queries" in 15 languages. Results have to be reported on the test sets of Retrieval whose utterances are used as queries (and keys for English). We augment the English keys with a large number of utterances to make the task more difficult. ```py from datasets import load_dataset fleurs_retrieval = load_dataset("google/fleurs", "af_za") # for Afrikaans # to download all data for multi-lingual fine-tuning uncomment following line # fleurs_retrieval = load_dataset("google/fleurs", "all") # see structure print(fleurs_retrieval) # load audio sample on the fly audio_input = fleurs_retrieval["train"][0]["audio"] # decoded audio sample text_sample_pos = fleurs_retrieval["train"][0]["transcription"] # positive text sample text_sample_neg = fleurs_retrieval["train"][1:20]["transcription"] # negative text samples # use `audio_input`, `text_sample_pos`, and `text_sample_neg` to fine-tune your model for retrieval ``` Users can leverage the training (and dev) sets of FLEURS-Retrieval with a ranking loss to build better cross-lingual fixed-size representations of speech. ## Dataset Structure We show detailed information the example configurations `af_za` of the dataset. All other configurations have the same structure. ### Data Instances **af_za** - Size of downloaded dataset files: 1.47 GB - Size of the generated dataset: 1 MB - Total amount of disk used: 1.47 GB An example of a data instance of the config `af_za` looks as follows: ``` {'id': 91, 'num_samples': 385920, 'path': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/310a663d52322700b3d3473cbc5af429bd92a23f9bc683594e70bc31232db39e/home/vaxelrod/FLEURS/oss2_obfuscated/af_za/audio/train/17797742076841560615.wav', 'audio': {'path': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/310a663d52322700b3d3473cbc5af429bd92a23f9bc683594e70bc31232db39e/home/vaxelrod/FLEURS/oss2_obfuscated/af_za/audio/train/17797742076841560615.wav', 'array': array([ 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., -1.1205673e-04, -8.4638596e-05, -1.2731552e-04], dtype=float32), 'sampling_rate': 16000}, 'raw_transcription': 'Dit is nog nie huidiglik bekend watter aantygings gemaak sal word of wat owerhede na die seun gelei het nie maar jeugmisdaad-verrigtinge het in die federale hof begin', 'transcription': 'dit is nog nie huidiglik bekend watter aantygings gemaak sal word of wat owerhede na die seun gelei het nie maar jeugmisdaad-verrigtinge het in die federale hof begin', 'gender': 0, 'lang_id': 0, 'language': 'Afrikaans', 'lang_group_id': 3} ``` ### Data Fields The data fields are the same among all splits. - **id** (int): ID of audio sample - **num_samples** (int): Number of float values - **path** (str): Path to the audio file - **audio** (dict): Audio object including loaded audio array, sampling rate and path ot audio - **raw_transcription** (str): The non-normalized transcription of the audio file - **transcription** (str): Transcription of the audio file - **gender** (int): Class id of gender - **lang_id** (int): Class id of language - **lang_group_id** (int): Class id of language group ### Data Splits Every config only has the `"train"` split containing of *ca.* 1000 examples, and a `"validation"` and `"test"` split each containing of *ca.* 400 examples. ## Dataset Creation We collect between one and three recordings for each sentence (2.3 on average), and buildnew train-dev-test splits with 1509, 150 and 350 sentences for train, dev and test respectively. ## Considerations for Using the Data ### Social Impact of Dataset This dataset is meant to encourage the development of speech technology in a lot more languages of the world. One of the goal is to give equal access to technologies like speech recognition or speech translation to everyone, meaning better dubbing or better access to content from the internet (like podcasts, streaming or videos). ### Discussion of Biases Most datasets have a fair distribution of gender utterances (e.g. the newly introduced FLEURS dataset). While many languages are covered from various regions of the world, the benchmark misses many languages that are all equally important. We believe technology built through FLEURS should generalize to all languages. ### Other Known Limitations The dataset has a particular focus on read-speech because common evaluation benchmarks like CoVoST-2 or LibriSpeech evaluate on this type of speech. There is sometimes a known mismatch between performance obtained in a read-speech setting and a more noisy setting (in production for instance). Given the big progress that remains to be made on many languages, we believe better performance on FLEURS should still correlate well with actual progress made for speech understanding. ## Additional Information All datasets are licensed under the [Creative Commons license (CC-BY)](https://creativecommons.org/licenses/). ### Citation Information You can access the FLEURS paper at https://arxiv.org/abs/2205.12446. Please cite the paper when referencing the FLEURS corpus as: ``` @article{fleurs2022arxiv, title = {FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech}, author = {Conneau, Alexis and Ma, Min and Khanuja, Simran and Zhang, Yu and Axelrod, Vera and Dalmia, Siddharth and Riesa, Jason and Rivera, Clara and Bapna, Ankur}, journal={arXiv preprint arXiv:2205.12446}, url = {https://arxiv.org/abs/2205.12446}, year = {2022}, ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) and [@aconneau](https://github.com/aconneau) for adding this dataset.

AmazonScience/massive

AmazonScience

--- annotations_creators: - expert-generated language_creators: - found license: - cc-by-4.0 multilinguality: - af-ZA - am-ET - ar-SA - az-AZ - bn-BD - ca-ES - cy-GB - da-DK - de-DE - el-GR - en-US - es-ES - fa-IR - fi-FI - fr-FR - he-IL - hi-IN - hu-HU - hy-AM - id-ID - is-IS - it-IT - ja-JP - jv-ID - ka-GE - km-KH - kn-IN - ko-KR - lv-LV - ml-IN - mn-MN - ms-MY - my-MM - nb-NO - nl-NL - pl-PL - pt-PT - ro-RO - ru-RU - sl-SL - sq-AL - sv-SE - sw-KE - ta-IN - te-IN - th-TH - tl-PH - tr-TR - ur-PK - vi-VN - zh-CN - zh-TW size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - intent-classification - multi-class-classification paperswithcode_id: massive pretty_name: MASSIVE language_bcp47: - af-ZA - am-ET - ar-SA - az-AZ - bn-BD - ca-ES - cy-GB - da-DK - de-DE - el-GR - en-US - es-ES - fa-IR - fi-FI - fr-FR - he-IL - hi-IN - hu-HU - hy-AM - id-ID - is-IS - it-IT - ja-JP - jv-ID - ka-GE - km-KH - kn-IN - ko-KR - lv-LV - ml-IN - mn-MN - ms-MY - my-MM - nb-NO - nl-NL - pl-PL - pt-PT - ro-RO - ru-RU - sl-SL - sq-AL - sv-SE - sw-KE - ta-IN - te-IN - th-TH - tl-PH - tr-TR - ur-PK - vi-VN - zh-CN - zh-TW tags: - natural-language-understanding --- # MASSIVE 1.1: A 1M-Example Multilingual Natural Language Understanding Dataset with 52 Typologically-Diverse Languages ## Table of Contents - [Dataset Card for [Needs More Information]](#dataset-card-for-needs-more-information) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [No Warranty](#no-warranty) - [Citation Information](#citation-information) ## Dataset Description - **Homepage:** https://github.com/alexa/massive - **Repository:** https://github.com/alexa/massive - **Paper:** https://arxiv.org/abs/2204.08582 - **Leaderboard:** https://eval.ai/web/challenges/challenge-page/1697/overview - **Point of Contact:** [GitHub](https://github.com/alexa/massive/issues) ### Dataset Summary MASSIVE 1.1 is a parallel dataset of > 1M utterances across 52 languages with annotations for the Natural Language Understanding tasks of intent prediction and slot annotation. Utterances span 60 intents and include 55 slot types. MASSIVE was created by localizing the SLURP dataset, composed of general Intelligent Voice Assistant single-shot interactions. | Name | Lang | Utt/Lang | Domains | Intents | Slots | |:-------------------------------------------------------------------------------:|:-------:|:--------------:|:-------:|:--------:|:------:| | MASSIVE 1.1 | 52 | 19,521 | 18 | 60 | 55 | | SLURP (Bastianelli et al., 2020) | 1 | 16,521 | 18 | 60 | 55 | | NLU Evaluation Data (Liu et al., 2019) | 1 | 25,716 | 18 | 54 | 56 | | Airline Travel Information System (ATIS) (Price, 1990) | 1 | 5,871 | 1 | 26 | 129 | | ATIS with Hindi and Turkish (Upadhyay et al., 2018) | 3 | 1,315-5,871 | 1 | 26 | 129 | | MultiATIS++ (Xu et al., 2020) | 9 | 1,422-5,897 | 1 | 21-26 | 99-140 | | Snips (Coucke et al., 2018) | 1 | 14,484 | - | 7 | 53 | | Snips with French (Saade et al., 2019) | 2 | 4,818 | 2 | 14-15 | 11-12 | | Task Oriented Parsing (TOP) (Gupta et al., 2018) | 1 | 44,873 | 2 | 25 | 36 | | Multilingual Task-Oriented Semantic Parsing (MTOP) (Li et al., 2021) | 6 | 15,195-22,288 | 11 | 104-113 | 72-75 | | Cross-Lingual Multilingual Task Oriented Dialog (Schuster et al., 2019) | 3 | 5,083-43,323 | 3 | 12 | 11 | | Microsoft Dialog Challenge (Li et al., 2018) | 1 | 38,276 | 3 | 11 | 29 | | Fluent Speech Commands (FSC) (Lugosch et al., 2019) | 1 | 30,043 | - | 31 | - | | Chinese Audio-Textual Spoken Language Understanding (CATSLU) (Zhu et al., 2019) | 1 | 16,258 | 4 | - | 94 | ### Supported Tasks and Leaderboards The dataset can be used to train a model for `natural-language-understanding` (NLU) : - `intent-classification` - `multi-class-classification` - `natural-language-understanding` ### Languages The MASSIVE 1.1 corpora consists of parallel sentences from 52 languages : - `Afrikaans - South Africa (af-ZA)` - `Amharic - Ethiopia (am-ET)` - `Arabic - Saudi Arabia (ar-SA)` - `Azeri - Azerbaijan (az-AZ)` - `Bengali - Bangladesh (bn-BD)` - `Catalan - Spain (ca-ES)` - `Chinese - China (zh-CN)` - `Chinese - Taiwan (zh-TW)` - `Danish - Denmark (da-DK)` - `German - Germany (de-DE)` - `Greek - Greece (el-GR)` - `English - United States (en-US)` - `Spanish - Spain (es-ES)` - `Farsi - Iran (fa-IR)` - `Finnish - Finland (fi-FI)` - `French - France (fr-FR)` - `Hebrew - Israel (he-IL)` - `Hungarian - Hungary (hu-HU)` - `Armenian - Armenia (hy-AM)` - `Indonesian - Indonesia (id-ID)` - `Icelandic - Iceland (is-IS)` - `Italian - Italy (it-IT)` - `Japanese - Japan (ja-JP)` - `Javanese - Indonesia (jv-ID)` - `Georgian - Georgia (ka-GE)` - `Khmer - Cambodia (km-KH)` - `Korean - Korea (ko-KR)` - `Latvian - Latvia (lv-LV)` - `Mongolian - Mongolia (mn-MN)` - `Malay - Malaysia (ms-MY)` - `Burmese - Myanmar (my-MM)` - `Norwegian - Norway (nb-NO)` - `Dutch - Netherlands (nl-NL)` - `Polish - Poland (pl-PL)` - `Portuguese - Portugal (pt-PT)` - `Romanian - Romania (ro-RO)` - `Russian - Russia (ru-RU)` - `Slovanian - Slovania (sl-SL)` - `Albanian - Albania (sq-AL)` - `Swedish - Sweden (sv-SE)` - `Swahili - Kenya (sw-KE)` - `Hindi - India (hi-IN)` - `Kannada - India (kn-IN)` - `Malayalam - India (ml-IN)` - `Tamil - India (ta-IN)` - `Telugu - India (te-IN)` - `Thai - Thailand (th-TH)` - `Tagalog - Philippines (tl-PH)` - `Turkish - Turkey (tr-TR)` - `Urdu - Pakistan (ur-PK)` - `Vietnamese - Vietnam (vi-VN)` - `Welsh - United Kingdom (cy-GB)` ## Load the dataset with HuggingFace ```python from datasets import load_dataset dataset = load_dataset("AmazonScience/massive", "en-US", split='train') print(dataset[0]) ``` ## Dataset Structure ### Data Instances ```json { "id": "0", "locale": "fr-FR", "partition": "test", "scenario": "alarm", "intent": "alarm_set", "utt": "réveille-moi à cinq heures du matin cette semaine", "annot_utt": "réveille-moi à [time : cinq heures du matin] [date : cette semaine]", "worker_id": "22", "slot_method": [ { "slot": "time", "method": "translation" }, { "slot": "date", "method": "translation" } ], "judgments": [ { "worker_id": "22", "intent_score": 1, "slots_score": 1, "grammar_score": 4, "spelling_score": 2, "language_identification": "target" }, { "worker_id": "8", "intent_score": 1, "slots_score": 1, "grammar_score": 4, "spelling_score": 2, "language_identification": "target" }, { "worker_id": "0", "intent_score": 1, "slots_score": 1, "grammar_score": 4, "spelling_score": 2, "language_identification": "target" } ] } ``` ### Data Fields `id`: maps to the original ID in the [SLURP](https://github.com/pswietojanski/slurp) collection. Mapping back to the SLURP en-US utterance, this utterance served as the basis for this localization. `locale`: is the language and country code accoring to ISO-639-1 and ISO-3166. `partition`: is either `train`, `dev`, or `test`, according to the original split in [SLURP](https://github.com/pswietojanski/slurp). `scenario`: is the general domain, aka "scenario" in SLURP terminology, of an utterance `intent`: is the specific intent of an utterance within a domain formatted as `{scenario}_{intent}` `utt`: the raw utterance text without annotations `annot_utt`: the text from `utt` with slot annotations formatted as `[{label} : {entity}]` `worker_id`: The obfuscated worker ID from MTurk of the worker completing the localization of the utterance. Worker IDs are specific to a locale and do *not* map across locales. `slot_method`: for each slot in the utterance, whether that slot was a `translation` (i.e., same expression just in the target language), `localization` (i.e., not the same expression but a different expression was chosen more suitable to the phrase in that locale), or `unchanged` (i.e., the original en-US slot value was copied over without modification). `judgments`: Each judgment collected for the localized utterance has 6 keys. `worker_id` is the obfuscated worker ID from MTurk of the worker completing the judgment. Worker IDs are specific to a locale and do *not* map across locales, but *are* consistent across the localization tasks and the judgment tasks, e.g., judgment worker ID 32 in the example above may appear as the localization worker ID for the localization of a different de-DE utterance, in which case it would be the same worker. ```plain intent_score : "Does the sentence match the intent?" 0: No 1: Yes 2: It is a reasonable interpretation of the goal slots_score : "Do all these terms match the categories in square brackets?" 0: No 1: Yes 2: There are no words in square brackets (utterance without a slot) grammar_score : "Read the sentence out loud. Ignore any spelling, punctuation, or capitalization errors. Does it sound natural?" 0: Completely unnatural (nonsensical, cannot be understood at all) 1: Severe errors (the meaning cannot be understood and doesn't sound natural in your language) 2: Some errors (the meaning can be understood but it doesn't sound natural in your language) 3: Good enough (easily understood and sounds almost natural in your language) 4: Perfect (sounds natural in your language) spelling_score : "Are all words spelled correctly? Ignore any spelling variances that may be due to differences in dialect. Missing spaces should be marked as a spelling error." 0: There are more than 2 spelling errors 1: There are 1-2 spelling errors 2: All words are spelled correctly language_identification : "The following sentence contains words in the following languages (check all that apply)" 1: target 2: english 3: other 4: target & english 5: target & other 6: english & other 7: target & english & other ``` ### Data Splits |Language|Train|Dev|Test| |:---:|:---:|:---:|:---:| |af-ZA|11514|2033|2974| |am-ET|11514|2033|2974| |ar-SA|11514|2033|2974| |az-AZ|11514|2033|2974| |bn-BD|11514|2033|2974| |ca-ES|11514|2033|2974| |cy-GB|11514|2033|2974| |da-DK|11514|2033|2974| |de-DE|11514|2033|2974| |el-GR|11514|2033|2974| |en-US|11514|2033|2974| |es-ES|11514|2033|2974| |fa-IR|11514|2033|2974| |fi-FI|11514|2033|2974| |fr-FR|11514|2033|2974| |he-IL|11514|2033|2974| |hi-IN|11514|2033|2974| |hu-HU|11514|2033|2974| |hy-AM|11514|2033|2974| |id-ID|11514|2033|2974| |is-IS|11514|2033|2974| |it-IT|11514|2033|2974| |ja-JP|11514|2033|2974| |jv-ID|11514|2033|2974| |ka-GE|11514|2033|2974| |km-KH|11514|2033|2974| |kn-IN|11514|2033|2974| |ko-KR|11514|2033|2974| |lv-LV|11514|2033|2974| |ml-IN|11514|2033|2974| |mn-MN|11514|2033|2974| |ms-MY|11514|2033|2974| |my-MM|11514|2033|2974| |nb-NO|11514|2033|2974| |nl-NL|11514|2033|2974| |pl-PL|11514|2033|2974| |pt-PT|11514|2033|2974| |ro-RO|11514|2033|2974| |ru-RU|11514|2033|2974| |sl-SL|11514|2033|2974| |sq-AL|11514|2033|2974| |sv-SE|11514|2033|2974| |sw-KE|11514|2033|2974| |ta-IN|11514|2033|2974| |te-IN|11514|2033|2974| |th-TH|11514|2033|2974| |tl-PH|11514|2033|2974| |tr-TR|11514|2033|2974| |ur-PK|11514|2033|2974| |vi-VN|11514|2033|2974| |zh-CN|11514|2033|2974| |zh-TW|11514|2033|2974| ### Personal and Sensitive Information The corpora is free of personal or sensitive information. ## Additional Information ### Dataset Curators __MASSIVE__: Jack FitzGerald and Christopher Hench and Charith Peris and Scott Mackie and Kay Rottmann and Ana Sanchez and Aaron Nash and Liam Urbach and Vishesh Kakarala and Richa Singh and Swetha Ranganath and Laurie Crist and Misha Britan and Wouter Leeuwis and Gokhan Tur and Prem Natarajan. __SLURP__: Bastianelli, Emanuele and Vanzo, Andrea and Swietojanski, Pawel and Rieser, Verena. __Hugging Face Upload and Integration__: Labrak Yanis (Not affiliated with the original corpus) ### Licensing Information ```plain Copyright Amazon.com Inc. or its affiliates. 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Creative Commons may be contacted at creativecommons.org. ``` ### Citation Information Please cite the following papers when using this dataset. ```latex @misc{fitzgerald2022massive, title={MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages}, author={Jack FitzGerald and Christopher Hench and Charith Peris and Scott Mackie and Kay Rottmann and Ana Sanchez and Aaron Nash and Liam Urbach and Vishesh Kakarala and Richa Singh and Swetha Ranganath and Laurie Crist and Misha Britan and Wouter Leeuwis and Gokhan Tur and Prem Natarajan}, year={2022}, eprint={2204.08582}, archivePrefix={arXiv}, primaryClass={cs.CL} } @inproceedings{bastianelli-etal-2020-slurp, title = "{SLURP}: A Spoken Language Understanding Resource Package", author = "Bastianelli, Emanuele and Vanzo, Andrea and Swietojanski, Pawel and Rieser, Verena", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.emnlp-main.588", doi = "10.18653/v1/2020.emnlp-main.588", pages = "7252--7262", abstract = "Spoken Language Understanding infers semantic meaning directly from audio data, and thus promises to reduce error propagation and misunderstandings in end-user applications. However, publicly available SLU resources are limited. In this paper, we release SLURP, a new SLU package containing the following: (1) A new challenging dataset in English spanning 18 domains, which is substantially bigger and linguistically more diverse than existing datasets; (2) Competitive baselines based on state-of-the-art NLU and ASR systems; (3) A new transparent metric for entity labelling which enables a detailed error analysis for identifying potential areas of improvement. SLURP is available at https://github.com/pswietojanski/slurp." } ```

khalidalt/tydiqa-goldp

khalidalt

--- pretty_name: TyDi QA annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en - ar - bn - fi - id - ja - sw - ko - ru - te - th license: - apache-2.0 multilinguality: - multilingual size_categories: - unknown source_datasets: - extended|wikipedia task_categories: - question-answering task_ids: - extractive-qa paperswithcode_id: tydi-qa --- # Dataset Card for "tydiqa" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://github.com/google-research-datasets/tydiqa](https://github.com/google-research-datasets/tydiqa) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 3726.74 MB - **Size of the generated dataset:** 5812.92 MB - **Total amount of disk used:** 9539.67 MB ### Dataset Summary TyDi QA is a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs. The languages of TyDi QA are diverse with regard to their typology -- the set of linguistic features that each language expresses -- such that we expect models performing well on this set to generalize across a large number of the languages in the world. It contains language phenomena that would not be found in English-only corpora. To provide a realistic information-seeking task and avoid priming effects, questions are written by people who want to know the answer, but don’t know the answer yet, (unlike SQuAD and its descendents) and the data is collected directly in each language without the use of translation (unlike MLQA and XQuAD). ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### primary_task - **Size of downloaded dataset files:** 1863.37 MB - **Size of the generated dataset:** 5757.59 MB - **Total amount of disk used:** 7620.96 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "annotations": { "minimal_answers_end_byte": [-1, -1, -1], "minimal_answers_start_byte": [-1, -1, -1], "passage_answer_candidate_index": [-1, -1, -1], "yes_no_answer": ["NONE", "NONE", "NONE"] }, "document_plaintext": "\"\\nรองศาสตราจารย์[1] หม่อมราชวงศ์สุขุมพันธุ์ บริพัตร (22 กันยายน 2495 -) ผู้ว่าราชการกรุงเทพมหานครคนที่ 15 อดีตรองหัวหน้าพรรคปร...", "document_title": "หม่อมราชวงศ์สุขุมพันธุ์ บริพัตร", "document_url": "\"https://th.wikipedia.org/wiki/%E0%B8%AB%E0%B8%A1%E0%B9%88%E0%B8%AD%E0%B8%A1%E0%B8%A3%E0%B8%B2%E0%B8%8A%E0%B8%A7%E0%B8%87%E0%B8%...", "language": "thai", "passage_answer_candidates": "{\"plaintext_end_byte\": [494, 1779, 2931, 3904, 4506, 5588, 6383, 7122, 8224, 9375, 10473, 12563, 15134, 17765, 19863, 21902, 229...", "question_text": "\"หม่อมราชวงศ์สุขุมพันธุ์ บริพัตร เรียนจบจากที่ไหน ?\"..." } ``` #### secondary_task - **Size of downloaded dataset files:** 1863.37 MB - **Size of the generated dataset:** 55.34 MB - **Total amount of disk used:** 1918.71 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "answers": { "answer_start": [394], "text": ["بطولتين"] }, "context": "\"أقيمت البطولة 21 مرة، شارك في النهائيات 78 دولة، وعدد الفرق التي فازت بالبطولة حتى الآن 8 فرق، ويعد المنتخب البرازيلي الأكثر تت...", "id": "arabic-2387335860751143628-1", "question": "\"كم عدد مرات فوز الأوروغواي ببطولة كاس العالم لكرو القدم؟\"...", "title": "قائمة نهائيات كأس العالم" } ``` ### Data Fields The data fields are the same among all splits. #### primary_task - `passage_answer_candidates`: a dictionary feature containing: - `plaintext_start_byte`: a `int32` feature. - `plaintext_end_byte`: a `int32` feature. - `question_text`: a `string` feature. - `document_title`: a `string` feature. - `language`: a `string` feature. - `annotations`: a dictionary feature containing: - `passage_answer_candidate_index`: a `int32` feature. - `minimal_answers_start_byte`: a `int32` feature. - `minimal_answers_end_byte`: a `int32` feature. - `yes_no_answer`: a `string` feature. - `document_plaintext`: a `string` feature. - `document_url`: a `string` feature. #### secondary_task - `id`: a `string` feature. - `title`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `answer_start`: a `int32` feature. ### Data Splits | name | train | validation | | -------------- | -----: | ---------: | | primary_task | 166916 | 18670 | | secondary_task | 49881 | 5077 | ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @article{tydiqa, title = {TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages}, author = {Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki} year = {2020}, journal = {Transactions of the Association for Computational Linguistics} } ``` ``` @inproceedings{ruder-etal-2021-xtreme, title = "{XTREME}-{R}: Towards More Challenging and Nuanced Multilingual Evaluation", author = "Ruder, Sebastian and Constant, Noah and Botha, Jan and Siddhant, Aditya and Firat, Orhan and Fu, Jinlan and Liu, Pengfei and Hu, Junjie and Garrette, Dan and Neubig, Graham and Johnson, Melvin", booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.802", doi = "10.18653/v1/2021.emnlp-main.802", pages = "10215--10245", } } ```

lex_glue

--- annotations_creators: - found language_creators: - found language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended task_categories: - question-answering - text-classification task_ids: - multi-class-classification - multi-label-classification - multiple-choice-qa - topic-classification pretty_name: LexGLUE dataset_info: - config_name: ecthr_a features: - name: text sequence: string - name: labels sequence: class_label: names: '0': '2' '1': '3' '2': '5' '3': '6' '4': '8' '5': '9' '6': '10' '7': '11' '8': '14' '9': P1-1 splits: - name: train num_bytes: 89637461 num_examples: 9000 - name: test num_bytes: 11884180 num_examples: 1000 - name: validation num_bytes: 10985180 num_examples: 1000 download_size: 32852475 dataset_size: 112506821 - config_name: ecthr_b features: - name: text sequence: string - name: labels sequence: class_label: names: '0': '2' '1': '3' '2': '5' '3': '6' '4': '8' '5': '9' '6': '10' '7': '11' '8': '14' '9': P1-1 splits: - name: train num_bytes: 89657661 num_examples: 9000 - name: test num_bytes: 11886940 num_examples: 1000 - name: validation num_bytes: 10987828 num_examples: 1000 download_size: 32852475 dataset_size: 112532429 - config_name: eurlex features: - name: text dtype: string - name: labels sequence: class_label: names: '0': '100163' '1': '100168' '2': '100169' '3': '100170' '4': '100171' '5': '100172' '6': '100173' '7': '100174' '8': '100175' '9': '100176' '10': '100177' '11': '100179' '12': '100180' '13': '100183' '14': '100184' '15': '100185' '16': '100186' '17': '100187' '18': '100189' '19': '100190' '20': '100191' '21': '100192' '22': '100193' '23': '100194' '24': '100195' '25': '100196' '26': '100197' '27': '100198' '28': '100199' '29': '100200' '30': '100201' '31': '100202' '32': '100204' '33': '100205' '34': '100206' '35': '100207' '36': '100212' '37': '100214' '38': '100215' '39': '100220' '40': '100221' '41': '100222' '42': '100223' '43': '100224' '44': '100226' '45': '100227' '46': '100229' '47': '100230' '48': '100231' '49': '100232' '50': '100233' '51': '100234' '52': '100235' '53': '100237' '54': '100238' '55': '100239' '56': '100240' '57': '100241' '58': '100242' '59': '100243' '60': '100244' '61': '100245' '62': '100246' '63': '100247' '64': '100248' '65': '100249' '66': '100250' '67': '100252' '68': '100253' '69': '100254' '70': '100255' '71': '100256' '72': '100257' '73': '100258' '74': '100259' '75': '100260' '76': '100261' '77': '100262' '78': '100263' '79': '100264' '80': '100265' '81': '100266' '82': '100268' '83': '100269' '84': '100270' '85': '100271' '86': '100272' '87': '100273' '88': '100274' '89': '100275' '90': '100276' '91': '100277' '92': '100278' '93': '100279' '94': '100280' '95': '100281' '96': '100282' '97': '100283' '98': '100284' '99': '100285' splits: - name: train num_bytes: 390770289 num_examples: 55000 - name: test num_bytes: 59739102 num_examples: 5000 - name: validation num_bytes: 41544484 num_examples: 5000 download_size: 125413277 dataset_size: 492053875 - config_name: scotus features: - name: text dtype: string - name: label dtype: class_label: names: '0': '1' '1': '2' '2': '3' '3': '4' '4': '5' '5': '6' '6': '7' '7': '8' '8': '9' '9': '10' '10': '11' '11': '12' '12': '13' splits: - name: train num_bytes: 178959320 num_examples: 5000 - name: test num_bytes: 76213283 num_examples: 1400 - name: validation num_bytes: 75600247 num_examples: 1400 download_size: 104763335 dataset_size: 330772850 - config_name: ledgar features: - name: text dtype: string - name: label dtype: class_label: names: '0': Adjustments '1': Agreements '2': Amendments '3': Anti-Corruption Laws '4': Applicable Laws '5': Approvals '6': Arbitration '7': Assignments '8': Assigns '9': Authority '10': Authorizations '11': Base Salary '12': Benefits '13': Binding Effects '14': Books '15': Brokers '16': Capitalization '17': Change In Control '18': Closings '19': Compliance With Laws '20': Confidentiality '21': Consent To Jurisdiction '22': Consents '23': Construction '24': Cooperation '25': Costs '26': Counterparts '27': Death '28': Defined Terms '29': Definitions '30': Disability '31': Disclosures '32': Duties '33': Effective Dates '34': Effectiveness '35': Employment '36': Enforceability '37': Enforcements '38': Entire Agreements '39': Erisa '40': Existence '41': Expenses '42': Fees '43': Financial Statements '44': Forfeitures '45': Further Assurances '46': General '47': Governing Laws '48': Headings '49': Indemnifications '50': Indemnity '51': Insurances '52': Integration '53': Intellectual Property '54': Interests '55': Interpretations '56': Jurisdictions '57': Liens '58': Litigations '59': Miscellaneous '60': Modifications '61': No Conflicts '62': No Defaults '63': No Waivers '64': Non-Disparagement '65': Notices '66': Organizations '67': Participations '68': Payments '69': Positions '70': Powers '71': Publicity '72': Qualifications '73': Records '74': Releases '75': Remedies '76': Representations '77': Sales '78': Sanctions '79': Severability '80': Solvency '81': Specific Performance '82': Submission To Jurisdiction '83': Subsidiaries '84': Successors '85': Survival '86': Tax Withholdings '87': Taxes '88': Terminations '89': Terms '90': Titles '91': Transactions With Affiliates '92': Use Of Proceeds '93': Vacations '94': Venues '95': Vesting '96': Waiver Of Jury Trials '97': Waivers '98': Warranties '99': Withholdings splits: - name: train num_bytes: 43358315 num_examples: 60000 - name: test num_bytes: 6845585 num_examples: 10000 - name: validation num_bytes: 7143592 num_examples: 10000 download_size: 16255623 dataset_size: 57347492 - config_name: unfair_tos features: - name: text dtype: string - name: labels sequence: class_label: names: '0': Limitation of liability '1': Unilateral termination '2': Unilateral change '3': Content removal '4': Contract by using '5': Choice of law '6': Jurisdiction '7': Arbitration splits: - name: train num_bytes: 1041790 num_examples: 5532 - name: test num_bytes: 303107 num_examples: 1607 - name: validation num_bytes: 452119 num_examples: 2275 download_size: 511342 dataset_size: 1797016 - config_name: case_hold features: - name: context dtype: string - name: endings sequence: string - name: label dtype: class_label: names: '0': '0' '1': '1' '2': '2' '3': '3' '4': '4' splits: - name: train num_bytes: 74781766 num_examples: 45000 - name: test num_bytes: 5989964 num_examples: 3600 - name: validation num_bytes: 6474615 num_examples: 3900 download_size: 30422703 dataset_size: 87246345 config_names: - case_hold - ecthr_a - ecthr_b - eurlex - ledgar - scotus - unfair_tos --- # Dataset Card for "LexGLUE" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/coastalcph/lex-glue - **Repository:** https://github.com/coastalcph/lex-glue - **Paper:** https://arxiv.org/abs/2110.00976 - **Leaderboard:** https://github.com/coastalcph/lex-glue - **Point of Contact:** [Ilias Chalkidis](mailto:ilias.chalkidis@di.ku.dk) ### Dataset Summary Inspired by the recent widespread use of the GLUE multi-task benchmark NLP dataset (Wang et al., 2018), the subsequent more difficult SuperGLUE (Wang et al., 2019), other previous multi-task NLP benchmarks (Conneau and Kiela, 2018; McCann et al., 2018), and similar initiatives in other domains (Peng et al., 2019), we introduce the *Legal General Language Understanding Evaluation (LexGLUE) benchmark*, a benchmark dataset to evaluate the performance of NLP methods in legal tasks. LexGLUE is based on seven existing legal NLP datasets, selected using criteria largely from SuperGLUE. As in GLUE and SuperGLUE (Wang et al., 2019b,a), one of our goals is to push towards generic (or ‘foundation’) models that can cope with multiple NLP tasks, in our case legal NLP tasks possibly with limited task-specific fine-tuning. Another goal is to provide a convenient and informative entry point for NLP researchers and practitioners wishing to explore or develop methods for legalNLP. Having these goals in mind, the datasets we include in LexGLUE and the tasks they address have been simplified in several ways to make it easier for newcomers and generic models to address all tasks. LexGLUE benchmark is accompanied by experimental infrastructure that relies on Hugging Face Transformers library and resides at: https://github.com/coastalcph/lex-glue. ### Supported Tasks and Leaderboards The supported tasks are the following: <table> <tr><td>Dataset</td><td>Source</td><td>Sub-domain</td><td>Task Type</td><td>Classes</td><tr> <tr><td>ECtHR (Task A)</td><td> <a href="https://aclanthology.org/P19-1424/">Chalkidis et al. (2019)</a> </td><td>ECHR</td><td>Multi-label classification</td><td>10+1</td></tr> <tr><td>ECtHR (Task B)</td><td> <a href="https://aclanthology.org/2021.naacl-main.22/">Chalkidis et al. (2021a)</a> </td><td>ECHR</td><td>Multi-label classification </td><td>10+1</td></tr> <tr><td>SCOTUS</td><td> <a href="http://scdb.wustl.edu">Spaeth et al. (2020)</a></td><td>US Law</td><td>Multi-class classification</td><td>14</td></tr> <tr><td>EUR-LEX</td><td> <a href="https://arxiv.org/abs/2109.00904">Chalkidis et al. (2021b)</a></td><td>EU Law</td><td>Multi-label classification</td><td>100</td></tr> <tr><td>LEDGAR</td><td> <a href="https://aclanthology.org/2020.lrec-1.155/">Tuggener et al. (2020)</a></td><td>Contracts</td><td>Multi-class classification</td><td>100</td></tr> <tr><td>UNFAIR-ToS</td><td><a href="https://arxiv.org/abs/1805.01217"> Lippi et al. (2019)</a></td><td>Contracts</td><td>Multi-label classification</td><td>8+1</td></tr> <tr><td>CaseHOLD</td><td><a href="https://arxiv.org/abs/2104.08671">Zheng et al. (2021)</a></td><td>US Law</td><td>Multiple choice QA</td><td>n/a</td></tr> </table> #### ecthr_a The European Court of Human Rights (ECtHR) hears allegations that a state has breached human rights provisions of the European Convention of Human Rights (ECHR). For each case, the dataset provides a list of factual paragraphs (facts) from the case description. Each case is mapped to articles of the ECHR that were violated (if any). #### ecthr_b The European Court of Human Rights (ECtHR) hears allegations that a state has breached human rights provisions of the European Convention of Human Rights (ECHR). For each case, the dataset provides a list of factual paragraphs (facts) from the case description. Each case is mapped to articles of ECHR that were allegedly violated (considered by the court). #### scotus The US Supreme Court (SCOTUS) is the highest federal court in the United States of America and generally hears only the most controversial or otherwise complex cases which have not been sufficiently well solved by lower courts. This is a single-label multi-class classification task, where given a document (court opinion), the task is to predict the relevant issue areas. The 14 issue areas cluster 278 issues whose focus is on the subject matter of the controversy (dispute). #### eurlex European Union (EU) legislation is published in EUR-Lex portal. All EU laws are annotated by EU's Publications Office with multiple concepts from the EuroVoc thesaurus, a multilingual thesaurus maintained by the Publications Office. The current version of EuroVoc contains more than 7k concepts referring to various activities of the EU and its Member States (e.g., economics, health-care, trade). Given a document, the task is to predict its EuroVoc labels (concepts). #### ledgar LEDGAR dataset aims contract provision (paragraph) classification. The contract provisions come from contracts obtained from the US Securities and Exchange Commission (SEC) filings, which are publicly available from EDGAR. Each label represents the single main topic (theme) of the corresponding contract provision. #### unfair_tos The UNFAIR-ToS dataset contains 50 Terms of Service (ToS) from on-line platforms (e.g., YouTube, Ebay, Facebook, etc.). The dataset has been annotated on the sentence-level with 8 types of unfair contractual terms (sentences), meaning terms that potentially violate user rights according to the European consumer law. #### case_hold The CaseHOLD (Case Holdings on Legal Decisions) dataset includes multiple choice questions about holdings of US court cases from the Harvard Law Library case law corpus. Holdings are short summaries of legal rulings accompany referenced decisions relevant for the present case. The input consists of an excerpt (or prompt) from a court decision, containing a reference to a particular case, while the holding statement is masked out. The model must identify the correct (masked) holding statement from a selection of five choices. The current leaderboard includes several Transformer-based (Vaswaniet al., 2017) pre-trained language models, which achieve state-of-the-art performance in most NLP tasks (Bommasani et al., 2021) and NLU benchmarks (Wang et al., 2019a). Results reported by [Chalkidis et al. (2021)](https://arxiv.org/abs/2110.00976): *Task-wise Test Results* <table> <tr><td><b>Dataset</b></td><td><b>ECtHR A</b></td><td><b>ECtHR B</b></td><td><b>SCOTUS</b></td><td><b>EUR-LEX</b></td><td><b>LEDGAR</b></td><td><b>UNFAIR-ToS</b></td><td><b>CaseHOLD</b></td></tr> <tr><td><b>Model</b></td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1</td><td>μ-F1 / m-F1 </td></tr> <tr><td>TFIDF+SVM</td><td> 64.7 / 51.7 </td><td>74.6 / 65.1 </td><td> <b>78.2</b> / <b>69.5</b> </td><td>71.3 / 51.4 </td><td>87.2 / 82.4 </td><td>95.4 / 78.8</td><td>n/a </td></tr> <tr><td colspan="8" style='text-align:center'><b>Medium-sized Models (L=12, H=768, A=12)</b></td></tr> <td>BERT</td> <td> 71.2 / 63.6 </td> <td> 79.7 / 73.4 </td> <td> 68.3 / 58.3 </td> <td> 71.4 / 57.2 </td> <td> 87.6 / 81.8 </td> <td> 95.6 / 81.3 </td> <td> 70.8 </td> </tr> <td>RoBERTa</td> <td> 69.2 / 59.0 </td> <td> 77.3 / 68.9 </td> <td> 71.6 / 62.0 </td> <td> 71.9 / <b>57.9</b> </td> <td> 87.9 / 82.3 </td> <td> 95.2 / 79.2 </td> <td> 71.4 </td> </tr> <td>DeBERTa</td> <td> 70.0 / 60.8 </td> <td> 78.8 / 71.0 </td> <td> 71.1 / 62.7 </td> <td> <b>72.1</b> / 57.4 </td> <td> 88.2 / 83.1 </td> <td> 95.5 / 80.3 </td> <td> 72.6 </td> </tr> <td>Longformer</td> <td> 69.9 / 64.7 </td> <td> 79.4 / 71.7 </td> <td> 72.9 / 64.0 </td> <td> 71.6 / 57.7 </td> <td> 88.2 / 83.0 </td> <td> 95.5 / 80.9 </td> <td> 71.9 </td> </tr> <td>BigBird</td> <td> 70.0 / 62.9 </td> <td> 78.8 / 70.9 </td> <td> 72.8 / 62.0 </td> <td> 71.5 / 56.8 </td> <td> 87.8 / 82.6 </td> <td> 95.7 / 81.3 </td> <td> 70.8 </td> </tr> <td>Legal-BERT</td> <td> 70.0 / 64.0 </td> <td> <b>80.4</b> / <b>74.7</b> </td> <td> 76.4 / 66.5 </td> <td> <b>72.1</b> / 57.4 </td> <td> 88.2 / 83.0 </td> <td> <b>96.0</b> / <b>83.0</b> </td> <td> 75.3 </td> </tr> <td>CaseLaw-BERT</td> <td> 69.8 / 62.9 </td> <td> 78.8 / 70.3 </td> <td> 76.6 / 65.9 </td> <td> 70.7 / 56.6 </td> <td> 88.3 / 83.0 </td> <td> <b>96.0</b> / 82.3 </td> <td> <b>75.4</b> </td> </tr> <tr><td colspan="8" style='text-align:center'><b>Large-sized Models (L=24, H=1024, A=18)</b></td></tr> <tr><td>RoBERTa</td> <td> <b>73.8</b> / <b>67.6</b> </td> <td> 79.8 / 71.6 </td> <td> 75.5 / 66.3 </td> <td> 67.9 / 50.3 </td> <td> <b>88.6</b> / <b>83.6</b> </td> <td> 95.8 / 81.6 </td> <td> 74.4 </td> </tr> </table> *Averaged (Mean over Tasks) Test Results* <table> <tr><td><b>Averaging</b></td><td><b>Arithmetic</b></td><td><b>Harmonic</b></td><td><b>Geometric</b></td></tr> <tr><td><b>Model</b></td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td></tr> <tr><td colspan="4" style='text-align:center'><b>Medium-sized Models (L=12, H=768, A=12)</b></td></tr> <tr><td>BERT</td><td> 77.8 / 69.5 </td><td> 76.7 / 68.2 </td><td> 77.2 / 68.8 </td></tr> <tr><td>RoBERTa</td><td> 77.8 / 68.7 </td><td> 76.8 / 67.5 </td><td> 77.3 / 68.1 </td></tr> <tr><td>DeBERTa</td><td> 78.3 / 69.7 </td><td> 77.4 / 68.5 </td><td> 77.8 / 69.1 </td></tr> <tr><td>Longformer</td><td> 78.5 / 70.5 </td><td> 77.5 / 69.5 </td><td> 78.0 / 70.0 </td></tr> <tr><td>BigBird</td><td> 78.2 / 69.6 </td><td> 77.2 / 68.5 </td><td> 77.7 / 69.0 </td></tr> <tr><td>Legal-BERT</td><td> <b>79.8</b> / <b>72.0</b> </td><td> <b>78.9</b> / <b>70.8</b> </td><td> <b>79.3</b> / <b>71.4</b> </td></tr> <tr><td>CaseLaw-BERT</td><td> 79.4 / 70.9 </td><td> 78.5 / 69.7 </td><td> 78.9 / 70.3 </td></tr> <tr><td colspan="4" style='text-align:center'><b>Large-sized Models (L=24, H=1024, A=18)</b></td></tr> <tr><td>RoBERTa</td><td> 79.4 / 70.8 </td><td> 78.4 / 69.1 </td><td> 78.9 / 70.0 </td></tr> </table> ### Languages We only consider English datasets, to make experimentation easier for researchers across the globe. ## Dataset Structure ### Data Instances #### ecthr_a An example of 'train' looks as follows. ```json { "text": ["8. The applicant was arrested in the early morning of 21 October 1990 ...", ...], "labels": [6] } ``` #### ecthr_b An example of 'train' looks as follows. ```json { "text": ["8. The applicant was arrested in the early morning of 21 October 1990 ...", ...], "label": [5, 6] } ``` #### scotus An example of 'train' looks as follows. ```json { "text": "Per Curiam\nSUPREME COURT OF THE UNITED STATES\nRANDY WHITE, WARDEN v. ROGER L. WHEELER\n Decided December 14, 2015\nPER CURIAM.\nA death sentence imposed by a Kentucky trial court and\naffirmed by the ...", "label": 8 } ``` #### eurlex An example of 'train' looks as follows. ```json { "text": "COMMISSION REGULATION (EC) No 1629/96 of 13 August 1996 on an invitation to tender for the refund on export of wholly milled round grain rice to certain third countries ...", "labels": [4, 20, 21, 35, 68] } ``` #### ledgar An example of 'train' looks as follows. ```json { "text": "All Taxes shall be the financial responsibility of the party obligated to pay such Taxes as determined by applicable law and neither party is or shall be liable at any time for any of the other party ...", "label": 32 } ``` #### unfair_tos An example of 'train' looks as follows. ```json { "text": "tinder may terminate your account at any time without notice if it believes that you have violated this agreement.", "label": 2 } ``` #### casehold An example of 'test' looks as follows. ```json { "context": "In Granato v. City and County of Denver, No. CIV 11-0304 MSK/BNB, 2011 WL 3820730 (D.Colo. Aug. 20, 2011), the Honorable Marcia S. Krieger, now-Chief United States District Judge for the District of Colorado, ruled similarly: At a minimum, a party asserting a Mo-nell claim must plead sufficient facts to identify ... to act pursuant to City or State policy, custom, decision, ordinance, re d 503, 506-07 (3d Cir.l985)(<HOLDING>).", "endings": ["holding that courts are to accept allegations in the complaint as being true including monell policies and writing that a federal court reviewing the sufficiency of a complaint has a limited task", "holding that for purposes of a class certification motion the court must accept as true all factual allegations in the complaint and may draw reasonable inferences therefrom", "recognizing that the allegations of the complaint must be accepted as true on a threshold motion to dismiss", "holding that a court need not accept as true conclusory allegations which are contradicted by documents referred to in the complaint", "holding that where the defendant was in default the district court correctly accepted the fact allegations of the complaint as true" ], "label": 0 } ``` ### Data Fields #### ecthr_a - `text`: a list of `string` features (list of factual paragraphs (facts) from the case description). - `labels`: a list of classification labels (a list of violated ECHR articles, if any) . <details> <summary>List of ECHR articles</summary> "Article 2", "Article 3", "Article 5", "Article 6", "Article 8", "Article 9", "Article 10", "Article 11", "Article 14", "Article 1 of Protocol 1" </details> #### ecthr_b - `text`: a list of `string` features (list of factual paragraphs (facts) from the case description) - `labels`: a list of classification labels (a list of articles considered). <details> <summary>List of ECHR articles</summary> "Article 2", "Article 3", "Article 5", "Article 6", "Article 8", "Article 9", "Article 10", "Article 11", "Article 14", "Article 1 of Protocol 1" </details> #### scotus - `text`: a `string` feature (the court opinion). - `label`: a classification label (the relevant issue area). <details> <summary>List of issue areas</summary> (1, Criminal Procedure), (2, Civil Rights), (3, First Amendment), (4, Due Process), (5, Privacy), (6, Attorneys), (7, Unions), (8, Economic Activity), (9, Judicial Power), (10, Federalism), (11, Interstate Relations), (12, Federal Taxation), (13, Miscellaneous), (14, Private Action) </details> #### eurlex - `text`: a `string` feature (an EU law). - `labels`: a list of classification labels (a list of relevant EUROVOC concepts). <details> <summary>List of EUROVOC concepts</summary> The list is very long including 100 EUROVOC concepts. You can find the EUROVOC concepts descriptors <a href="https://raw.githubusercontent.com/nlpaueb/multi-eurlex/master/data/eurovoc_descriptors.json">here</a>. </details> #### ledgar - `text`: a `string` feature (a contract provision/paragraph). - `label`: a classification label (the type of contract provision). <details> <summary>List of contract provision types</summary> "Adjustments", "Agreements", "Amendments", "Anti-Corruption Laws", "Applicable Laws", "Approvals", "Arbitration", "Assignments", "Assigns", "Authority", "Authorizations", "Base Salary", "Benefits", "Binding Effects", "Books", "Brokers", "Capitalization", "Change In Control", "Closings", "Compliance With Laws", "Confidentiality", "Consent To Jurisdiction", "Consents", "Construction", "Cooperation", "Costs", "Counterparts", "Death", "Defined Terms", "Definitions", "Disability", "Disclosures", "Duties", "Effective Dates", "Effectiveness", "Employment", "Enforceability", "Enforcements", "Entire Agreements", "Erisa", "Existence", "Expenses", "Fees", "Financial Statements", "Forfeitures", "Further Assurances", "General", "Governing Laws", "Headings", "Indemnifications", "Indemnity", "Insurances", "Integration", "Intellectual Property", "Interests", "Interpretations", "Jurisdictions", "Liens", "Litigations", "Miscellaneous", "Modifications", "No Conflicts", "No Defaults", "No Waivers", "Non-Disparagement", "Notices", "Organizations", "Participations", "Payments", "Positions", "Powers", "Publicity", "Qualifications", "Records", "Releases", "Remedies", "Representations", "Sales", "Sanctions", "Severability", "Solvency", "Specific Performance", "Submission To Jurisdiction", "Subsidiaries", "Successors", "Survival", "Tax Withholdings", "Taxes", "Terminations", "Terms", "Titles", "Transactions With Affiliates", "Use Of Proceeds", "Vacations", "Venues", "Vesting", "Waiver Of Jury Trials", "Waivers", "Warranties", "Withholdings", </details> #### unfair_tos - `text`: a `string` feature (a ToS sentence) - `labels`: a list of classification labels (a list of unfair types, if any). <details> <summary>List of unfair types</summary> "Limitation of liability", "Unilateral termination", "Unilateral change", "Content removal", "Contract by using", "Choice of law", "Jurisdiction", "Arbitration" </details> #### casehold - `context`: a `string` feature (a context sentence incl. a masked holding statement). - `holdings`: a list of `string` features (a list of candidate holding statements). - `label`: a classification label (the id of the original/correct holding). ### Data Splits <table> <tr><td>Dataset </td><td>Training</td><td>Development</td><td>Test</td><td>Total</td></tr> <tr><td>ECtHR (Task A)</td><td>9,000</td><td>1,000</td><td>1,000</td><td>11,000</td></tr> <tr><td>ECtHR (Task B)</td><td>9,000</td><td>1,000</td><td>1,000</td><td>11,000</td></tr> <tr><td>SCOTUS</td><td>5,000</td><td>1,400</td><td>1,400</td><td>7,800</td></tr> <tr><td>EUR-LEX</td><td>55,000</td><td>5,000</td><td>5,000</td><td>65,000</td></tr> <tr><td>LEDGAR</td><td>60,000</td><td>10,000</td><td>10,000</td><td>80,000</td></tr> <tr><td>UNFAIR-ToS</td><td>5,532</td><td>2,275</td><td>1,607</td><td>9,414</td></tr> <tr><td>CaseHOLD</td><td>45,000</td><td>3,900</td><td>3,900</td><td>52,800</td></tr> </table> ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data <table> <tr><td>Dataset</td><td>Source</td><td>Sub-domain</td><td>Task Type</td><tr> <tr><td>ECtHR (Task A)</td><td> <a href="https://aclanthology.org/P19-1424/">Chalkidis et al. (2019)</a> </td><td>ECHR</td><td>Multi-label classification</td></tr> <tr><td>ECtHR (Task B)</td><td> <a href="https://aclanthology.org/2021.naacl-main.22/">Chalkidis et al. (2021a)</a> </td><td>ECHR</td><td>Multi-label classification </td></tr> <tr><td>SCOTUS</td><td> <a href="http://scdb.wustl.edu">Spaeth et al. (2020)</a></td><td>US Law</td><td>Multi-class classification</td></tr> <tr><td>EUR-LEX</td><td> <a href="https://arxiv.org/abs/2109.00904">Chalkidis et al. (2021b)</a></td><td>EU Law</td><td>Multi-label classification</td></tr> <tr><td>LEDGAR</td><td> <a href="https://aclanthology.org/2020.lrec-1.155/">Tuggener et al. (2020)</a></td><td>Contracts</td><td>Multi-class classification</td></tr> <tr><td>UNFAIR-ToS</td><td><a href="https://arxiv.org/abs/1805.01217"> Lippi et al. (2019)</a></td><td>Contracts</td><td>Multi-label classification</td></tr> <tr><td>CaseHOLD</td><td><a href="https://arxiv.org/abs/2104.08671">Zheng et al. (2021)</a></td><td>US Law</td><td>Multiple choice QA</td></tr> </table> #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Dataset Curators *Ilias Chalkidis, Abhik Jana, Dirk Hartung, Michael Bommarito, Ion Androutsopoulos, Daniel Martin Katz, and Nikolaos Aletras.* *LexGLUE: A Benchmark Dataset for Legal Language Understanding in English.* *2022. In the Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. Dublin, Ireland.* ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information [*Ilias Chalkidis, Abhik Jana, Dirk Hartung, Michael Bommarito, Ion Androutsopoulos, Daniel Martin Katz, and Nikolaos Aletras.* *LexGLUE: A Benchmark Dataset for Legal Language Understanding in English.* *2022. In the Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. Dublin, Ireland.*](https://arxiv.org/abs/2110.00976) ``` @inproceedings{chalkidis-etal-2021-lexglue, title={LexGLUE: A Benchmark Dataset for Legal Language Understanding in English}, author={Chalkidis, Ilias and Jana, Abhik and Hartung, Dirk and Bommarito, Michael and Androutsopoulos, Ion and Katz, Daniel Martin and Aletras, Nikolaos}, year={2022}, booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics}, address={Dubln, Ireland}, } ``` ### Contributions Thanks to [@iliaschalkidis](https://github.com/iliaschalkidis) for adding this dataset.

ought/raft

ought

--- annotations_creators: - expert-generated - crowdsourced language_creators: - expert-generated language: - en license: - other multilinguality: - monolingual size_categories: - unknown source_datasets: - original - extended|ade_corpus_v2 - extended|banking77 task_categories: - text-classification task_ids: - multi-class-classification pretty_name: 'Real-world Annotated Few-shot Tasks: RAFT' language_bcp47: - en-US --- # Dataset Card for RAFT ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://raft.elicit.org - **Repository:** https://huggingface.co/datasets/ought/raft - **Paper:** [arxiv.org](https://arxiv.org/abs/2109.14076) - **Leaderboard:** https://huggingface.co/spaces/ought/raft-leaderboard - **Point of Contact:** [Eli Lifland](eli.d.lifland@gmail.com) ### Dataset Summary The Real-world Annotated Few-shot Tasks (RAFT) dataset is an aggregation of English-language datasets found in the real world. Associated with each dataset is a binary or multiclass classification task, intended to improve our understanding of how language models perform on tasks that have concrete, real-world value. Only 50 labeled examples are provided in each dataset. ### Supported Tasks and Leaderboards - `text-classification`: Each subtask in RAFT is a text classification task, and the provided train and test sets can be used to submit to the [RAFT Leaderboard](https://huggingface.co/spaces/ought/raft-leaderboard) To prevent overfitting and tuning on a held-out test set, the leaderboard is only evaluated once per week. Each task has its macro-f1 score calculated, then those scores are averaged to produce the overall leaderboard score. ### Languages RAFT is entirely in American English (en-US). ## Dataset Structure ### Data Instances | Dataset | First Example | | ----------- | ----------- | | Ade Corpus V2 | <pre>Sentence: No regional side effects were noted.<br>ID: 0<br>Label: 2</pre> | | Banking 77 | <pre>Query: Is it possible for me to change my PIN number?<br>ID: 0<br>Label: 23<br></pre> | | NeurIPS Impact Statement Risks | <pre>Paper title: Auto-Panoptic: Cooperative Multi-Component Architecture Search for Panoptic Segmentation...<br>Paper link: https://proceedings.neurips.cc/paper/2020/file/ec1f764517b7ffb52057af6df18142b7-Paper.pdf...<br>Impact statement: This work makes the first attempt to search for all key components of panoptic pipeline and manages to accomplish this via the p...<br>ID: 0<br>Label: 1</pre> | | One Stop English | <pre>Article: For 85 years, it was just a grey blob on classroom maps of the solar system. But, on 15 July, Pluto was seen in high resolution ...<br>ID: 0<br>Label: 3<br></pre> | | Overruling | <pre>Sentence: in light of both our holding today and previous rulings in johnson, dueser, and gronroos, we now explicitly overrule dupree....<br>ID: 0<br>Label: 2<br></pre> | | Semiconductor Org Types | <pre>Paper title: 3Gb/s AC-coupled chip-to-chip communication using a low-swing pulse receiver...<br>Organization name: North Carolina State Univ.,Raleigh,NC,USA<br>ID: 0<br>Label: 3<br></pre> | | Systematic Review Inclusion | <pre>Title: Prototyping and transforming facial textures for perception research...<br>Abstract: Wavelet based methods for prototyping facial textures for artificially transforming the age of facial images were described. Pro...<br>Authors: Tiddeman, B.; Burt, M.; Perrett, D.<br>Journal: IEEE Comput Graphics Appl<br>ID: 0<br>Label: 2</pre> | | TAI Safety Research | <pre>Title: Malign generalization without internal search<br>Abstract Note: In my last post, I challenged the idea that inner alignment failures should be explained by appealing to agents which perform ex...<br>Url: https://www.alignmentforum.org/posts/ynt9TD6PrYw6iT49m/malign-generalization-without-internal-search...<br>Publication Year: 2020<br>Item Type: blogPost<br>Author: Barnett, Matthew<br>Publication Title: AI Alignment Forum<br>ID: 0<br>Label: 1</pre> | | Terms Of Service | <pre>Sentence: Crowdtangle may change these terms of service, as described above, notwithstanding any provision to the contrary in any agreemen...<br>ID: 0<br>Label: 2<br></pre> | | Tweet Eval Hate | <pre>Tweet: New to Twitter-- any men on here know what the process is to get #verified?...<br>ID: 0<br>Label: 2<br></pre> | | Twitter Complaints | <pre>Tweet text: @HMRCcustomers No this is my first job<br>ID: 0<br>Label: 2</pre> | ### Data Fields The ID field is used for indexing data points. It will be used to match your submissions with the true test labels, so you must include it in your submission. All other columns contain textual data. Some contain links and URLs to websites on the internet. All output fields are designated with the "Label" column header. The 0 value in this column indicates that the entry is unlabeled, and should only appear in the unlabeled test set. Other values in this column are various other labels. To get their textual value for a given dataset: ``` # Load the dataset dataset = datasets.load_dataset("ought/raft", "ade_corpus_v2") # First, get the object that holds information about the "Label" feature in the dataset. label_info = dataset.features["Label"] # Use the int2str method to access the textual labels. print([label_info.int2str(i) for i in (0, 1, 2)]) # ['Unlabeled', 'ADE-related', 'not ADE-related'] ``` ### Data Splits There are two splits provided: train data and unlabeled test data. The training examples were chosen at random. No attempt was made to ensure that classes were balanced or proportional in the training data -- indeed, the Banking 77 task with 77 different classes if used cannot fit all of its classes into the 50 training examples. | Dataset | Train Size | Test Size | | |--------------------------------|------------|-----------|---| | Ade Corpus V2 | 50 | 5000 | | | Banking 77 | 50 | 5000 | | | NeurIPS Impact Statement Risks | 50 | 150 | | | One Stop English | 50 | 516 | | | Overruling | 50 | 2350 | | | Semiconductor Org Types | 50 | 449 | | | Systematic Review Inclusion | 50 | 2243 | | | TAI Safety Research | 50 | 1639 | | | Terms Of Service | 50 | 5000 | | | Tweet Eval Hate | 50 | 2966 | | | Twitter Complaints | 50 | 3399 | | | **Total** | **550** | **28712** | | ## Dataset Creation ### Curation Rationale Generally speaking, the rationale behind RAFT was to create a benchmark for evaluating NLP models that didn't consist of contrived or artificial data sources, for which the tasks weren't originally assembled for the purpose of testing NLP models. However, each individual dataset in RAFT was collected independently. For the majority of datasets, we only collected them second-hand from existing curated sources. The datasets that we curated are: * NeurIPS impact statement risks * Semiconductor org types * TAI Safety Research Each of these three datasets was sourced from our existing collaborators at Ought. They had used our service, Elicit, to analyze their dataset in the past, and we contact them to include their dataset and the associated classification task in the benchmark. For all datasets, more information is provided in our paper. For the ones which we did not curate, we provide a link to the dataset. For the ones which we did, we provide a datasheet that elaborates on many of the topics here in greater detail. For the three datasets that we introduced: * **NeurIPS impact statement risks** The dataset was created to evaluate the then new requirement for authors to include an "impact statement" in their 2020 NeurIPS papers. Had it been successful? What kind of things did authors mention the most? How long were impact statements on average? Etc. * **Semiconductor org types** The dataset was originally created to understand better which countries’ organisations have contributed most to semiconductor R\&D over the past 25 years using three main conferences. Moreover, to estimate the share of academic and private sector contributions, the organisations were classified as “university”, “research institute” or “company”. * **TAI Safety Research** The primary motivations for assembling this database were to: (1) Aid potential donors in assessing organizations focusing on TAI safety by collecting and analyzing their research output. (2) Assemble a comprehensive bibliographic database that can be used as a base for future projects, such as a living review of the field. **For the following sections, we will only describe the datasets we introduce. All other dataset details, and more details on the ones described here, can be found in our paper.** ### Source Data #### Initial Data Collection and Normalization * **NeurIPS impact statement risks** The data was directly observable (raw text scraped) for the most part; although some data was taken from previous datasets (which themselves had taken it from raw text). The data was validated, but only in part, by human reviewers. Cf this link for full details: * **Semiconductor org types** We used the IEEE API to obtain institutions that contributed papers to semiconductor conferences in the last 25 years. This is a random sample of 500 of them with a corresponding conference paper title. The three conferences were the International Solid-State Circuits Conference (ISSCC), the Symposia on VLSI Technology and Circuits (VLSI) and the International Electron Devices Meeting (IEDM). * **TAI Safety Research** We asked TAI safety organizations for what their employees had written, emailed some individual authors, and searched Google Scholar. See the LessWrong post for more details: https://www.lesswrong.com/posts/4DegbDJJiMX2b3EKm/tai-safety-bibliographic-database #### Who are the source language producers? * **NeurIPS impact statement risks** Language generated from NeurIPS 2020 impact statement authors, generally the authors of submission papers. * **Semiconductor org types** Language generated from IEEE API. Generally machine-formatted names, and title of academic papers. * **TAI Safety Research** Language generated by authors of TAI safety research publications. ### Annotations #### Annotation process * **NeurIPS impact statement risks** Annotations were entered directly into a Google Spreadsheet with instructions, labeled training examples, and unlabeled testing examples. * **Semiconductor org types** Annotations were entered directly into a Google Spreadsheet with instructions, labeled training examples, and unlabeled testing examples. * **TAI Safety Research** N/A #### Who are the annotators? * **NeurIPS impact statement risks** Contractors paid by Ought performed the labeling of whether impact statements mention harmful applications. A majority vote was taken from 3 annotators. * **Semiconductor org types** Contractors paid by Ought performed the labeling of organization types. A majority vote was taken from 3 annotators. * **TAI Safety Research** The dataset curators annotated the dataset by hand. ### Personal and Sensitive Information It is worth mentioning that the Tweet Eval Hate, by necessity, contains highly offensive content. * **NeurIPS impact statement risks** The dataset contains authors' names. These were scraped from publicly available scientific papers submitted to NeurIPS 2020. * **Semiconductor org types** N/A * **TAI Safety Research** N/A ## Considerations for Using the Data ### Social Impact of Dataset * **NeurIPS impact statement risks** N/A * **Semiconductor org types** N/A * **TAI Safety Research** N/A ### Discussion of Biases * **NeurIPS impact statement risks** N/A * **Semiconductor org types** N/A * **TAI Safety Research** N/A ### Other Known Limitations * **NeurIPS impact statement risks** This dataset has limitations that should be taken into consideration when using it. In particular, the method used to collect broader impact statements involved automated downloads, conversions and scraping and was not error-proof. Although care has been taken to identify and correct as many errors as possible, not all texts have been reviewed by a human. This means it is possible some of the broader impact statements contained in the dataset are truncated or otherwise incorrectly extracted from their original article. * **Semiconductor org types** N/A * **TAI Safety Research** Don't use it to create a dangerous AI that could bring the end of days. ## Additional Information ### Dataset Curators The overall RAFT curators are Neel Alex, Eli Lifland, and Andreas Stuhlmüller. * **NeurIPS impact statement risks** Volunteers working with researchers affiliated to Oxford's Future of Humanity Institute (Carolyn Ashurst, now at The Alan Turing Institute) created the impact statements dataset. * **Semiconductor org types** The data science unit of Stiftung Neue Verantwortung (Berlin). * **TAI Safety Research** Angelica Deibel and Jess Riedel. We did not do it on behalf of any entity. ### Licensing Information RAFT aggregates many other datasets, each of which is provided under its own license. Generally, those licenses permit research and commercial use. | Dataset | License | | ----------- | ----------- | | Ade Corpus V2 | Unlicensed | | Banking 77 | CC BY 4.0 | | NeurIPS Impact Statement Risks | MIT License/CC BY 4.0 | | One Stop English | CC BY-SA 4.0 | | Overruling | Unlicensed | | Semiconductor Org Types | CC BY-NC 4.0 | | Systematic Review Inclusion | CC BY 4.0 | | TAI Safety Research | CC BY-SA 4.0 | | Terms Of Service | Unlicensed | | Tweet Eval Hate | Unlicensed | | Twitter Complaints | Unlicensed | ### Citation Information [More Information Needed] ### Contributions Thanks to [@neel-alex](https://github.com/neel-alex), [@uvafan](https://github.com/uvafan), and [@lewtun](https://github.com/lewtun) for adding this dataset.

gsarti/flores_101

gsarti

--- annotations_creators: - found language_creators: - expert-generated language: - af - am - ar - hy - as - ast - az - be - bn - bs - bg - my - ca - ceb - zho - hr - cs - da - nl - en - et - tl - fi - fr - ff - gl - lg - ka - de - el - gu - ha - he - hi - hu - is - ig - id - ga - it - ja - jv - kea - kam - kn - kk - km - ko - ky - lo - lv - ln - lt - luo - lb - mk - ms - ml - mt - mi - mr - mn - ne - ns - 'no' - ny - oc - or - om - ps - fa - pl - pt - pa - ro - ru - sr - sn - sd - sk - sl - so - ku - es - sw - sv - tg - ta - te - th - tr - uk - umb - ur - uz - vi - cy - wo - xh - yo - zu license: - cc-by-sa-4.0 multilinguality: - multilingual - translation size_categories: - unknown source_datasets: - extended|flores task_categories: - text-generation - translation task_ids: [] paperswithcode_id: flores pretty_name: flores101 tags: - conditional-text-generation --- # Dataset Card for Flores 101 ## Table of Contents - [Dataset Card for Flores 101](#dataset-card-for-flores-101) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - **Home:** [WMT](http://www.statmt.org/wmt21/large-scale-multilingual-translation-task.html) - **Repository:** [Github](https://github.com/facebookresearch/flores) - **Blogpost:** [FAIR](https://ai.facebook.com/blog/the-flores-101-data-set-helping-build-better-translation-systems-around-the-world) - **Paper:** [Arxiv](https://arxiv.org/abs/2106.03193) - **Point of Contact:** [flores@fb.com](mailto:flores@fb.com) - **Leaderboard** [Dynabench](https://dynabench.org/flores/Flores%20MT%20Evaluation%20(FULL)) ### Dataset Summary FLORES is a benchmark dataset for machine translation between English and low-resource languages. Abstract from the original paper: > One of the biggest challenges hindering progress in low-resource and multilingual machine translation is the lack of good evaluation benchmarks. Current evaluation benchmarks either lack good coverage of low-resource languages, consider only restricted domains, or are low quality because they are constructed using semi-automatic procedures. In this work, we introduce the FLORES evaluation benchmark, consisting of 3001 sentences extracted from English Wikipedia and covering a variety of different topics and domains. These sentences have been translated in 101 languages by professional translators through a carefully controlled process. The resulting dataset enables better assessment of model quality on the long tail of low-resource languages, including the evaluation of many-to-many multilingual translation systems, as all translations are multilingually aligned. By publicly releasing such a high-quality and high-coverage dataset, we hope to foster progress in the machine translation community and beyond. **Disclaimer**: *The Flores-101 dataset is hosted by the Facebook and licensed under the [Creative Commons Attribution-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-sa/4.0/). ### Supported Tasks and Leaderboards #### Multilingual Machine Translation Refer to the [Dynabench leaderboard](https://dynabench.org/flores/Flores%20MT%20Evaluation%20(FULL)) for additional details on model evaluation on FLORES-101 in the context of the WMT2021 shared task on [Large-Scale Multilingual Machine Translation](http://www.statmt.org/wmt21/large-scale-multilingual-translation-task.html). ### Languages The dataset contains parallel sentences for 101 languages, as mentioned in the original [Github](https://github.com/facebookresearch/flores/blob/master/README.md) page for the project. Languages are identified with the ISO 639-3 code (e.g. `eng`, `fra`, `rus`) as in the original dataset. **New:** Use the configuration `all` to access the full set of parallel sentences for all the available languages in a single command. ## Dataset Structure ### Data Instances A sample from the `dev` split for the Russian language (`rus` config) is provided below. All configurations have the same structure, and all sentences are aligned across configurations and splits. ```python { 'id': 1, 'sentence': 'В понедельник ученые из Медицинской школы Стэнфордского университета объявили об изобретении нового диагностического инструмента, который может сортировать клетки по их типу; это маленький чип, который можно напечатать, используя стандартный струйный принтер примерно за 1 цент США.', 'URL': 'https://en.wikinews.org/wiki/Scientists_say_new_medical_diagnostic_chip_can_sort_cells_anywhere_with_an_inkjet', 'domain': 'wikinews', 'topic': 'health', 'has_image': 0, 'has_hyperlink': 0 } ``` The text is provided as-in the original dataset, without further preprocessing or tokenization. ### Data Fields - `id`: Row number for the data entry, starting at 1. - `sentence`: The full sentence in the specific language. - `URL`: The URL for the English article from which the sentence was extracted. - `domain`: The domain of the sentence. - `topic`: The topic of the sentence. - `has_image`: Whether the original article contains an image. - `has_hyperlink`: Whether the sentence contains a hyperlink. ### Data Splits | config| `dev`| `devtest`| |-----------------:|-----:|---------:| |all configurations| 997| 1012:| ### Dataset Creation Please refer to the original article [The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation](https://arxiv.org/abs/2106.03193) for additional information on dataset creation. ## Additional Information ### Dataset Curators The original authors of FLORES-101 are the curators of the original dataset. For problems or updates on this 🤗 Datasets version, please contact [gabriele.sarti996@gmail.com](mailto:gabriele.sarti996@gmail.com). ### Licensing Information Licensed with Creative Commons Attribution Share Alike 4.0. License available [here](https://creativecommons.org/licenses/by-sa/4.0/). ### Citation Information Please cite the authors if you use these corpora in your work: ```bibtex @inproceedings{flores101, title={The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation}, author={Goyal, Naman and Gao, Cynthia and Chaudhary, Vishrav and Chen, Peng-Jen and Wenzek, Guillaume and Ju, Da and Krishnan, Sanjana and Ranzato, Marc'Aurelio and Guzm\'{a}n, Francisco and Fan, Angela}, journal={arXiv preprint arXiv:2106.03193}, year={2021} } ```

lvwerra/stack-exchange-paired

lvwerra

--- task_categories: - text-generation - question-answering language: - en pretty_name: StackExchange Paired size_categories: - 10M<n<100M --- # StackExchange Paired This is a processed version of the [`HuggingFaceH4/stack-exchange-preferences`](https://huggingface.co/datasets/HuggingFaceH4/stack-exchange-preferences). The following steps were applied: - Parse HTML to Markdown with `markdownify` - Create pairs `(response_j, response_k)` where j was rated better than k - Sample at most 10 pairs per question - Shuffle the dataset globally This dataset is designed to be used for preference learning. The processing notebook is in [the repository](https://huggingface.co/datasets/lvwerra/stack-exchange-paired/tree/main) as well.

universal_morphologies

--- annotations_creators: - expert-generated language_creators: - found language: - ady - ang - ar - arn - ast - az - ba - be - bg - bn - bo - br - ca - ckb - crh - cs - csb - cu - cy - da - de - dsb - el - en - es - et - eu - fa - fi - fo - fr - frm - fro - frr - fur - fy - ga - gal - gd - gmh - gml - got - grc - gv - hai - he - hi - hu - hy - is - it - izh - ka - kbd - kjh - kk - kl - klr - kmr - kn - krl - kw - la - liv - lld - lt - lud - lv - mk - mt - mwf - nap - nb - nds - nl - nn - nv - oc - olo - osx - pl - ps - pt - qu - ro - ru - sa - sga - sh - sl - sme - sq - sv - swc - syc - te - tg - tk - tr - tt - uk - ur - uz - vec - vep - vot - xcl - xno - yi - zu license: - cc-by-sa-3.0 multilinguality: - monolingual size_categories: - 10K<n<100K - 1K<n<10K - n<1K source_datasets: - original task_categories: - token-classification - text-classification task_ids: - multi-class-classification - multi-label-classification paperswithcode_id: null pretty_name: UniversalMorphologies tags: - morphology dataset_info: - config_name: ady features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 3428235 num_examples: 1666 download_size: 1008487 dataset_size: 3428235 - config_name: ang features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 6569844 num_examples: 1867 download_size: 1435972 dataset_size: 6569844 - config_name: ara features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 24388295 num_examples: 4134 download_size: 7155824 dataset_size: 24388295 - config_name: arn features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 124050 num_examples: 26 download_size: 20823 dataset_size: 124050 - config_name: ast features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 4913008 num_examples: 436 download_size: 1175901 dataset_size: 4913008 - config_name: aze features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 1248687 num_examples: 340 download_size: 276306 dataset_size: 1248687 - config_name: bak features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 1984657 num_examples: 1084 download_size: 494758 dataset_size: 1984657 - config_name: bel features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 2626405 num_examples: 1027 download_size: 739537 dataset_size: 2626405 - config_name: ben features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 746181 num_examples: 136 download_size: 251991 dataset_size: 746181 - config_name: bod features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 880074 num_examples: 1335 download_size: 197523 dataset_size: 880074 - config_name: bre features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 387583 num_examples: 44 download_size: 82159 dataset_size: 387583 - config_name: bul features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 9589915 num_examples: 2468 download_size: 3074574 dataset_size: 9589915 - config_name: cat features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 12988492 num_examples: 1547 download_size: 2902458 dataset_size: 12988492 - config_name: ces features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 21056640 num_examples: 5125 download_size: 4875288 dataset_size: 21056640 - config_name: chu features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 628237 num_examples: 152 download_size: 149081 dataset_size: 628237 - config_name: ckb features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 3843267 num_examples: 274 download_size: 914302 dataset_size: 3843267 - config_name: cor features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 83434 num_examples: 9 download_size: 17408 dataset_size: 83434 - config_name: crh features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 1154595 num_examples: 1230 download_size: 186325 dataset_size: 1154595 - config_name: csb features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 82172 num_examples: 37 download_size: 14259 dataset_size: 82172 - config_name: cym features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 1748431 num_examples: 183 download_size: 374501 dataset_size: 1748431 - config_name: dan features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 4204551 num_examples: 3193 download_size: 845939 dataset_size: 4204551 - config_name: deu features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 28436466 num_examples: 15060 download_size: 5966618 dataset_size: 28436466 - config_name: dsb features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 2985168 num_examples: 994 download_size: 536096 dataset_size: 2985168 - config_name: ell features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 34112450 num_examples: 11906 download_size: 11222248 dataset_size: 34112450 - config_name: eng features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 18455909 num_examples: 22765 download_size: 3285554 dataset_size: 18455909 - config_name: est features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 6125879 num_examples: 886 download_size: 1397385 dataset_size: 6125879 - config_name: eus features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 2444247 num_examples: 26 download_size: 876480 dataset_size: 2444247 - config_name: fao features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 7117926 num_examples: 3077 download_size: 1450065 dataset_size: 7117926 - config_name: fas features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 6382709 num_examples: 273 download_size: 2104724 dataset_size: 6382709 - config_name: fin features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: '1' num_bytes: 331855860 num_examples: 46152 - name: '2' num_bytes: 81091817 num_examples: 11491 download_size: 109324828 dataset_size: 412947677 - config_name: fra features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 58747699 num_examples: 7535 download_size: 13404983 dataset_size: 58747699 - config_name: frm features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 6015940 num_examples: 603 download_size: 1441122 dataset_size: 6015940 - config_name: fro features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 20260793 num_examples: 1700 download_size: 4945582 dataset_size: 20260793 - config_name: frr features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 526898 num_examples: 51 download_size: 112236 dataset_size: 526898 - config_name: fry features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 222067 num_examples: 85 download_size: 38227 dataset_size: 222067 - config_name: fur features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 1282374 num_examples: 168 download_size: 258793 dataset_size: 1282374 - config_name: gal features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 5844604 num_examples: 486 download_size: 1259120 dataset_size: 5844604 - config_name: gla features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 126847 num_examples: 73 download_size: 25025 dataset_size: 126847 - config_name: gle features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 17065939 num_examples: 7464 download_size: 3853188 dataset_size: 17065939 - config_name: glv features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 7523 num_examples: 1 download_size: 401 dataset_size: 7523 - config_name: gmh features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 114677 num_examples: 29 download_size: 20851 dataset_size: 114677 - config_name: gml features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 233831 num_examples: 52 download_size: 47151 dataset_size: 233831 - config_name: got features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train download_size: 2 dataset_size: 0 - config_name: grc features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 6779867 num_examples: 2431 download_size: 2057514 dataset_size: 6779867 - config_name: hai features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 1166240 num_examples: 41 download_size: 329817 dataset_size: 1166240 - config_name: hbs features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 132933961 num_examples: 24419 download_size: 32194142 dataset_size: 132933961 - config_name: heb features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 2211208 num_examples: 510 download_size: 498065 dataset_size: 2211208 - config_name: hin features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 10083004 num_examples: 258 download_size: 3994359 dataset_size: 10083004 - config_name: hun features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 83517327 num_examples: 14892 download_size: 19544319 dataset_size: 83517327 - config_name: hye features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 56537127 num_examples: 7033 download_size: 17810316 dataset_size: 56537127 - config_name: isl features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 12120572 num_examples: 4775 download_size: 2472980 dataset_size: 12120572 - config_name: ita features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 81905203 num_examples: 10009 download_size: 19801423 dataset_size: 81905203 - config_name: izh features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 170094 num_examples: 50 download_size: 28558 dataset_size: 170094 - config_name: kal features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 60434 num_examples: 23 download_size: 9795 dataset_size: 60434 - config_name: kan features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 1052294 num_examples: 159 download_size: 318512 dataset_size: 1052294 - config_name: kat features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 12532540 num_examples: 3782 download_size: 4678979 dataset_size: 12532540 - config_name: kaz features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 62519 num_examples: 26 download_size: 14228 dataset_size: 62519 - config_name: kbd features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 511406 num_examples: 250 download_size: 133788 dataset_size: 511406 - config_name: kjh features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 193741 num_examples: 75 download_size: 44907 dataset_size: 193741 - config_name: klr features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 28909688 num_examples: 591 download_size: 7561829 dataset_size: 28909688 - config_name: kmr features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 35504487 num_examples: 15083 download_size: 8592722 dataset_size: 35504487 - config_name: krl features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 106475 num_examples: 20 download_size: 19024 dataset_size: 106475 - config_name: lat features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 81932667 num_examples: 17214 download_size: 19567252 dataset_size: 81932667 - config_name: lav features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 21219584 num_examples: 7548 download_size: 5048680 dataset_size: 21219584 - config_name: lit features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 5287268 num_examples: 1458 download_size: 1191554 dataset_size: 5287268 - config_name: liv features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 642166 num_examples: 203 download_size: 141467 dataset_size: 642166 - config_name: lld features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 1240257 num_examples: 180 download_size: 278592 dataset_size: 1240257 - config_name: lud features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: mikhailovskoye num_bytes: 11361 num_examples: 2 - name: new_written num_bytes: 35132 num_examples: 94 - name: southern_ludian_svjatozero num_bytes: 57276 num_examples: 71 download_size: 14697 dataset_size: 103769 - config_name: mkd features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 27800390 num_examples: 10313 download_size: 8157589 dataset_size: 27800390 - config_name: mlt features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 604577 num_examples: 112 download_size: 124584 dataset_size: 604577 - config_name: mwf features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 172890 num_examples: 29 download_size: 25077 dataset_size: 172890 - config_name: nap features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 293699 num_examples: 40 download_size: 64163 dataset_size: 293699 - config_name: nav features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 2051393 num_examples: 674 download_size: 523673 dataset_size: 2051393 - config_name: nds features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train download_size: 2 dataset_size: 0 - config_name: nld features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 8813867 num_examples: 4993 download_size: 1874427 dataset_size: 8813867 - config_name: nno features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 2704566 num_examples: 4689 download_size: 420695 dataset_size: 2704566 - config_name: nob features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 3359706 num_examples: 5527 download_size: 544432 dataset_size: 3359706 - config_name: oci features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 1327716 num_examples: 174 download_size: 276611 dataset_size: 1327716 - config_name: olo features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: kotkozero num_bytes: 7682 num_examples: 5 - name: new_written num_bytes: 11158424 num_examples: 15293 - name: syamozero num_bytes: 6379 num_examples: 2 - name: vedlozero num_bytes: 6120 num_examples: 1 - name: vidlitsa num_bytes: 54363 num_examples: 3 download_size: 2130154 dataset_size: 11232968 - config_name: osx features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 3500590 num_examples: 863 download_size: 759997 dataset_size: 3500590 - config_name: pol features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 30855235 num_examples: 10185 download_size: 6666266 dataset_size: 30855235 - config_name: por features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 48530106 num_examples: 4001 download_size: 10982524 dataset_size: 48530106 - config_name: pus features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 1176421 num_examples: 395 download_size: 297043 dataset_size: 1176421 - config_name: que features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 27823298 num_examples: 1006 download_size: 6742890 dataset_size: 27823298 - config_name: ron features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 13187957 num_examples: 4405 download_size: 2990521 dataset_size: 13187957 - config_name: rus features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 77484460 num_examples: 28068 download_size: 25151401 dataset_size: 77484460 - config_name: san features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 5500001 num_examples: 917 download_size: 1788739 dataset_size: 5500001 - config_name: sga features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 190479 num_examples: 49 download_size: 43469 dataset_size: 190479 - config_name: slv features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 9071547 num_examples: 2535 download_size: 1911039 dataset_size: 9071547 - config_name: sme features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 9764653 num_examples: 2103 download_size: 2050015 dataset_size: 9764653 - config_name: spa features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 61472202 num_examples: 5460 download_size: 14386131 dataset_size: 61472202 - config_name: sqi features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 5422400 num_examples: 589 download_size: 1261468 dataset_size: 5422400 - config_name: swc features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 1694529 num_examples: 100 download_size: 414624 dataset_size: 1694529 - config_name: swe features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 12897827 num_examples: 10553 download_size: 2709960 dataset_size: 12897827 - config_name: syc features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 553392 num_examples: 160 download_size: 130000 dataset_size: 553392 - config_name: tat features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 1203356 num_examples: 1283 download_size: 194277 dataset_size: 1203356 - config_name: tel features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 285769 num_examples: 127 download_size: 95069 dataset_size: 285769 - config_name: tgk features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 25276 num_examples: 75 download_size: 2366 dataset_size: 25276 - config_name: tuk features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 127712 num_examples: 68 download_size: 20540 dataset_size: 127712 - config_name: tur features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 44723850 num_examples: 3579 download_size: 11552946 dataset_size: 44723850 - config_name: ukr features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 3299187 num_examples: 1493 download_size: 870660 dataset_size: 3299187 - config_name: urd features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 2197237 num_examples: 182 download_size: 685613 dataset_size: 2197237 - config_name: uzb features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 196802 num_examples: 15 download_size: 41921 dataset_size: 196802 - config_name: vec features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 2892987 num_examples: 368 download_size: 615931 dataset_size: 2892987 - config_name: vep features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: central_eastern num_bytes: 500981 num_examples: 65 - name: central_western num_bytes: 2527618 num_examples: 111 - name: new_written num_bytes: 79899484 num_examples: 9304 - name: northern num_bytes: 175242 num_examples: 21 - name: southern num_bytes: 206289 num_examples: 17 download_size: 20131151 dataset_size: 83309614 - config_name: vot features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 217663 num_examples: 55 download_size: 37179 dataset_size: 217663 - config_name: xcl features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 16856327 num_examples: 4300 download_size: 4950513 dataset_size: 16856327 - config_name: xno features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 48938 num_examples: 5 download_size: 9641 dataset_size: 48938 - config_name: yid features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 1409582 num_examples: 803 download_size: 429391 dataset_size: 1409582 - config_name: zul features: - name: lemma dtype: string - name: forms sequence: - name: word dtype: string - name: Aktionsart sequence: class_label: names: 0: STAT 1: DYN 2: TEL 3: ATEL 4: PCT 5: DUR 6: ACH 7: ACCMP 8: SEMEL 9: ACTY - name: Animacy sequence: class_label: names: 0: ANIM 1: INAN 2: HUM 3: NHUM - name: Argument_Marking sequence: class_label: names: 0: ARGNO1S 1: ARGNO2S 2: ARGNO3S 3: ARGNO1P 4: ARGNO2P 5: ARGNO3P 6: ARGAC1S 7: ARGAC2S 8: ARGAC3S 9: ARGAC1P 10: ARGAC2P 11: ARGAC3P 12: ARGAB1S 13: ARGAB2S 14: ARGAB3S 15: ARGAB1P 16: ARGAB2P 17: ARGAB3P 18: ARGER1S 19: ARGER2S 20: ARGER3S 21: ARGER1P 22: ARGER2P 23: ARGER3P 24: ARGDA1S 25: ARGDA2S 26: ARGDA3S 27: ARGDA1P 28: ARGDA2P 29: ARGDA3P 30: ARGBE1S 31: ARGBE2S 32: ARGBE3S 33: ARGBE1P 34: ARGBE2P 35: ARGBE3P - name: Aspect sequence: class_label: names: 0: IPFV 1: PFV 2: PRF 3: PROG 4: PROSP 5: ITER 6: HAB - name: Case sequence: class_label: names: 0: NOM 1: ACC 2: ERG 3: ABS 4: NOMS 5: DAT 6: BEN 7: PRP 8: GEN 9: REL 10: PRT 11: INS 12: COM 13: VOC 14: COMPV 15: EQTV 16: PRIV 17: PROPR 18: AVR 19: FRML 20: TRANS 21: BYWAY 22: INTER 23: AT 24: POST 25: IN 26: CIRC 27: ANTE 28: APUD 29: 'ON' 30: ONHR 31: ONVR 32: SUB 33: REM 34: PROXM 35: ESS 36: ALL 37: ABL 38: APPRX 39: TERM - name: Comparison sequence: class_label: names: 0: CMPR 1: SPRL 2: AB 3: RL 4: EQT - name: Definiteness sequence: class_label: names: 0: DEF 1: INDF 2: SPEC 3: NSPEC - name: Deixis sequence: class_label: names: 0: PROX 1: MED 2: REMT 3: REF1 4: REF2 5: NOREF 6: PHOR 7: VIS 8: NVIS 9: ABV 10: EVEN 11: BEL - name: Evidentiality sequence: class_label: names: 0: FH 1: DRCT 2: SEN 3: VISU 4: NVSEN 5: AUD 6: NFH 7: QUOT 8: RPRT 9: HRSY 10: INFER 11: ASSUM - name: Finiteness sequence: class_label: names: 0: FIN 1: NFIN - name: Gender sequence: class_label: names: 0: MASC 1: FEM 2: NEUT 3: NAKH1 4: NAKH2 5: NAKH3 6: NAKH4 7: NAKH5 8: NAKH6 9: NAKH7 10: NAKH8 11: BANTU1 12: BANTU2 13: BANTU3 14: BANTU4 15: BANTU5 16: BANTU6 17: BANTU7 18: BANTU8 19: BANTU9 20: BANTU10 21: BANTU11 22: BANTU12 23: BANTU13 24: BANTU14 25: BANTU15 26: BANTU16 27: BANTU17 28: BANTU18 29: BANTU19 30: BANTU20 31: BANTU21 32: BANTU22 33: BANTU23 - name: Information_Structure sequence: class_label: names: 0: TOP 1: FOC - name: Interrogativity sequence: class_label: names: 0: DECL 1: INT - name: Language_Specific sequence: class_label: names: 0: LGSPEC1 1: LGSPEC2 2: LGSPEC3 3: LGSPEC4 4: LGSPEC5 5: LGSPEC6 6: LGSPEC7 7: LGSPEC8 8: LGSPEC9 9: LGSPEC10 - name: Mood sequence: class_label: names: 0: IND 1: SBJV 2: REAL 3: IRR 4: AUPRP 5: AUNPRP 6: IMP 7: COND 8: PURP 9: INTEN 10: POT 11: LKLY 12: ADM 13: OBLIG 14: DEB 15: PERM 16: DED 17: SIM 18: OPT - name: Number sequence: class_label: names: 0: SG 1: PL 2: GRPL 3: DU 4: TRI 5: PAUC 6: GRPAUC 7: INVN - name: Part_Of_Speech sequence: class_label: names: 0: N 1: PROPN 2: ADJ 3: PRO 4: CLF 5: ART 6: DET 7: V 8: ADV 9: AUX 10: V.PTCP 11: V.MSDR 12: V.CVB 13: ADP 14: COMP 15: CONJ 16: NUM 17: PART 18: INTJ - name: Person sequence: class_label: names: 0: '0' 1: '1' 2: '2' 3: '3' 4: '4' 5: INCL 6: EXCL 7: PRX 8: OBV - name: Polarity sequence: class_label: names: 0: POS 1: NEG - name: Politeness sequence: class_label: names: 0: INFM 1: FORM 2: ELEV 3: HUMB 4: POL 5: AVOID 6: LOW 7: HIGH 8: STELEV 9: STSUPR 10: LIT 11: FOREG 12: COL - name: Possession sequence: class_label: names: 0: ALN 1: NALN 2: PSS1S 3: PSS2S 4: PSS2SF 5: PSS2SM 6: PSS2SINFM 7: PSS2SFORM 8: PSS3S 9: PSS3SF 10: PSS3SM 11: PSS1D 12: PSS1DI 13: PSS1DE 14: PSS2D 15: PSS2DM 16: PSS2DF 17: PSS3D 18: PSS3DF 19: PSS3DM 20: PSS1P 21: PSS1PI 22: PSS1PE 23: PSS2P 24: PSS2PF 25: PSS2PM 26: PSS3PF 27: PSS3PM - name: Switch_Reference sequence: class_label: names: 0: SS 1: SSADV 2: DS 3: DSADV 4: OR 5: SIMMA 6: SEQMA 7: LOG - name: Tense sequence: class_label: names: 0: PRS 1: PST 2: FUT 3: IMMED 4: HOD 5: 1DAY 6: RCT 7: RMT - name: Valency sequence: class_label: names: 0: IMPRS 1: INTR 2: TR 3: DITR 4: REFL 5: RECP 6: CAUS 7: APPL - name: Voice sequence: class_label: names: 0: ACT 1: MID 2: PASS 3: ANTIP 4: DIR 5: INV 6: AGFOC 7: PFOC 8: LFOC 9: BFOC 10: ACFOC 11: IFOC 12: CFOC - name: Other sequence: string splits: - name: train num_bytes: 7152507 num_examples: 566 download_size: 1581402 dataset_size: 7152507 config_names: - ady - ang - ara - arn - ast - aze - bak - bel - ben - bod - bre - bul - cat - ces - chu - ckb - cor - crh - csb - cym - dan - deu - dsb - ell - eng - est - eus - fao - fas - fin - fra - frm - fro - frr - fry - fur - gal - gla - gle - glv - gmh - gml - got - grc - hai - hbs - heb - hin - hun - hye - isl - ita - izh - kal - kan - kat - kaz - kbd - kjh - klr - kmr - krl - lat - lav - lit - liv - lld - lud - mkd - mlt - mwf - nap - nav - nds - nld - nno - nob - oci - olo - osx - pol - por - pus - que - ron - rus - san - sga - slv - sme - spa - sqi - swc - swe - syc - tat - tel - tgk - tuk - tur - ukr - urd - uzb - vec - vep - vot - xcl - xno - yid - zul --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [UniMorph Homepage](https://unimorph.github.io/) - **Repository:** [List of UniMorph repositories](https://github.com/unimorph) - **Paper:** [The Composition and Use of the Universal Morphological Feature Schema (UniMorph Schema)](https://unimorph.github.io/doc/unimorph-schema.pdf) - **Point of Contact:** [Arya McCarthy](mailto:arya@jhu.edu) ### Dataset Summary The Universal Morphology (UniMorph) project is a collaborative effort to improve how NLP handles complex morphology in the world’s languages. The goal of UniMorph is to annotate morphological data in a universal schema that allows an inflected word from any language to be defined by its lexical meaning, typically carried by the lemma, and by a rendering of its inflectional form in terms of a bundle of morphological features from our schema. The specification of the schema is described in Sylak-Glassman (2016). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The current version of the UniMorph dataset covers 110 languages. ## Dataset Structure ### Data Instances Each data instance comprises of a lemma and a set of possible realizations with morphological and meaning annotations. For example: ``` {'forms': {'Aktionsart': [[], [], [], [], []], 'Animacy': [[], [], [], [], []], ... 'Finiteness': [[], [], [], [1], []], ... 'Number': [[], [], [0], [], []], 'Other': [[], [], [], [], []], 'Part_Of_Speech': [[7], [10], [7], [7], [10]], ... 'Tense': [[1], [1], [0], [], [0]], ... 'word': ['ablated', 'ablated', 'ablates', 'ablate', 'ablating']}, 'lemma': 'ablate'} ``` ### Data Fields Each instance in the dataset has the following fields: - `lemma`: the common lemma for all all_forms - `forms`: all annotated forms for this lemma, with: - `word`: the full word form - [`category`]: a categorical variable denoting one or several tags in a category (several to represent composite tags, originally denoted with `A+B`). The full list of categories and possible tags for each can be found [here](https://github.com/unimorph/unimorph.github.io/blob/master/unimorph-schema-json/dimensions-to-features.json) ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@yjernite](https://github.com/yjernite) for adding this dataset.

hendrycks/ethics

hendrycks

--- license: mit language: en dataset_info: - config_name: default features: - name: label dtype: int64 - name: input dtype: string - config_name: commonsense features: - name: label dtype: int32 - name: input dtype: string splits: - name: train num_bytes: 14429921 num_examples: 13910 - name: validation num_bytes: 3148616 num_examples: 3885 - name: test num_bytes: 3863068 num_examples: 3964 download_size: 21625153 dataset_size: 21441605 - config_name: deontology features: - name: label dtype: int32 - name: scenario dtype: string - name: excuse dtype: string splits: - name: train num_bytes: 1854277 num_examples: 18164 - name: validation num_bytes: 369318 num_examples: 3596 - name: test num_bytes: 359268 num_examples: 3536 download_size: 2384007 dataset_size: 2582863 - config_name: justice features: - name: label dtype: int32 - name: scenario dtype: string splits: - name: train num_bytes: 2423889 num_examples: 21791 - name: validation num_bytes: 297935 num_examples: 2704 - name: test num_bytes: 228008 num_examples: 2052 download_size: 2837375 dataset_size: 2949832 - config_name: utilitarianism features: - name: baseline dtype: string - name: less_pleasant dtype: string splits: - name: train num_bytes: 2186713 num_examples: 13737 - name: validation num_bytes: 730391 num_examples: 4807 - name: test num_bytes: 668429 num_examples: 4271 download_size: 3466564 dataset_size: 3585533 - config_name: virtue features: - name: label dtype: int32 - name: scenario dtype: string splits: - name: train num_bytes: 2605021 num_examples: 28245 - name: validation num_bytes: 467254 num_examples: 4975 - name: test num_bytes: 452491 num_examples: 4780 download_size: 3364070 dataset_size: 3524766 tags: - AI Alignment --- # Dataset Card for ETHICS This is the data from [Aligning AI With Shared Human Values](https://arxiv.org/pdf/2008.02275) by Dan Hendrycks, Collin Burns, Steven Basart, Andrew Critch, Jerry Li, Dawn Song, and Jacob Steinhardt, published at ICLR 2021. For more information, see the [Github Repo](https://github.com/hendrycks/ethics). ## Dataset Summary This dataset provides ethics-based tasks for evaluating language models for AI alignment. ## Loading Data To load this data, you can use HuggingFace datasets and the dataloader script. ``` from datasets import load_dataset load_dataset("hendrycks/ethics", "commonsense") ``` Where `commonsense` is one of the following sections: commonsense, deontology, justice, utilitarianism, and virtue. ### Citation Information ``` @article{hendrycks2021ethics, title={Aligning AI With Shared Human Values}, author={Dan Hendrycks and Collin Burns and Steven Basart and Andrew Critch and Jerry Li and Dawn Song and Jacob Steinhardt}, journal={Proceedings of the International Conference on Learning Representations (ICLR)}, year={2021} } ```

tatsu-lab/alpaca_farm

tatsu-lab

--- license: cc-by-nc-4.0 ---

garage-bAInd/Open-Platypus

garage-bAInd

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: input dtype: string - name: output dtype: string - name: instruction dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 30776452 num_examples: 24926 download_size: 15565850 dataset_size: 30776452 language: - en size_categories: - 10K<n<100K --- # OpenPlatypus This dataset is focused on improving LLM logical reasoning skills and was used to train the Platypus2 models. It is comprised of the following datasets, which were filtered using keyword search and then Sentence Transformers to remove questions with a similarity above 80%: | Dataset Name | License Type | |--------------------------------------------------------------|--------------| | [PRM800K](https://github.com/openai/prm800k) | MIT | | [ScienceQA](https://github.com/lupantech/ScienceQA) | [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/) | | [SciBench](https://github.com/mandyyyyii/scibench) | MIT | | [ReClor](https://whyu.me/reclor/) | Non-commercial | | [TheoremQA](https://huggingface.co/datasets/wenhu/TheoremQA) | MIT | | [`nuprl/leetcode-solutions-python-testgen-gpt4`](https://huggingface.co/datasets/nuprl/leetcode-solutions-python-testgen-gpt4/viewer/nuprl--leetcode-solutions-python-testgen-gpt4/train?p=1) | None listed | | [`jondurbin/airoboros-gpt4-1.4.1`](https://huggingface.co/datasets/jondurbin/airoboros-gpt4-1.4.1) | other | | [`TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k`](https://huggingface.co/datasets/TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k/viewer/TigerResearch--tigerbot-kaggle-leetcodesolutions-en-2k/train?p=2) | apache-2.0 | | [openbookQA](https://huggingface.co/datasets/openbookqa/viewer/additional/train?row=35) | apache-2.0 | | [ARB](https://arb.duckai.org) | MIT | | [`timdettmers/openassistant-guanaco`](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) | apache-2.0 | ## Data Contamination Check We've removed approximately 200 questions that appear in the Hugging Face benchmark test sets. Please see our [paper](https://arxiv.org/abs/2308.07317) and [project webpage](https://platypus-llm.github.io) for additional information. ## Model Info Please see models at [`garage-bAInd`](https://huggingface.co/garage-bAInd). ## Training and filtering code Please see the [Platypus GitHub repo](https://github.com/arielnlee/Platypus). ## Citations ```bibtex @article{platypus2023, title={Platypus: Quick, Cheap, and Powerful Refinement of LLMs}, author={Ariel N. Lee and Cole J. Hunter and Nataniel Ruiz}, booktitle={arXiv preprint arxiv:2308.07317}, year={2023} } ``` ```bibtex @article{lightman2023lets, title={Let's Verify Step by Step}, author={Lightman, Hunter and Kosaraju, Vineet and Burda, Yura and Edwards, Harri and Baker, Bowen and Lee, Teddy and Leike, Jan and Schulman, John and Sutskever, Ilya and Cobbe, Karl}, journal={preprint arXiv:2305.20050}, year={2023} } ``` ```bibtex @inproceedings{lu2022learn, title={Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering}, author={Lu, Pan and Mishra, Swaroop and Xia, Tony and Qiu, Liang and Chang, Kai-Wei and Zhu, Song-Chun and Tafjord, Oyvind and Clark, Peter and Ashwin Kalyan}, booktitle={The 36th Conference on Neural Information Processing Systems (NeurIPS)}, year={2022} } ``` ```bibtex @misc{wang2023scibench, title={SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models}, author={Xiaoxuan Wang and Ziniu Hu and Pan Lu and Yanqiao Zhu and Jieyu Zhang and Satyen Subramaniam and Arjun R. Loomba and Shichang Zhang and Yizhou Sun and Wei Wang}, year={2023}, arXiv eprint 2307.10635 } ``` ```bibtex @inproceedings{yu2020reclor, author = {Yu, Weihao and Jiang, Zihang and Dong, Yanfei and Feng, Jiashi}, title = {ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning}, booktitle = {International Conference on Learning Representations (ICLR)}, month = {April}, year = {2020} } ``` ```bibtex @article{chen2023theoremqa, title={TheoremQA: A Theorem-driven Question Answering dataset}, author={Chen, Wenhu and Ming Yin, Max Ku, Elaine Wan, Xueguang Ma, Jianyu Xu, Tony Xia, Xinyi Wang, Pan Lu}, journal={preprint arXiv:2305.12524}, year={2023} } ``` ```bibtex @inproceedings{OpenBookQA2018, title={Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering}, author={Todor Mihaylov and Peter Clark and Tushar Khot and Ashish Sabharwal}, booktitle={EMNLP}, year={2018} } ``` ```bibtex @misc{sawada2023arb, title={ARB: Advanced Reasoning Benchmark for Large Language Models}, author={Tomohiro Sawada and Daniel Paleka and Alexander Havrilla and Pranav Tadepalli and Paula Vidas and Alexander Kranias and John J. Nay and Kshitij Gupta and Aran Komatsuzaki}, arXiv eprint 2307.13692, year={2023} } ```

Skylion007/openwebtext

Skylion007

--- annotations_creators: - no-annotation language_creators: - found language: - en license: - cc0-1.0 multilinguality: - monolingual pretty_name: OpenWebText size_categories: - 1M<n<10M source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: openwebtext dataset_info: features: - name: text dtype: string config_name: plain_text splits: - name: train num_bytes: 39769491688 num_examples: 8013769 download_size: 12880189440 dataset_size: 39769491688 --- # Dataset Card for "openwebtext" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://skylion007.github.io/OpenWebTextCorpus/](https://skylion007.github.io/OpenWebTextCorpus/) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 13.51 GB - **Size of the generated dataset:** 41.70 GB - **Total amount of disk used:** 55.21 GB ### Dataset Summary An open-source replication of the WebText dataset from OpenAI, that was used to train GPT-2. This distribution was created by Aaron Gokaslan and Vanya Cohen of Brown University. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### plain_text - **Size of downloaded dataset files:** 13.51 GB - **Size of the generated dataset:** 41.70 GB - **Total amount of disk used:** 55.21 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "text": "\"A magazine supplement with an image of Adolf Hitler and the title 'The Unreadable Book' is pictured in Berlin. No law bans “Mei..." } ``` ### Data Fields The data fields are the same among all splits. #### plain_text - `text`: a `string` feature. ### Data Splits | name | train | |------------|--------:| | plain_text | 8013769 | ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization The authors started by extracting all Reddit post urls from the Reddit submissions dataset. These links were deduplicated, filtered to exclude non-html content, and then shuffled randomly. The links were then distributed to several machines in parallel for download, and all web pages were extracted using the newspaper python package. Using Facebook FastText, non-English web pages were filtered out. Subsequently, near-duplicate documents were identified using local-sensitivity hashing (LSH). Documents were hashed into sets of 5-grams and all documents that had a similarity threshold of greater than 0.5 were removed. The the remaining documents were tokenized, and documents with fewer than 128 tokens were removed. This left 38GB of text data (40GB using SI units) from 8,013,769 documents. #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations The dataset doesn't contain annotations. ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information These data are released under this licensing scheme from the original authors ([source](https://skylion007.github.io/OpenWebTextCorpus/)): ``` We do not own any of the text from which these data has been extracted. We license the actual packaging of these parallel data under the [Creative Commons CC0 license (“no rights reserved”)](https://creativecommons.org/share-your-work/public-domain/cc0/) ``` #### Notice policy Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please: Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted. Clearly identify the copyrighted work claimed to be infringed. Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material. And contact us at the following email address: openwebtext at gmail.com and datasets at huggingface.co #### Take down policy The original authors will comply to legitimate requests by removing the affected sources from the next release of the corpus. Hugging Face will also update this repository accordingly. ### Citation Information ``` @misc{Gokaslan2019OpenWeb, title={OpenWebText Corpus}, author={Aaron Gokaslan*, Vanya Cohen*, Ellie Pavlick, Stefanie Tellex}, howpublished{\url{http://Skylion007.github.io/OpenWebTextCorpus}}, year={2019} } ``` ### Contributions Thanks to [@richarddwang](https://github.com/richarddwang) for adding this dataset.

mteb/sts12-sts

mteb

--- language: - en ---

cosmos_qa

--- annotations_creators: - crowdsourced language: - en language_creators: - found license: - cc-by-4.0 multilinguality: - monolingual pretty_name: CosmosQA size_categories: - 10K<n<100K source_datasets: - original task_categories: - multiple-choice task_ids: - multiple-choice-qa paperswithcode_id: cosmosqa dataset_info: features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answer0 dtype: string - name: answer1 dtype: string - name: answer2 dtype: string - name: answer3 dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 17159918 num_examples: 25262 - name: test num_bytes: 5121479 num_examples: 6963 - name: validation num_bytes: 2186987 num_examples: 2985 download_size: 24399475 dataset_size: 24468384 --- # Dataset Card for "cosmos_qa" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://wilburone.github.io/cosmos/](https://wilburone.github.io/cosmos/) - **Repository:** https://github.com/wilburOne/cosmosqa/ - **Paper:** [Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning](https://arxiv.org/abs/1909.00277) - **Point of Contact:** [Lifu Huang](mailto:warrior.fu@gmail.com) - **Size of downloaded dataset files:** 24.40 MB - **Size of the generated dataset:** 24.51 MB - **Total amount of disk used:** 48.91 MB ### Dataset Summary Cosmos QA is a large-scale dataset of 35.6K problems that require commonsense-based reading comprehension, formulated as multiple-choice questions. It focuses on reading between the lines over a diverse collection of people's everyday narratives, asking questions concerning on the likely causes or effects of events that require reasoning beyond the exact text spans in the context ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 24.40 MB - **Size of the generated dataset:** 24.51 MB - **Total amount of disk used:** 48.91 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "answer0": "If he gets married in the church he wo nt have to get a divorce .", "answer1": "He wants to get married to a different person .", "answer2": "He wants to know if he does nt like this girl can he divorce her ?", "answer3": "None of the above choices .", "context": "\"Do i need to go for a legal divorce ? I wanted to marry a woman but she is not in the same religion , so i am not concern of th...", "id": "3BFF0DJK8XA7YNK4QYIGCOG1A95STE##3180JW2OT5AF02OISBX66RFOCTG5J7##A2LTOS0AZ3B28A##Blog_56156##q1_a1##378G7J1SJNCDAAIN46FM2P7T6KZEW2", "label": 1, "question": "Why is this person asking about divorce ?" } ``` ### Data Fields The data fields are the same among all splits. #### default - `id`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answer0`: a `string` feature. - `answer1`: a `string` feature. - `answer2`: a `string` feature. - `answer3`: a `string` feature. - `label`: a `int32` feature. ### Data Splits | name |train|validation|test| |-------|----:|---------:|---:| |default|25262| 2985|6963| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information As reported via email by Yejin Choi, the dataset is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license. ### Citation Information ``` @inproceedings{huang-etal-2019-cosmos, title = "Cosmos {QA}: Machine Reading Comprehension with Contextual Commonsense Reasoning", author = "Huang, Lifu and Le Bras, Ronan and Bhagavatula, Chandra and Choi, Yejin", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)", month = nov, year = "2019", address = "Hong Kong, China", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D19-1243", doi = "10.18653/v1/D19-1243", pages = "2391--2401", } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@albertvillanova](https://github.com/albertvillanova), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

exams

--- pretty_name: EXAMS annotations_creators: - found language_creators: - found language: - ar - bg - de - es - fr - hr - hu - it - lt - mk - pl - pt - sq - sr - tr - vi license: - cc-by-sa-4.0 multilinguality: - monolingual - multilingual size_categories: - 10K<n<100K - 1K<n<10K - n<1K source_datasets: - original task_categories: - question-answering task_ids: - multiple-choice-qa paperswithcode_id: exams dataset_info: - config_name: alignments features: - name: source_id dtype: string - name: target_id_list sequence: string splits: - name: full num_bytes: 1265280 num_examples: 10834 download_size: 169745177 dataset_size: 1265280 - config_name: multilingual features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 3385865 num_examples: 7961 - name: validation num_bytes: 1143067 num_examples: 2672 - name: test num_bytes: 5753625 num_examples: 13510 download_size: 169745177 dataset_size: 10282557 - config_name: multilingual_with_para features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 127298595 num_examples: 7961 - name: validation num_bytes: 42713069 num_examples: 2672 - name: test num_bytes: 207981218 num_examples: 13510 download_size: 169745177 dataset_size: 377992882 - config_name: crosslingual_test features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: test num_bytes: 8412531 num_examples: 19736 download_size: 169745177 dataset_size: 8412531 - config_name: crosslingual_with_para_test features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: test num_bytes: 207981218 num_examples: 13510 download_size: 169745177 dataset_size: 207981218 - config_name: crosslingual_bg features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 1078545 num_examples: 2344 - name: validation num_bytes: 282115 num_examples: 593 download_size: 169745177 dataset_size: 1360660 - config_name: crosslingual_with_para_bg features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 47068024 num_examples: 2344 - name: validation num_bytes: 11916370 num_examples: 593 download_size: 169745177 dataset_size: 58984394 - config_name: crosslingual_hr features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 808320 num_examples: 2341 - name: validation num_bytes: 176910 num_examples: 538 download_size: 169745177 dataset_size: 985230 - config_name: crosslingual_with_para_hr features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 24890820 num_examples: 2341 - name: validation num_bytes: 5695382 num_examples: 538 download_size: 169745177 dataset_size: 30586202 - config_name: crosslingual_hu features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 678447 num_examples: 1731 - name: validation num_bytes: 202324 num_examples: 536 download_size: 169745177 dataset_size: 880771 - config_name: crosslingual_with_para_hu features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 19036575 num_examples: 1731 - name: validation num_bytes: 6043577 num_examples: 536 download_size: 169745177 dataset_size: 25080152 - config_name: crosslingual_it features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 399864 num_examples: 1010 - name: validation num_bytes: 93343 num_examples: 246 download_size: 169745177 dataset_size: 493207 - config_name: crosslingual_with_para_it features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 16409787 num_examples: 1010 - name: validation num_bytes: 4018497 num_examples: 246 download_size: 169745177 dataset_size: 20428284 - config_name: crosslingual_mk features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 826582 num_examples: 1665 - name: validation num_bytes: 204570 num_examples: 410 download_size: 169745177 dataset_size: 1031152 - config_name: crosslingual_with_para_mk features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 38446774 num_examples: 1665 - name: validation num_bytes: 9673826 num_examples: 410 download_size: 169745177 dataset_size: 48120600 - config_name: crosslingual_pl features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 574246 num_examples: 1577 - name: validation num_bytes: 141877 num_examples: 394 download_size: 169745177 dataset_size: 716123 - config_name: crosslingual_with_para_pl features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 16374617 num_examples: 1577 - name: validation num_bytes: 4159076 num_examples: 394 download_size: 169745177 dataset_size: 20533693 - config_name: crosslingual_pt features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 375214 num_examples: 740 - name: validation num_bytes: 87850 num_examples: 184 download_size: 169745177 dataset_size: 463064 - config_name: crosslingual_with_para_pt features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 12185799 num_examples: 740 - name: validation num_bytes: 3093848 num_examples: 184 download_size: 169745177 dataset_size: 15279647 - config_name: crosslingual_sq features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 424388 num_examples: 1194 - name: validation num_bytes: 110293 num_examples: 311 download_size: 169745177 dataset_size: 534681 - config_name: crosslingual_with_para_sq features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 17341921 num_examples: 1194 - name: validation num_bytes: 4450152 num_examples: 311 download_size: 169745177 dataset_size: 21792073 - config_name: crosslingual_sr features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 650268 num_examples: 1323 - name: validation num_bytes: 145928 num_examples: 314 download_size: 169745177 dataset_size: 796196 - config_name: crosslingual_with_para_sr features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 24576553 num_examples: 1323 - name: validation num_bytes: 5772713 num_examples: 314 download_size: 169745177 dataset_size: 30349266 - config_name: crosslingual_tr features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 718431 num_examples: 1571 - name: validation num_bytes: 182974 num_examples: 393 download_size: 169745177 dataset_size: 901405 - config_name: crosslingual_with_para_tr features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 18597963 num_examples: 1571 - name: validation num_bytes: 4763341 num_examples: 393 download_size: 169745177 dataset_size: 23361304 - config_name: crosslingual_vi features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 954191 num_examples: 1955 - name: validation num_bytes: 232264 num_examples: 488 download_size: 169745177 dataset_size: 1186455 - config_name: crosslingual_with_para_vi features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 40884023 num_examples: 1955 - name: validation num_bytes: 10260662 num_examples: 488 download_size: 169745177 dataset_size: 51144685 config_names: - alignments - crosslingual_bg - crosslingual_hr - crosslingual_hu - crosslingual_it - crosslingual_mk - crosslingual_pl - crosslingual_pt - crosslingual_sq - crosslingual_sr - crosslingual_test - crosslingual_tr - crosslingual_vi - crosslingual_with_para_bg - crosslingual_with_para_hr - crosslingual_with_para_hu - crosslingual_with_para_it - crosslingual_with_para_mk - crosslingual_with_para_pl - crosslingual_with_para_pt - crosslingual_with_para_sq - crosslingual_with_para_sr - crosslingual_with_para_test - crosslingual_with_para_tr - crosslingual_with_para_vi - multilingual - multilingual_with_para --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** https://github.com/mhardalov/exams-qa - **Paper:** [EXAMS: A Multi-Subject High School Examinations Dataset for Cross-Lingual and Multilingual Question Answering](https://arxiv.org/abs/2011.03080) - **Point of Contact:** [hardalov@@fmi.uni-sofia.bg](hardalov@@fmi.uni-sofia.bg) ### Dataset Summary EXAMS is a benchmark dataset for multilingual and cross-lingual question answering from high school examinations. It consists of more than 24,000 high-quality high school exam questions in 16 languages, covering 8 language families and 24 school subjects from Natural Sciences and Social Sciences, among others. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The languages in the dataset are: - ar - bg - de - es - fr - hr - hu - it - lt - mk - pl - pt - sq - sr - tr - vi ## Dataset Structure ### Data Instances An example of a data instance (with support paragraphs, in Bulgarian) is: ``` {'answerKey': 'C', 'id': '35dd6b52-7e71-11ea-9eb1-54bef70b159e', 'info': {'grade': 12, 'language': 'Bulgarian', 'subject': 'Biology'}, 'question': {'choices': {'label': ['A', 'B', 'C', 'D'], 'para': ['Това води до наследствени изменения между организмите. Мирновременните вождове са наследствени. Черният, сивият и кафявият цвят на оцветяване на тялото се определя от пигмента меланин и възниква в резултат на наследствени изменения. Тези различия, според Монтескьо, не са наследствени. Те са и важни наследствени вещи в клана. Те са били наследствени архонти и управляват демократично. Реликвите са исторически, религиозни, семейни (наследствени) и технически. Общо са направени 800 изменения. Не всички наследствени аномалии на хемоглобина са вредни, т.е. Моногенните наследствени болести, които водят до мигрена, са редки. Няма наследствени владетели. Повечето от тях са наследствени и се предават на потомството. Всичките синове са ерцхерцози на всичките наследствени земи и претенденти. През 1509 г. Фраунбергите са издигнати на наследствени имперски графове. Фамилията Валдбург заради постиженията са номинирани на „наследствени имперски трушсеси“. Фамилията Валдбург заради постиженията са номинирани на „наследствени имперски трушсеси“. Описани са единични наследствени случаи, но по-често липсва фамилна обремененост. Позициите им са наследствени и се предават в рамките на клана. Внесени са изменения в конструкцията на веригите. и са направени изменения в ходовата част. На храма са правени лоши архитектурни изменения. Изменения са предприети и вътре в двореца. Имало двама наследствени вождове. Имало двама наследствени вождове. Годишният календар, „компасът“ и биологичния часовник са наследствени и при много бозайници.', 'Постепенно задълбочаващите се функционални изменения довеждат и до структурни изменения. Те се дължат както на растягането на кожата, така и на въздействието на хормоналните изменения върху кожната тъкан. тези изменения се долавят по-ясно. Впоследствие, той претърпява изменения. Ширината остава без изменения. След тяхното издаване се налагат изменения в първоначалния Кодекс, защото не е съобразен с направените в Дигестите изменения. Еволюционният преход се характеризира със следните изменения: Наблюдават се и сезонни изменения в теглото. Приемат се изменения и допълнения към Устава. Тук се размножават и предизвикват възпалителни изменения. Общо са направени 800 изменения. Бронирането не претърпява съществени изменения. При животните се откриват изменения при злокачествената форма. Срещат се и дегенеративни изменения в семенните каналчета. ТАВКР „Баку“ се строи по изменения проект 1143.4. Трансът се съпровожда с определени изменения на мозъчната дейност. На изменения е подложен и Светия Синод. Внесени са изменения в конструкцията на веригите. На храма са правени лоши архитектурни изменения. Оттогава стиховете претърпяват изменения няколко пъти. Настъпват съществени изменения в музикалната култура. По-късно той претърпява леки изменения. Настъпват съществени изменения в музикалната култура. Претърпява сериозни изменения само носовата надстройка. Хоризонталното брониране е оставено без изменения.', 'Модификациите са обратими. Тези реакции са обратими. В началните стадии тези натрупвания са обратими. Всички такива ефекти са временни и обратими. Много от реакциите са обратими и идентични с тези при гликолизата. Ако в обращение има книжни пари, те са обратими в злато при поискване . Общо са направени 800 изменения. Непоследователността е представена от принципа на "симетрия", при който взаимоотношенията са разглеждани като симетрични или обратими. Откакто формулите в клетките на електронната таблица не са обратими, тази техника е с ограничена стойност. Ефектът на Пелтие-Зеебек и ефектът Томсън са обратими (ефектът на Пелтие е обратен на ефекта на Зеебек). Плазмолизата протича в три етапа, в зависимост от силата и продължителността на въздействието:\n\nПървите два етапа са обратими. Внесени са изменения в конструкцията на веригите. и са направени изменения в ходовата част. На храма са правени лоши архитектурни изменения. Изменения са предприети и вътре в двореца. Оттогава насетне екипите не са претърпявали съществени изменения. Изменения са направени и в колесника на машината. Тези изменения са обявени през октомври 1878 година. Последните изменения са внесени през януари 2009 година. В процеса на последващото проектиране са внесени някои изменения. Сериозните изменения са в края на Втората световна война. Внесени са изменения в конструкцията на погребите и подемниците. Внесени са изменения в конструкцията на погребите и подемниците. Внесени са изменения в конструкцията на погребите и подемниците. Постепенно задълбочаващите се функционални изменения довеждат и до структурни изменения.', 'Ерозионни процеси от масов характер липсват. Обновлението в редиците на партията приема масов характер. Тя обаче няма масов характер поради спецификата на формата. Движението против десятъка придобива масов характер и в Балчишка околия. Понякога екзекутирането на „обсебените от Сатана“ взимало невероятно масов характер. Укриването на дължими като наряд продукти в селата придобива масов характер. Периодичните миграции са в повечето случаи с масов характер и са свързани със сезонните изменения в природата, а непериодичните са премествания на животни, които настъпват след пожари, замърсяване на средата, висока численост и др. Имат необратим характер. Именно по време на двувековните походи на западните рицари използването на гербовете придобива масов характер. След присъединяването на Южен Кавказ към Русия, изселването на азербайджанци от Грузия придобива масов характер. Те имат нормативен характер. Те имат установителен характер. Освобождаването на работна сила обикновено има масов характер, защото обхваща големи контингенти от носителите на труд. Валежите имат подчертано континентален характер. Имат най-често издънков характер. Приливите имат предимно полуденонощен характер. Някои от тях имат мистериален характер. Тези сведения имат случаен, епизодичен характер. Те имат сезонен или годишен характер. Временните обезпечителни мерки имат временен характер. Други имат пожелателен характер (Здравко, Слава). Ловът и събирачеството имат спомагателен характер. Фактически успяват само малко да усилят бронирането на артилерийските погреби, другите изменения носят само частен характер. Някои карикатури имат само развлекателен характер, докато други имат политически нюанси. Поемите на Хезиод имат по-приложен характер.'], 'text': ['дължат се на фенотипни изменения', 'имат масов характер', 'са наследствени', 'са обратими']}, 'stem': 'Мутационите изменения:'}} ``` ### Data Fields A data instance contains the following fields: - `id`: A question ID, unique across the dataset - `question`: the question contains the following: - `stem`: a stemmed representation of the question textual - `choices`: a set of 3 to 5 candidate answers, which each have: - `text`: the text of the answers - `label`: a label in `['A', 'B', 'C', 'D', 'E']` used to match to the `answerKey` - `para`: (optional) a supported paragraph from Wikipedia in the same language as the question and answer - `answerKey`: the key corresponding to the right answer's `label` - `info`: some additional information on the question including: - `grade`: the school grade for the exam this question was taken from - `subject`: a free text description of the academic subject - `language`: the English name of the language for this question ### Data Splits Depending on the configuration, the dataset have different splits: - "alignments": a single "full" split - "multilingual" and "multilingual_with_para": "train", "validation" and "test" splits - "crosslingual_test" and "crosslingual_with_para_test": a single "test" split - the rest of crosslingual configurations: "train" and "validation" splits ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization Eχαµs was collected from official state exams prepared by the ministries of education of various countries. These exams are taken by students graduating from high school, and often require knowledge learned through the entire course. The questions cover a large variety of subjects and material based on the country’s education system. They cover major school subjects such as Biology, Chemistry, Geography, History, and Physics, but we also highly specialized ones such as Agriculture, Geology, Informatics, as well as some applied and profiled studies. Some countries allow students to take official examinations in several languages. This dataset provides 9,857 parallel question pairs spread across seven languages coming from Croatia (Croatian, Serbian, Italian, Hungarian), Hungary (Hungarian, German, French, Spanish, Croatian, Serbian, Italian), and North Macedonia (Macedonian, Albanian, Turkish). For all languages in the dataset, the first step in the process of data collection was to download the PDF files per year, per subject, and per language (when parallel languages were available in the same source), convert the PDF files to text, and select those that were well formatted and followed the document structure. Then, Regular Expressions (RegEx) were used to parse the questions, their corresponding choices and the correct answer choice. In order to ensure that all our questions are answerable using textual input only, questions that contained visual information were removed, as selected by using curated list of words such as map, table, picture, graph, etc., in the corresponding language. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information The dataset, which contains paragraphs from Wikipedia, is licensed under CC-BY-SA 4.0. The code in this repository is licensed according the [LICENSE file](https://raw.githubusercontent.com/mhardalov/exams-qa/main/LICENSE). ### Citation Information ``` @article{hardalov2020exams, title={EXAMS: A Multi-subject High School Examinations Dataset for Cross-lingual and Multilingual Question Answering}, author={Hardalov, Momchil and Mihaylov, Todor and Dimitrina Zlatkova and Yoan Dinkov and Ivan Koychev and Preslav Nvakov}, journal={arXiv preprint arXiv:2011.03080}, year={2020} } ``` ### Contributions Thanks to [@yjernite](https://github.com/yjernite) for adding this dataset.

mteb/sts13-sts

mteb

--- language: - en ---

mteb/sts14-sts

mteb

--- language: - en ---

amazon_polarity

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - apache-2.0 multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification pretty_name: Amazon Review Polarity dataset_info: features: - name: label dtype: class_label: names: '0': negative '1': positive - name: title dtype: string - name: content dtype: string config_name: amazon_polarity splits: - name: train num_bytes: 1604364432 num_examples: 3600000 - name: test num_bytes: 178176193 num_examples: 400000 download_size: 688339454 dataset_size: 1782540625 train-eval-index: - config: amazon_polarity task: text-classification task_id: binary_classification splits: train_split: train eval_split: test col_mapping: content: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- # Dataset Card for Amazon Review Polarity ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://registry.opendata.aws/ - **Repository:** https://github.com/zhangxiangxiao/Crepe - **Paper:** https://arxiv.org/abs/1509.01626 - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Xiang Zhang](mailto:xiang.zhang@nyu.edu) ### Dataset Summary The Amazon reviews dataset consists of reviews from amazon. The data span a period of 18 years, including ~35 million reviews up to March 2013. Reviews include product and user information, ratings, and a plaintext review. ### Supported Tasks and Leaderboards - `text-classification`, `sentiment-classification`: The dataset is mainly used for text classification: given the content and the title, predict the correct star rating. ### Languages Mainly English. ## Dataset Structure ### Data Instances A typical data point, comprises of a title, a content and the corresponding label. An example from the AmazonPolarity test set looks as follows: ``` { 'title':'Great CD', 'content':"My lovely Pat has one of the GREAT voices of her generation. I have listened to this CD for YEARS and I still LOVE IT. When I'm in a good mood it makes me feel better. A bad mood just evaporates like sugar in the rain. This CD just oozes LIFE. Vocals are jusat STUUNNING and lyrics just kill. One of life's hidden gems. This is a desert isle CD in my book. Why she never made it big is just beyond me. Everytime I play this, no matter black, white, young, old, male, female EVERYBODY says one thing ""Who was that singing ?""", 'label':1 } ``` ### Data Fields - 'title': a string containing the title of the review - escaped using double quotes (") and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is "\n". - 'content': a string containing the body of the document - escaped using double quotes (") and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is "\n". - 'label': either 1 (positive) or 0 (negative) rating. ### Data Splits The Amazon reviews polarity dataset is constructed by taking review score 1 and 2 as negative, and 4 and 5 as positive. Samples of score 3 is ignored. Each class has 1,800,000 training samples and 200,000 testing samples. ## Dataset Creation ### Curation Rationale The Amazon reviews polarity dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu). It is used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015). ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information Apache License 2.0 ### Citation Information McAuley, Julian, and Jure Leskovec. "Hidden factors and hidden topics: understanding rating dimensions with review text." In Proceedings of the 7th ACM conference on Recommender systems, pp. 165-172. 2013. Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015) ### Contributions Thanks to [@hfawaz](https://github.com/hfawaz) for adding this dataset.

mteb/sts15-sts

mteb

--- language: - en ---

mteb/sts16-sts

mteb

--- language: - en ---

graelo/wikipedia

graelo

--- annotations_creators: - no-annotation language_creators: - crowdsourced pretty_name: Wikipedia paperswithcode_id: null license: - cc-by-sa-3.0 - gfdl task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling source_datasets: - original multilinguality: - multilingual size_categories: - n<1K - 1K<n<10K - 10K<n<100K - 100K<n<1M - 1M<n<10M language: # - aa - closed and no dump - ab - ace - ady - af - ak - als - alt - am - ami - an - ang - anp - ar - arc - ary - arz - as - ast - atj - av - avk - awa - ay - az - azb - ba - ban - bar # - bat-smg - see bcp47 below - bcl # - be-x-old - see bcp47 below - be - bg - bh - bi - bjn - blk - bm - bn - bo - bpy - br - bs - bug - bxr - ca # - cbk-zam - see bcp47 below - cdo - ce - ceb - ch - cho # closed - chr - chy - ckb - co - cr - crh - cs - csb - cu - cv - cy - da - dag - de - din - diq - dsb - dty - dv - dz - ee - el - eml - eo - es - et - eu - ext - fa - fat - ff - fi # - fiu-vro - see bcp47 below - fj - fo - fr - frp - frr - fur - fy - ga - gag - gan - gcr - gd - gl - glk - gn - gom - gor - got - gu - guc - gur - guw - gv - ha - hak - haw - he - hi - hif - ho # closed - hr - hsb - ht - hu - hy - hyw # - hz - closed and no dump - ia - id - ie - ig - ii # closed - ik - ilo - inh - io - is - it - iu - ja - jam - jbo - jv - ka - kaa - kab - kbd - kbp - kcg - kg - ki - kj # closed - kk - kl - km - kn - ko - koi # - kr - closed and no dump - krc - ks - ksh - ku - kv - kw - ky - la - lad - lb - lbe - lez - lfn - lg - li - lij - lld - lmo - ln - lo - lrc # closed - lt - ltg - lv - mad - mai # - map-bms - see bcp47 below - mdf - mg - mh - mhr - mi - min - mk - ml - mn - mni - mnw - mr - mrj - ms - mt - mus # closed - mwl - my - myv - mzn # - na - closed and no dump - nah - nap # - nds-nl - see bcp47 below - nds - ne - new - ng # closed - nia - nl - nn - no - nov - nqo - nrm - nso - nv - ny - oc - olo - om - or - os - pa - pag - pam - pap - pcd - pcm - pdc - pfl - pi - pih - pl - pms - pnb - pnt - ps - pt - pwn - qu - rm - rmy - rn - ro # - roa-rup - see bcp47 below # - roa-tara - see bcp47 below - ru - rue - rw - sa - sah - sat - sc - scn - sco - sd - se - sg - sh - shi - shn - si # - simple - see bcp47 below - sk - skr - sl - sm - smn - sn - so - sq - sr - srn - ss - st - stq - su - sv - sw - szl - szy - ta - tay - tcy - te - tet - tg - th - ti - tk - tl - tn - to - tpi - tr - trv - ts - tt - tum - tw - ty - tyv - udm - ug - uk - ur - uz - ve - vec - vep - vi - vls - vo - wa - war - wo - wuu - xal - xh - xmf - yi - yo - za - zea - zh # - zh-classical - see bcp47 below # - zh-min-nan - see bcp47 below # - zh-yue - see bcp47 below - zu language_bcp47: - bat-smg - be-x-old - cbk-zam - fiu-vro - map-bms - nds-nl - roa-rup - roa-tara - simple - zh-classical - zh-min-nan - zh-yue dataset_info: - config_name: 20230601.ab features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 4183525 num_examples: 6114 download_size: 1172328 dataset_size: 4183525 - config_name: 20230601.ace features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 4887561 num_examples: 12839 download_size: 1473823 dataset_size: 4887561 - config_name: 20230601.ady features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 613082 num_examples: 609 download_size: 280249 dataset_size: 613082 - config_name: 20230601.af features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 220678901 num_examples: 108170 download_size: 121238071 dataset_size: 220678901 - config_name: 20230601.ak features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 189 num_examples: 1 download_size: 3045 dataset_size: 189 - config_name: 20230601.als features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 80615079 num_examples: 29804 download_size: 48883379 dataset_size: 80615079 - config_name: 20230601.alt features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 5786027 num_examples: 1082 download_size: 2401701 dataset_size: 5786027 - config_name: 20230601.am features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 24009050 num_examples: 13839 download_size: 10615909 dataset_size: 24009050 - config_name: 20230601.ami features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 3865236 num_examples: 1570 download_size: 2006639 dataset_size: 3865236 - config_name: 20230601.an features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 56295233 num_examples: 43744 download_size: 29055888 dataset_size: 56295233 - config_name: 20230601.ang features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 2854073 num_examples: 4019 download_size: 1756372 dataset_size: 2854073 - config_name: 20230601.anp features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 9055032 num_examples: 2736 download_size: 3270423 dataset_size: 9055032 - config_name: 20230601.ar features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 3052201469 num_examples: 1205403 download_size: 1319905253 dataset_size: 3052201469 - config_name: 20230601.arc features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 830073 num_examples: 1925 download_size: 360590 dataset_size: 830073 - config_name: 20230601.ary features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 10007364 num_examples: 6703 download_size: 4094420 dataset_size: 10007364 - config_name: 20230601.arz features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 1364641408 num_examples: 1617770 download_size: 306336320 dataset_size: 1364641408 - config_name: 20230601.as features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 86645223 num_examples: 11988 download_size: 33149841 dataset_size: 86645223 - config_name: 20230601.ast features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 470349731 num_examples: 132550 download_size: 271011784 dataset_size: 470349731 - config_name: 20230601.atj features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 993287 num_examples: 1965 download_size: 502890 dataset_size: 993287 - config_name: 20230601.av features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 5996158 num_examples: 3392 download_size: 2514243 dataset_size: 5996158 - config_name: 20230601.avk features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 31189461 num_examples: 27493 download_size: 7729144 dataset_size: 31189461 - config_name: 20230601.awa features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 3588050 num_examples: 3701 download_size: 1230725 dataset_size: 3588050 - config_name: 20230601.ay features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 4357283 num_examples: 5287 download_size: 1736571 dataset_size: 4357283 - config_name: 20230601.az features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 425710145 num_examples: 194486 download_size: 225589717 dataset_size: 425710145 - config_name: 20230601.azb features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 186034971 num_examples: 243041 download_size: 46251265 dataset_size: 186034971 - config_name: 20230601.ba features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 293142247 num_examples: 62907 download_size: 120320323 dataset_size: 293142247 - config_name: 20230601.ban features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 16509353 num_examples: 19293 download_size: 6302437 dataset_size: 16509353 - config_name: 20230601.bar features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 36001708 num_examples: 26978 download_size: 21611902 dataset_size: 36001708 - config_name: 20230601.bat-smg features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 7536614 num_examples: 17181 download_size: 3411835 dataset_size: 7536614 - config_name: 20230601.be-x-old features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 244894736 num_examples: 82917 download_size: 110733701 dataset_size: 244894736 - config_name: 20230601.bcl features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 18259970 num_examples: 13934 download_size: 10086356 dataset_size: 18259970 - config_name: 20230601.be features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 606416485 num_examples: 231617 download_size: 280474552 dataset_size: 606416485 - config_name: 20230601.bg features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 1080390968 num_examples: 291361 download_size: 506945262 dataset_size: 1080390968 - config_name: 20230601.bh features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 16078510 num_examples: 8446 download_size: 5648960 dataset_size: 16078510 - config_name: 20230601.bi features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 398357 num_examples: 1539 download_size: 200277 dataset_size: 398357 - config_name: 20230601.bjn features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 6755874 num_examples: 10379 download_size: 3265979 dataset_size: 6755874 - config_name: 20230601.blk features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 24413622 num_examples: 2725 download_size: 7356285 dataset_size: 24413622 - config_name: 20230601.bm features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 473185 num_examples: 1221 download_size: 261438 dataset_size: 473185 - config_name: 20230601.bn features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 913676298 num_examples: 138515 download_size: 330147337 dataset_size: 913676298 - config_name: 20230601.bo features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 132034426 num_examples: 12434 download_size: 38687191 dataset_size: 132034426 - config_name: 20230601.bpy features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 42862119 num_examples: 25167 download_size: 6532133 dataset_size: 42862119 - config_name: 20230601.br features: - name: id dtype: string - 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name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 402322741 num_examples: 197343 download_size: 164074548 dataset_size: 402322741 - config_name: 20230901.uz features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 385386661 num_examples: 242726 download_size: 203362895 dataset_size: 385386661 - config_name: 20230901.ve features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 349857 num_examples: 840 download_size: 161562 dataset_size: 349857 - config_name: 20230901.vec features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 37883286 num_examples: 69250 download_size: 16164035 dataset_size: 37883286 - config_name: 20230901.vep features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 11487509 num_examples: 6918 download_size: 6327017 dataset_size: 11487509 - config_name: 20230901.vi features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 1606980713 num_examples: 1287263 download_size: 742700712 dataset_size: 1606980713 - config_name: 20230901.vls features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 11310015 num_examples: 7839 download_size: 6960289 dataset_size: 11310015 - config_name: 20230901.vo features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 19274897 num_examples: 34504 download_size: 6491359 dataset_size: 19274897 - config_name: 20230901.wa features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 12140372 num_examples: 11955 download_size: 7231141 dataset_size: 12140372 - config_name: 20230901.war features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 467623925 num_examples: 1266345 download_size: 109503863 dataset_size: 467623925 - config_name: 20230901.wo features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 3498562 num_examples: 1718 download_size: 2077375 dataset_size: 3498562 - config_name: 20230901.wuu features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 25005942 num_examples: 42969 download_size: 15994961 dataset_size: 25005942 - config_name: 20230901.xal features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 1390063 num_examples: 2290 download_size: 507117 dataset_size: 1390063 - config_name: 20230901.xh features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 2415590 num_examples: 1667 download_size: 1503917 dataset_size: 2415590 - config_name: 20230901.xmf features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 37262425 num_examples: 17949 download_size: 12771047 dataset_size: 37262425 - config_name: 20230901.yi features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 36150608 num_examples: 15329 download_size: 16208341 dataset_size: 36150608 - config_name: 20230901.yo features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 18460117 num_examples: 33495 download_size: 8504564 dataset_size: 18460117 - config_name: 20230901.za features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 1359106 num_examples: 2971 download_size: 662982 dataset_size: 1359106 - config_name: 20230901.zea features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 5106625 num_examples: 5834 download_size: 2567716 dataset_size: 5106625 - config_name: 20230901.zh features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 2766648619 num_examples: 1375017 download_size: 1748154636 dataset_size: 2766648619 - config_name: 20230901.zh-classical features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 14819164 num_examples: 12615 download_size: 10031693 dataset_size: 14819164 - config_name: 20230901.zh-min-nan features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 159385896 num_examples: 432644 download_size: 37476665 dataset_size: 159385896 - config_name: 20230901.zh-yue features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 108979942 num_examples: 133155 download_size: 64318527 dataset_size: 108979942 - config_name: 20230901.zu features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 6925330 num_examples: 11486 download_size: 3690925 dataset_size: 6925330 - config_name: 20230601.et features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 431680309 num_examples: 236848 download_size: 262989758 dataset_size: 431680309 --- # Wikipedia This Wikipedia dataset contains all available languages for recent dumps. It is a refresh of the [20220301 wikipedia](https://hf.co/datasets/wikipedia) from Huggingface, so it has the same license and dataset card details. The benefits of this dataset are: - more recent dumps (see table below) - a few additional languages - all available languages are preprocessed (including the largests: `en` and `ceb`) | version | dump | # available languages | closed & dump | closed & no dump | | ----- | ---- | ----- | ------ | --- | | `1.0.0` | 20230601 | 328 | 9: ak (soon), cho, ho, ii, kj, lrc, mh, mus, ng | 4: aa, hz, kr, na | | `1.1.0` | 20230601 | 329 (+et ~[az,ceb,ch,hr,ii,lrc,ta]) | 9: ak (soon), cho, ho, ii, kj, lrc, mh, mus, ng | 4: aa, hz, kr, na | | `1.2.0` | 20230901 | idem | 9: ak , cho, ho, ii, kj, lrc, mh, mus, ng | 4: aa, hz, kr, na | Source: [List of Wikimedia Languages](https://en.wikipedia.org/wiki/List_of_Wikipedias). A few (9) Wikimedias are closed, meaning they won't have new pages, but the dumps are still available. In addition, very few (4) Wikimedias are closed and don't have dumps anymore. ## Release Notes `1.2.0` - **chore**: Update to 20230901 `1.1.0` - **feat**: Add missing estonian (my bad), thanks Chris Ha - **fix**: update category lists for az, ceb, ch, hr, ii, lrc, ta, which means they were all processed again. `1.0.0` - **chore**: File layout is now `data/{dump}/{lang}/{info.json,*.parquet}`. Sorry for the radical update, probably won't happen again. - **chore**: Parquet files are now sharded (size < 200 MB), allowing parallel downloads and processing. - **fix**: All languages were all processed again because of a bug in the media and category names, leading to some links not being extracted. - **feat**: Add `en` and `ceb` which were too big for my Beam DirectRunner at the time. ## Usage ```python from datasets import load_dataset wikipedia_es = load_dataset("graelo/wikipedia", "20230601.es") ``` --- ## Build instructions Developer only. This dataset was preprocessed with a Beam DirectRunner as follows. ### 1. Determine the date of the dump you are interested in Choose one wikipedia dump, for instance <https://dumps.wikimedia.org/cewiki/> and identify the date. ### 2. [Optional] Get a refreshed list of languages This is optional because it not very likely that a new language will have suddenly appeared since the last version _and_ have a significant dataset. Navigate to <https://en.wikipedia.org/wiki/List_of_Wikipedias> and copy the languages column from the "Detailed list" table (near the end of the page). Copy that content in the form of a Python list into `lang_def.py` (at the top of the repo) under a new date. ### 3. [Optional] Create Media and Category aliases In order to properly extract links to images and media in all languages, we must refresh the two corresponding files. To do so, from the root of the repo, run ```sh python -m prep.create_aliases ``` This will create or update these two files at the root of the repo: - `media_aliases.py` - `category_aliases.py` These files are used in the final step ### 4. Build and prepare the datasets into sharded parquet files Running this script downloads the wikipedia dumps for each language in `lang_def.py` and shards each language dataset into the appropriate number of shards (max size ~ 250MB). ```sh python -m prep.build --date 20230601 ``` There are other options: ```text $ python -m prep.build --help usage: Wikipedia Builder [-h] [--date DATE] [--language [LANG ...]] [--cache-dir DIR] [--mirror MIRROR] Prepares the Wikipedia dataset for each language optional arguments: -h, --help show this help message and exit --date DATE Wikipedia dump date (e.g. 20230601) --language [LANG ...] Language code (e.g. en). If missing, all languages are processed --cache-dir DIR Cache directory for 🤗 Datasets --mirror MIRROR Mirror URL ``` For instance, for faster downloads of the dumps, use the mirror option: ```sh python -m prep.build \ --date 20230601 \ --language bs \ --mirror https://mirror.accum.se/mirror/wikimedia.org/dumps/ ``` It will download the dumps at around 60MB/s instead of the capped speed (~4MB/s) from <https://dumps.wikimedia.org>. The script will skip existing directories, allowing you to run the script in several passes. Notes: - These instructions build upon the build process of the [Wikipedia](https://huggingface.co/datasets/wikipedia) 🤗 Dataset. HF did a fantastic job, I just pushed it a bit further. - Be aware that not all mirrors contain all dumps. For instance mirror.accum.se does not contain dumps for languages such as be-x-old or cbk-zam. My own solution is to run a first pass using the aforementioned mirror, and a second pass with the official `https://dumps.wikimedia.org` site (omitting the `--mirror` parameter).

senti_lex

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - af - an - ar - az - be - bg - bn - br - bs - ca - cs - cy - da - de - el - eo - es - et - eu - fa - fi - fo - fr - fy - ga - gd - gl - gu - he - hi - hr - ht - hu - hy - ia - id - io - is - it - ja - ka - km - kn - ko - ku - ky - la - lb - lt - lv - mk - mr - ms - mt - nl - nn - 'no' - pl - pt - rm - ro - ru - sk - sl - sq - sr - sv - sw - ta - te - th - tk - tl - tr - uk - ur - uz - vi - vo - wa - yi - zh - zhw license: - gpl-3.0 multilinguality: - multilingual size_categories: - 1K<n<10K - n<1K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification pretty_name: SentiWS dataset_info: - config_name: af features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 45954 num_examples: 2299 download_size: 0 dataset_size: 45954 - config_name: an features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 1832 num_examples: 97 download_size: 0 dataset_size: 1832 - config_name: ar features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 58707 num_examples: 2794 download_size: 0 dataset_size: 58707 - config_name: az features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 40044 num_examples: 1979 download_size: 0 dataset_size: 40044 - config_name: be features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 41915 num_examples: 1526 download_size: 0 dataset_size: 41915 - config_name: bg features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 78779 num_examples: 2847 download_size: 0 dataset_size: 78779 - config_name: bn features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 70928 num_examples: 2393 download_size: 0 dataset_size: 70928 - config_name: br features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 3234 num_examples: 184 download_size: 0 dataset_size: 3234 - config_name: bs features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 39890 num_examples: 2020 download_size: 0 dataset_size: 39890 - config_name: ca features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 64512 num_examples: 3204 download_size: 0 dataset_size: 64512 - config_name: cs features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 53194 num_examples: 2599 download_size: 0 dataset_size: 53194 - config_name: cy features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 31546 num_examples: 1647 download_size: 0 dataset_size: 31546 - config_name: da features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 66756 num_examples: 3340 download_size: 0 dataset_size: 66756 - config_name: de features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 82223 num_examples: 3974 download_size: 0 dataset_size: 82223 - config_name: el features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 76281 num_examples: 2703 download_size: 0 dataset_size: 76281 - config_name: eo features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 50271 num_examples: 2604 download_size: 0 dataset_size: 50271 - config_name: es features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 87157 num_examples: 4275 download_size: 0 dataset_size: 87157 - config_name: et features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 41964 num_examples: 2105 download_size: 0 dataset_size: 41964 - config_name: eu features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 39641 num_examples: 1979 download_size: 0 dataset_size: 39641 - config_name: fa features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 53399 num_examples: 2477 download_size: 0 dataset_size: 53399 - config_name: fi features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 68294 num_examples: 3295 download_size: 0 dataset_size: 68294 - config_name: fo features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 2213 num_examples: 123 download_size: 0 dataset_size: 2213 - config_name: fr features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 94832 num_examples: 4653 download_size: 0 dataset_size: 94832 - config_name: fy features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 3916 num_examples: 224 download_size: 0 dataset_size: 3916 - config_name: ga features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 21209 num_examples: 1073 download_size: 0 dataset_size: 21209 - config_name: gd features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 6441 num_examples: 345 download_size: 0 dataset_size: 6441 - config_name: gl features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 55279 num_examples: 2714 download_size: 0 dataset_size: 55279 - config_name: gu features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 60025 num_examples: 2145 download_size: 0 dataset_size: 60025 - config_name: he features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 54706 num_examples: 2533 download_size: 0 dataset_size: 54706 - config_name: hi features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 103800 num_examples: 3640 download_size: 0 dataset_size: 103800 - config_name: hr features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 43775 num_examples: 2208 download_size: 0 dataset_size: 43775 - config_name: ht features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 8261 num_examples: 472 download_size: 0 dataset_size: 8261 - config_name: hu features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 74203 num_examples: 3522 download_size: 0 dataset_size: 74203 - config_name: hy features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 44593 num_examples: 1657 download_size: 0 dataset_size: 44593 - config_name: ia features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 6401 num_examples: 326 download_size: 0 dataset_size: 6401 - config_name: id features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 56879 num_examples: 2900 download_size: 0 dataset_size: 56879 - config_name: io features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 3348 num_examples: 183 download_size: 0 dataset_size: 3348 - config_name: is features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 34565 num_examples: 1770 download_size: 0 dataset_size: 34565 - config_name: it features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 92165 num_examples: 4491 download_size: 0 dataset_size: 92165 - config_name: ja features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 21770 num_examples: 1017 download_size: 0 dataset_size: 21770 - config_name: ka features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 81286 num_examples: 2202 download_size: 0 dataset_size: 81286 - config_name: km features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 23133 num_examples: 956 download_size: 0 dataset_size: 23133 - config_name: kn features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 70449 num_examples: 2173 download_size: 0 dataset_size: 70449 - config_name: ko features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 41716 num_examples: 2118 download_size: 0 dataset_size: 41716 - config_name: ku features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 2510 num_examples: 145 download_size: 0 dataset_size: 2510 - config_name: ky features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 5746 num_examples: 246 download_size: 0 dataset_size: 5746 - config_name: la features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 39092 num_examples: 2033 download_size: 0 dataset_size: 39092 - config_name: lb features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 4150 num_examples: 224 download_size: 0 dataset_size: 4150 - config_name: lt features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 45274 num_examples: 2190 download_size: 0 dataset_size: 45274 - config_name: lv features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 39879 num_examples: 1938 download_size: 0 dataset_size: 39879 - config_name: mk features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 81619 num_examples: 2965 download_size: 0 dataset_size: 81619 - config_name: mr features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 48601 num_examples: 1825 download_size: 0 dataset_size: 48601 - config_name: ms features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 57265 num_examples: 2934 download_size: 0 dataset_size: 57265 - config_name: mt features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 16913 num_examples: 863 download_size: 0 dataset_size: 16913 - config_name: nl features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 80335 num_examples: 3976 download_size: 0 dataset_size: 80335 - config_name: nn features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 35835 num_examples: 1894 download_size: 0 dataset_size: 35835 - config_name: 'no' features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 61160 num_examples: 3089 download_size: 0 dataset_size: 61160 - config_name: pl features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 73213 num_examples: 3533 download_size: 0 dataset_size: 73213 - config_name: pt features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 80618 num_examples: 3953 download_size: 0 dataset_size: 80618 - config_name: rm features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 2060 num_examples: 116 download_size: 0 dataset_size: 2060 - config_name: ro features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 66071 num_examples: 3329 download_size: 0 dataset_size: 66071 - config_name: ru features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 82966 num_examples: 2914 download_size: 0 dataset_size: 82966 - config_name: sk features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 49751 num_examples: 2428 download_size: 0 dataset_size: 49751 - config_name: sl features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 44430 num_examples: 2244 download_size: 0 dataset_size: 44430 - config_name: sq features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 40484 num_examples: 2076 download_size: 0 dataset_size: 40484 - config_name: sr features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 53257 num_examples: 2034 download_size: 0 dataset_size: 53257 - config_name: sv features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 73939 num_examples: 3722 download_size: 0 dataset_size: 73939 - config_name: sw features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 24962 num_examples: 1314 download_size: 0 dataset_size: 24962 - config_name: ta features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 71071 num_examples: 2057 download_size: 0 dataset_size: 71071 - config_name: te features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 77306 num_examples: 2523 download_size: 0 dataset_size: 77306 - config_name: th features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 34209 num_examples: 1279 download_size: 0 dataset_size: 34209 - config_name: tk features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 1425 num_examples: 78 download_size: 0 dataset_size: 1425 - config_name: tl features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 36190 num_examples: 1858 download_size: 0 dataset_size: 36190 - config_name: tr features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 49295 num_examples: 2500 download_size: 0 dataset_size: 49295 - config_name: uk features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 80226 num_examples: 2827 download_size: 0 dataset_size: 80226 - config_name: ur features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 28469 num_examples: 1347 download_size: 0 dataset_size: 28469 - config_name: uz features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 1944 num_examples: 111 download_size: 0 dataset_size: 1944 - config_name: vi features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 18100 num_examples: 1016 download_size: 0 dataset_size: 18100 - config_name: vo features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 775 num_examples: 43 download_size: 0 dataset_size: 775 - config_name: wa features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 3450 num_examples: 193 download_size: 0 dataset_size: 3450 - config_name: yi features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 9001 num_examples: 395 download_size: 0 dataset_size: 9001 - config_name: zh features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 33025 num_examples: 1879 download_size: 0 dataset_size: 33025 - config_name: zhw features: - name: word dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 67675 num_examples: 3828 download_size: 0 dataset_size: 67675 config_names: - 'no' - af - an - ar - az - be - bg - bn - br - bs - ca - cs - cy - da - de - el - eo - es - et - eu - fa - fi - fo - fr - fy - ga - gd - gl - gu - he - hi - hr - ht - hu - hy - ia - id - io - is - it - ja - ka - km - kn - ko - ku - ky - la - lb - lt - lv - mk - mr - ms - mt - nl - nn - pl - pt - rm - ro - ru - sk - sl - sq - sr - sv - sw - ta - te - th - tk - tl - tr - uk - ur - uz - vi - vo - wa - yi - zh - zhw --- # Dataset Card for SentiWS ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://sites.google.com/site/datascienceslab/projects/multilingualsentiment - **Repository:** https://www.kaggle.com/rtatman/sentiment-lexicons-for-81-languages - **Paper:** https://aclanthology.org/P14-2063/ - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary This dataset add sentiment lexicons for 81 languages generated via graph propagation based on a knowledge graph--a graphical representation of real-world entities and the links between them ### Supported Tasks and Leaderboards Sentiment-Classification ### Languages Afrikaans Aragonese Arabic Azerbaijani Belarusian Bulgarian Bengali Breton Bosnian Catalan; Valencian Czech Welsh Danish German Greek, Modern Esperanto Spanish; Castilian Estonian Basque Persian Finnish Faroese French Western Frisian Irish Scottish Gaelic; Gaelic Galician Gujarati Hebrew (modern) Hindi Croatian Haitian; Haitian Creole Hungarian Armenian Interlingua Indonesian Ido Icelandic Italian Japanese Georgian Khmer Kannada Korean Kurdish Kirghiz, Kyrgyz Latin Luxembourgish, Letzeburgesch Lithuanian Latvian Macedonian Marathi (Marāṭhī) Malay Maltese Dutch Norwegian Nynorsk Norwegian Polish Portuguese Romansh Romanian, Moldavian, Moldovan Russian Slovak Slovene Albanian Serbian Swedish Swahili Tamil Telugu Thai Turkmen Tagalog Turkish Ukrainian Urdu Uzbek Vietnamese Volapük Walloon Yiddish Chinese Zhoa ## Dataset Structure ### Data Instances ``` { "word":"die", "sentiment": 0, #"negative" } ``` ### Data Fields - word: one word as a string, - sentiment-score: the sentiment classification of the word as a string either negative (0) or positive (1) ### Data Splits [Needs More Information] ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information GNU General Public License v3. It is distributed here under the [GNU General Public License](http://www.gnu.org/licenses/gpl-3.0.html). Note that this is the full GPL, which allows many free uses, but does not allow its incorporation into any type of distributed proprietary software, even in part or in translation. For commercial applications please contact the dataset creators (see "Citation Information"). ### Citation Information This dataset was collected by Yanqing Chen and Steven Skiena. If you use it in your work, please cite the following paper: ```bibtex @inproceedings{chen-skiena-2014-building, title = "Building Sentiment Lexicons for All Major Languages", author = "Chen, Yanqing and Skiena, Steven", booktitle = "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)", month = jun, year = "2014", address = "Baltimore, Maryland", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/P14-2063", doi = "10.3115/v1/P14-2063", pages = "383--389", } ``` ### Contributions Thanks to [@KMFODA](https://github.com/KMFODA) for adding this dataset.

CarperAI/openai_summarize_tldr

CarperAI

--- dataset_info: features: - name: prompt dtype: string - name: label dtype: string splits: - name: train num_bytes: 181260841 num_examples: 116722 - name: valid num_bytes: 10018338 num_examples: 6447 - name: test num_bytes: 10198128 num_examples: 6553 download_size: 122973500 dataset_size: 201477307 --- # Dataset Card for "openai_summarize_tldr" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

knkarthick/dialogsum

knkarthick

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: cc-by-nc-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - summarization - text2text-generation - text-generation task_ids: [] pretty_name: DIALOGSum Corpus tags: - dialogue-summary - one-liner-summary - meeting-title - email-subject --- # Dataset Card for DIALOGSum Corpus ## Dataset Description ### Links - **Homepage:** https://aclanthology.org/2021.findings-acl.449 - **Repository:** https://github.com/cylnlp/dialogsum - **Paper:** https://aclanthology.org/2021.findings-acl.449 - **Point of Contact:** https://huggingface.co/knkarthick ### Dataset Summary DialogSum is a large-scale dialogue summarization dataset, consisting of 13,460 (Plus 100 holdout data for topic generation) dialogues with corresponding manually labeled summaries and topics. ### Languages English ## Dataset Structure ### Data Instances DialogSum is a large-scale dialogue summarization dataset, consisting of 13,460 dialogues (+1000 tests) split into train, test and validation. The first instance in the training set: {'id': 'train_0', 'summary': "Mr. Smith's getting a check-up, and Doctor Hawkins advises him to have one every year. Hawkins'll give some information about their classes and medications to help Mr. Smith quit smoking.", 'dialogue': "#Person1#: Hi, Mr. Smith. I'm Doctor Hawkins. Why are you here today?\n#Person2#: I found it would be a good idea to get a check-up.\n#Person1#: Yes, well, you haven't had one for 5 years. You should have one every year.\n#Person2#: I know. I figure as long as there is nothing wrong, why go see the doctor?\n#Person1#: Well, the best way to avoid serious illnesses is to find out about them early. So try to come at least once a year for your own good.\n#Person2#: Ok.\n#Person1#: Let me see here. Your eyes and ears look fine. Take a deep breath, please. Do you smoke, Mr. Smith?\n#Person2#: Yes.\n#Person1#: Smoking is the leading cause of lung cancer and heart disease, you know. You really should quit.\n#Person2#: I've tried hundreds of times, but I just can't seem to kick the habit.\n#Person1#: Well, we have classes and some medications that might help. I'll give you more information before you leave.\n#Person2#: Ok, thanks doctor.", 'topic': "get a check-up} ### Data Fields - dialogue: text of dialogue. - summary: human written summary of the dialogue. - topic: human written topic/one liner of the dialogue. - id: unique file id of an example. ### Data Splits - train: 12460 - val: 500 - test: 1500 - holdout: 100 [Only 3 features: id, dialogue, topic] ## Dataset Creation ### Curation Rationale In paper: We collect dialogue data for DialogSum from three public dialogue corpora, namely Dailydialog (Li et al., 2017), DREAM (Sun et al., 2019) and MuTual (Cui et al., 2019), as well as an English speaking practice website. These datasets contain face-to-face spoken dialogues that cover a wide range of daily-life topics, including schooling, work, medication, shopping, leisure, travel. Most conversations take place between friends, colleagues, and between service providers and customers. Compared with previous datasets, dialogues from DialogSum have distinct characteristics: Under rich real-life scenarios, including more diverse task-oriented scenarios; Have clear communication patterns and intents, which is valuable to serve as summarization sources; Have a reasonable length, which comforts the purpose of automatic summarization. We ask annotators to summarize each dialogue based on the following criteria: Convey the most salient information; Be brief; Preserve important named entities within the conversation; Be written from an observer perspective; Be written in formal language. ### Who are the source language producers? linguists ### Who are the annotators? language experts ## Licensing Information CC BY-NC-SA 4.0 ## Citation Information ``` @inproceedings{chen-etal-2021-dialogsum, title = "{D}ialog{S}um: {A} Real-Life Scenario Dialogue Summarization Dataset", author = "Chen, Yulong and Liu, Yang and Chen, Liang and Zhang, Yue", booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-acl.449", doi = "10.18653/v1/2021.findings-acl.449", pages = "5062--5074", ``` ## Contributions Thanks to [@cylnlp](https://github.com/cylnlp) for adding this dataset.

wiki_lingua

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - ar - cs - de - en - es - fr - hi - id - it - ja - ko - nl - pt - ru - th - tr - vi - zh license: - cc-by-3.0 multilinguality: - multilingual size_categories: - 10K<n<100K - 1K<n<10K source_datasets: - original task_categories: - summarization task_ids: [] paperswithcode_id: wikilingua pretty_name: WikiLingua dataset_info: - config_name: arabic features: - name: url dtype: string - name: article sequence: - name: section_name dtype: string - name: document dtype: string - name: summary dtype: string - name: english_url dtype: string - name: english_section_name dtype: string splits: - name: train num_bytes: 119116119 num_examples: 9995 download_size: 119358890 dataset_size: 119116119 - config_name: chinese features: - name: url dtype: string - name: article sequence: - name: section_name dtype: string - name: document dtype: string - name: summary dtype: string - name: english_url dtype: string - name: english_section_name dtype: string splits: - name: train num_bytes: 41170689 num_examples: 6541 download_size: 41345464 dataset_size: 41170689 - config_name: czech features: - name: url dtype: string - name: article sequence: - name: section_name dtype: string - name: document dtype: string - name: summary dtype: string - name: english_url dtype: string - name: english_section_name dtype: string splits: - name: train num_bytes: 20816390 num_examples: 2520 download_size: 20894511 dataset_size: 20816390 - config_name: dutch features: - name: url dtype: string - name: article sequence: - name: section_name dtype: string - name: document dtype: string - name: summary dtype: string - name: english_url dtype: string - name: english_section_name dtype: string splits: - name: train num_bytes: 87258040 num_examples: 10862 download_size: 87533442 dataset_size: 87258040 - config_name: english features: - name: url dtype: string - name: article sequence: - name: section_name dtype: string - name: document dtype: string - name: summary dtype: string splits: - name: train num_bytes: 333700114 num_examples: 57945 download_size: 338036185 dataset_size: 333700114 - config_name: french features: - name: url dtype: string - name: article sequence: - name: section_name dtype: string - name: document dtype: string - name: summary dtype: string - name: english_url dtype: string - name: english_section_name dtype: string splits: - name: train num_bytes: 197550376 num_examples: 21690 download_size: 198114157 dataset_size: 197550376 - config_name: german features: - name: url dtype: string - name: article sequence: - name: section_name dtype: string - name: document dtype: string - name: summary dtype: string - name: english_url dtype: string - name: english_section_name dtype: string splits: - name: train num_bytes: 168674340 num_examples: 20103 download_size: 169195050 dataset_size: 168674340 - config_name: hindi features: - name: url dtype: string - name: article sequence: - name: section_name dtype: string - name: document dtype: string - name: summary dtype: string - name: english_url dtype: string - name: english_section_name dtype: string splits: - name: train num_bytes: 63785051 num_examples: 3402 download_size: 63874759 dataset_size: 63785051 - config_name: indonesian features: - name: url dtype: string - name: article sequence: - name: section_name dtype: string - name: document dtype: string - name: summary dtype: string - name: english_url dtype: string - name: english_section_name dtype: string splits: - name: train num_bytes: 136408861 num_examples: 16308 download_size: 136833587 dataset_size: 136408861 - config_name: italian features: - name: url dtype: string - name: article sequence: - name: section_name dtype: string - name: document dtype: string - name: summary dtype: string - name: english_url dtype: string - name: english_section_name dtype: string splits: - name: train num_bytes: 138119527 num_examples: 17673 download_size: 138578956 dataset_size: 138119527 - config_name: japanese features: - name: url dtype: string - name: article sequence: - name: section_name dtype: string - name: document dtype: string - name: summary dtype: string - name: english_url dtype: string - name: english_section_name dtype: string splits: - name: train num_bytes: 40145031 num_examples: 4372 download_size: 40259570 dataset_size: 40145031 - config_name: korean features: - name: url dtype: string - name: article sequence: - name: section_name dtype: string - name: document dtype: string - name: summary dtype: string - name: english_url dtype: string - name: english_section_name dtype: string splits: - name: train num_bytes: 38647614 num_examples: 4111 download_size: 38748961 dataset_size: 38647614 - config_name: portuguese features: - name: url dtype: string - name: article sequence: - name: section_name dtype: string - name: document dtype: string - name: summary dtype: string - name: english_url dtype: string - name: english_section_name dtype: string splits: - name: train num_bytes: 204270845 num_examples: 28143 download_size: 204997686 dataset_size: 204270845 - config_name: russian features: - name: url dtype: string - name: article sequence: - name: section_name dtype: string - name: document dtype: string - name: summary dtype: string - name: english_url dtype: string - name: english_section_name dtype: string splits: - name: train num_bytes: 241924032 num_examples: 18143 download_size: 242377242 dataset_size: 241924032 - config_name: spanish features: - name: url dtype: string - name: article sequence: - name: section_name dtype: string - name: document dtype: string - name: summary dtype: string - name: english_url dtype: string - name: english_section_name dtype: string splits: - name: train num_bytes: 314618618 num_examples: 38795 download_size: 315609530 dataset_size: 314618618 - config_name: thai features: - name: url dtype: string - name: article sequence: - name: section_name dtype: string - name: document dtype: string - name: summary dtype: string - name: english_url dtype: string - name: english_section_name dtype: string splits: - name: train num_bytes: 86982851 num_examples: 5093 download_size: 87104200 dataset_size: 86982851 - config_name: turkish features: - name: url dtype: string - name: article sequence: - name: section_name dtype: string - name: document dtype: string - name: summary dtype: string - name: english_url dtype: string - name: english_section_name dtype: string splits: - name: train num_bytes: 11371821 num_examples: 1512 download_size: 11405793 dataset_size: 11371821 - config_name: vietnamese features: - name: url dtype: string - name: article sequence: - name: section_name dtype: string - name: document dtype: string - name: summary dtype: string - name: english_url dtype: string - name: english_section_name dtype: string splits: - name: train num_bytes: 69868788 num_examples: 6616 download_size: 70024093 dataset_size: 69868788 config_names: - arabic - chinese - czech - dutch - english - french - german - hindi - indonesian - italian - japanese - korean - portuguese - russian - spanish - thai - turkish - vietnamese --- # Dataset Card for "wiki_lingua" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** [URL](https://github.com/esdurmus/Wikilingua) - **Paper:** [WikiLingua: A Multilingual Abstractive Summarization Dataset](https://arxiv.org/abs/2010.03093) ### Dataset Summary We introduce WikiLingua, a large-scale, multilingual dataset for the evaluation of cross-lingual abstractive summarization systems. We extract article and summary pairs in 18 languages from WikiHow, a high quality, collaborative resource of how-to guides on a diverse set of topics written by human authors. We create gold-standard article-summary alignments across languages by aligning the images that are used to describe each how-to step in an article. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The table below shows number of article-summary pairs with a parallel article-summary pair in English. ______________________________ | Language | Num. parallel | | ----------- | --------------| | English | 141,457 | | Spanish | 113,215 | | Portuguese | 81,695 | | French | 63,692 | | German | 58,375 | | Russian | 52,928 | | Italian | 50,968 | | Indonesian | 47,511 | | Dutch | 31,270 | | Arabic | 29,229 | | Vietnamese | 19,600 | | Chinese | 18,887 | | Thai | 14,770 | | Japanese | 12,669 | | Korean | 12,189 | | Hindi | 9,929 | | Czech | 7,200 | | Turkish | 4,503 | ## Dataset Structure ### Data Instances ``` { 'article': { 'document': ['make sure that the area is a safe place, especially if you plan on walking home at night. It’s always a good idea to practice the buddy system. Have a friend meet up and walk with you. Research the bus, train, or streetcar routes available in your area to find safe and affordable travel to your destination. Make sure you check the schedule for your outgoing and return travel. Some public transportation will cease to run late at night. Be sure if you take public transportation to the venue that you will also be able to get home late at night. Check the routes. Even if some public transit is still running late at night, the routing may change. Some may run express past many of the stops, or not travel all the way to the ends. Be sure that your stop will still be available when you need it for your return trip. If you are taking public transit in a vulnerable state after drinking, it is always a good idea to travel in groups. Having friends available is a good way to stay safe and make sure that you reach your destination. This is more expensive option than a taxi or ride share service, but could be a fun and fancy way to stay safe and ensure that you will have a ride home. Plan this service in advance with a scheduled time to pick you up from your home and the venue. You want to be sure that the service will still be available when you need to get home. This may be easy in a large city, but taxis may be less frequent in smaller towns. This is especially true late at night, so this is a less reliable option than scheduling a ride in advance. Have a friend accompany you and help you flag a cab to make sure you are able to get one. Set up a plan to call a friend when you get home to make sure that you made it safely to your destination. If there are no taxis readily available call a local service to send a car to pick you up. You can share a ride with your friends, or other people using the app at the same moment. If you are in a vulnerable state it is best to share the ride with your friends to make sure you get home safe. You can request the car to yourself rather than sharing rides with strangers. If you travel home on your own or are the last of your group to be dropped off, make plans to call a friend when you get home so they know you made it safely to your destination. There may be a designated driver service in your area which can chauffeur your group. Make reservations with them in advance and keep their contact information handy while you are drinking.', "Designating a driver is a very popular tactic to avoid drinking and driving. It is important to plan in advance, because your brain function will slow down and your decision making skills will be impaired once you start drinking. Decide before you begin drinking that you will not drive. Figure out who will be getting you home before you leave. Make sure this person is responsible and keep them in your sight while you are drinking. Have their contact information handy in case you can’t find them when you are ready to leave. Choose a friend who doesn’t drink alcohol. You likely have someone in your friend group who doesn’t drink. This person is the most likely to remain sober. Decide on one person who will remain sober. You can take turns within your friend group, alternating who will be the designated driver on each occasion. Be sure that the designated driver actually remains sober. The person who has drank the least is still not sober. If you don’t have your car with you, you can guarantee that you won’t make the choice to drive it home. If you are drinking at your home. Give your keys to a responsible friend to ensure that you don't choose to drive somewhere after you have been drinking. It may be tempting to stay longer or leave with someone else. Stick to the plan you made in advance and only leave with your sober, designated driver. Keep the phone number of your driver handy in case you can't find them when you are ready to leave. If your designated driver drinks alcohol, find alternate transportation to get home.", 'If you have been drinking at all you are at least on the spectrum of drunkenness. You could be showing signs of impairment and slower brain function including lack of motor skills and slower reaction time, leading to the inability to operate a motor vehicle. Some of these signs could be: Poor balance or stumbling. Difficulty speaking clearly and slurred words. Abnormal behavior leading to you doing things you wouldn’t normally do if you were sober. As soon as you notice that you are showing signs of impairment, give your keys to a friend, the host or the bartender to ensure that you won’t drive until you are sober. Make sure to only give them your car key. Hold onto your house keys. If your friend, the host or the bartender are advising you not to drive, you are likely too drunk. Listen to their advice and acknowledge that they are trying to help you. Bystander intervention is common when it comes to drinking and driving. Many people will be willing to step in, take your keys and help you get home safely. If no one if offering to help, you may need to ask. Take a ride from a sober friend. It is best to get in a car with someone you trust when you are in this vulnerable state. Allow the host or bartender to call a cab or car service to take you home. If you are having a difficult time finding a safe way to get home, find a place to stay which does not involve you driving. Ask the host of the party if there is a place you can sleep. Give them your keys and ask that they keep them in a safe place until the morning. Stay with a friend if they live nearby and are on their way home. Find a hotel within walking distance. Call them to book a room, or have a friend help you secure one. Ask the friend if they will walk you to the hotel and make sure you get checked in safely. There are people in your life who care about you and want to be sure that you are safe. It may seem scary or embarrassing to call your parents or your siblings if you are too drunk to drive, but they will be glad you did. Your safety is the most important. You may need your phone to call someone for a ride or get help from a friend. Be sure to charge your phone before you leave the house. It is also a good idea to bring a charger with you in case your battery dies before the end of the night or you end up staying where you are and need to get home the next morning. You may also want to invest in a portable battery charger for your phone should there not be a power outlet available. Make sure it is fully charged before you leave your house. Keep it handy in your pocket or your bag throughout the night.' ], 'section_name': ['Finding Other Transportation', 'Designating a Driver', 'Staying Safe' ], 'summary': ['Walk to the venue where you will be drinking if it is close enough. Take public transit. Show up in style by hiring a limo or black car service. Flag a taxi cab for a convenient option to get where you’re going. Request a rideshare service like Uber or Lyft using an app on your phone. Reserve a designated driver service.', 'Plan in advance. Assign a designated driver. Leave your car at home. Leave the venue with your designated driver.', 'Pay attention to your body. Give up your keys. Listen to other people. Accept help. Stay where you are. Have an emergency back-up plan. Make sure that your phone is charged.' ] }, 'url': 'https://www.wikihow.com/Avoid-Drinking-and-Driving' } ``` ### Data Fields - `url`: WikiHow URL of the article - `article`: A dictionary containing `section_name`, `document` and `summary` - `section_name`: List of section headings in an article - `document`: List of documents, one for each section in the `section_name` list - `summary`: List of summarized document ### Data Splits | | train | |:-----------|--------:| | arabic | 9995 | | chinese | 6541 | | czech | 2520 | | dutch | 10862 | | english | 57945 | | french | 21690 | | german | 20103 | | hindi | 3402 | | indonesian | 16308 | | italian | 17673 | | japanese | 4372 | | korean | 4111 | | portuguese | 28143 | | russian | 18143 | | spanish | 6616 | | thai | 5093 | | turkish | 1512 | | vietnamese | 6616 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information - Article provided by wikiHow https://www.wikihow.com/Main-Page, a wiki building the world's largest, highest quality how-to manual. Please edit this article and find author credits at wikiHow.com. Content on wikiHow can be shared under a [Creative Commons license](http://creativecommons.org/licenses/by-nc-sa/3.0/). - Refer to [this webpage](https://www.wikihow.com/wikiHow:Attribution) for the specific attribution guidelines. - also see https://gem-benchmark.com/data_cards/WikiLingua ### Citation Information ```bibtex @inproceedings{ladhak-etal-2020-wikilingua, title = "{W}iki{L}ingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization", author = "Ladhak, Faisal and Durmus, Esin and Cardie, Claire and McKeown, Kathleen", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.findings-emnlp.360", doi = "10.18653/v1/2020.findings-emnlp.360", pages = "4034--4048", } ``` ### Contributions Thanks to [@katnoria](https://github.com/katnoria) for adding this dataset.

mteb/tatoeba-bitext-mining

mteb

--- language: - eng - sqi - fry - kur - tur - deu - nld - ron - ang - ido - jav - isl - slv - cym - kaz - est - heb - gla - mar - lat - bel - pms - gle - pes - nob - bul - cbk - hun - uig - rus - spa - hye - tel - afr - mon - arz - hrv - nov - gsw - nds - ukr - uzb - lit - ina - lfn - zsm - ita - cmn - lvs - glg - ceb - bre - ben - swg - arq - kab - fra - por - tat - oci - pol - war - aze - vie - nno - cha - mhr - dan - ell - amh - pam - hsb - srp - epo - kzj - awa - fao - mal - ile - bos - cor - cat - eus - yue - swe - dtp - kat - jpn - csb - xho - orv - ind - tuk - max - swh - hin - dsb - ber - tam - slk - tgl - ast - mkd - khm - ces - tzl - urd - ara - kor - yid - fin - tha - wuu ---

pubmed_qa

--- annotations_creators: - expert-generated - machine-generated language_creators: - expert-generated language: - en license: - mit multilinguality: - monolingual size_categories: - 100K<n<1M - 10K<n<100K - 1K<n<10K source_datasets: - original task_categories: - question-answering task_ids: - multiple-choice-qa paperswithcode_id: pubmedqa pretty_name: PubMedQA dataset_info: - config_name: pqa_labeled features: - name: pubid dtype: int32 - name: question dtype: string - name: context sequence: - name: contexts dtype: string - name: labels dtype: string - name: meshes dtype: string - name: reasoning_required_pred dtype: string - name: reasoning_free_pred dtype: string - name: long_answer dtype: string - name: final_decision dtype: string splits: - name: train num_bytes: 2089200 num_examples: 1000 download_size: 687882700 dataset_size: 2089200 - config_name: pqa_unlabeled features: - name: pubid dtype: int32 - name: question dtype: string - name: context sequence: - name: contexts dtype: string - name: labels dtype: string - name: meshes dtype: string - name: long_answer dtype: string splits: - name: train num_bytes: 125938502 num_examples: 61249 download_size: 687882700 dataset_size: 125938502 - config_name: pqa_artificial features: - name: pubid dtype: int32 - name: question dtype: string - name: context sequence: - name: contexts dtype: string - name: labels dtype: string - name: meshes dtype: string - name: long_answer dtype: string - name: final_decision dtype: string splits: - name: train num_bytes: 443554667 num_examples: 211269 download_size: 687882700 dataset_size: 443554667 config_names: - pqa_artificial - pqa_labeled - pqa_unlabeled --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [PUBMED_QA homepage](https://pubmedqa.github.io/ ) - **Repository:** [PUBMED_QA repository](https://github.com/pubmedqa/pubmedqa) - **Paper:** [PUBMED_QA: A Dataset for Biomedical Research Question Answering](https://arxiv.org/abs/1909.06146) - **Leaderboard:** [PUBMED_QA: Leaderboard](https://pubmedqa.github.io/) ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@tuner007](https://github.com/tuner007) for adding this dataset.

jfleg

--- annotations_creators: - expert-generated language_creators: - found language: - en license: - cc-by-nc-sa-4.0 multilinguality: - monolingual - other-language-learner size_categories: - 1K<n<10K source_datasets: - extended|other-GUG-grammaticality-judgements task_categories: - text2text-generation task_ids: [] paperswithcode_id: jfleg pretty_name: JHU FLuency-Extended GUG corpus tags: - grammatical-error-correction dataset_info: features: - name: sentence dtype: string - name: corrections sequence: string splits: - name: validation num_bytes: 379991 num_examples: 755 - name: test num_bytes: 379711 num_examples: 748 download_size: 731111 dataset_size: 759702 --- # Dataset Card for JFLEG ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Github](https://github.com/keisks/jfleg) - **Repository:** [Github](https://github.com/keisks/jfleg) - **Paper:** [Napoles et al., 2020](https://www.aclweb.org/anthology/E17-2037/) - **Leaderboard:** [Leaderboard](https://github.com/keisks/jfleg#leader-board-published-results) - **Point of Contact:** Courtney Napoles, Keisuke Sakaguchi ### Dataset Summary JFLEG (JHU FLuency-Extended GUG) is an English grammatical error correction (GEC) corpus. It is a gold standard benchmark for developing and evaluating GEC systems with respect to fluency (extent to which a text is native-sounding) as well as grammaticality. For each source document, there are four human-written corrections. ### Supported Tasks and Leaderboards Grammatical error correction. ### Languages English (native as well as L2 writers) ## Dataset Structure ### Data Instances Each instance contains a source sentence and four corrections. For example: ```python { 'sentence': "They are moved by solar energy ." 'corrections': [ "They are moving by solar energy .", "They are moved by solar energy .", "They are moved by solar energy .", "They are propelled by solar energy ." ] } ``` ### Data Fields - sentence: original sentence written by an English learner - corrections: corrected versions by human annotators. The order of the annotations are consistent (eg first sentence will always be written by annotator "ref0"). ### Data Splits - This dataset contains 1511 examples in total and comprise a dev and test split. - There are 754 and 747 source sentences for dev and test, respectively. - Each sentence has 4 corresponding corrected versions. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information This work is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/). ### Citation Information This benchmark was proposed by [Napoles et al., 2020](https://www.aclweb.org/anthology/E17-2037/). ``` @InProceedings{napoles-sakaguchi-tetreault:2017:EACLshort, author = {Napoles, Courtney and Sakaguchi, Keisuke and Tetreault, Joel}, title = {JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction}, booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers}, month = {April}, year = {2017}, address = {Valencia, Spain}, publisher = {Association for Computational Linguistics}, pages = {229--234}, url = {http://www.aclweb.org/anthology/E17-2037} } @InProceedings{heilman-EtAl:2014:P14-2, author = {Heilman, Michael and Cahill, Aoife and Madnani, Nitin and Lopez, Melissa and Mulholland, Matthew and Tetreault, Joel}, title = {Predicting Grammaticality on an Ordinal Scale}, booktitle = {Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)}, month = {June}, year = {2014}, address = {Baltimore, Maryland}, publisher = {Association for Computational Linguistics}, pages = {174--180}, url = {http://www.aclweb.org/anthology/P14-2029} } ``` ### Contributions Thanks to [@j-chim](https://github.com/j-chim) for adding this dataset.

jmhessel/newyorker_caption_contest

jmhessel

--- annotations_creators: - expert-generated - crowdsourced - found language_creators: - crowdsourced - expert-generated language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - image-to-text - multiple-choice - text-classification - text-generation - visual-question-answering - other - text2text-generation task_ids: - multi-class-classification - language-modeling - visual-question-answering - explanation-generation pretty_name: newyorker_caption_contest tags: - humor - caption contest - new yorker dataset_info: - config_name: explanation features: - name: image dtype: image - name: contest_number dtype: int32 - name: image_location dtype: string - name: image_description dtype: string - name: image_uncanny_description dtype: string - name: entities sequence: string - name: questions sequence: string - name: caption_choices dtype: string - name: from_description dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 133827514.64 num_examples: 2340 - name: validation num_bytes: 8039885.0 num_examples: 130 - name: test num_bytes: 6863533.0 num_examples: 131 download_size: 139737042 dataset_size: 148730932.64 - config_name: explanation_1 features: - name: image dtype: image - name: contest_number dtype: int32 - name: image_location dtype: string - name: image_description dtype: string - name: image_uncanny_description dtype: string - name: entities sequence: string - name: questions sequence: string - name: caption_choices dtype: string - name: from_description dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 136614332.45999998 num_examples: 2358 - name: validation num_bytes: 7911995.0 num_examples: 128 - name: test num_bytes: 8039885.0 num_examples: 130 download_size: 134637839 dataset_size: 152566212.45999998 - config_name: explanation_2 features: - name: image dtype: image - name: contest_number dtype: int32 - name: image_location dtype: string - name: image_description dtype: string - name: image_uncanny_description dtype: string - name: entities sequence: string - name: questions sequence: string - name: caption_choices dtype: string - name: from_description dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 138337491.342 num_examples: 2346 - name: validation num_bytes: 7460490.0 num_examples: 132 - name: test num_bytes: 7911995.0 num_examples: 128 download_size: 138271185 dataset_size: 153709976.342 - config_name: explanation_3 features: - name: image dtype: image - name: contest_number dtype: int32 - name: image_location dtype: string - name: image_description dtype: string - name: image_uncanny_description dtype: string - name: entities sequence: string - name: questions sequence: string - name: caption_choices dtype: string - name: from_description dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 138247435.342 num_examples: 2334 - name: validation num_bytes: 7911920.0 num_examples: 130 - name: test num_bytes: 7460490.0 num_examples: 132 download_size: 136862726 dataset_size: 153619845.342 - config_name: explanation_4 features: - name: image dtype: image - name: contest_number dtype: int32 - name: image_location dtype: string - name: image_description dtype: string - name: image_uncanny_description dtype: string - name: entities sequence: string - name: questions sequence: string - name: caption_choices dtype: string - name: from_description dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 141175335.3 num_examples: 2340 - name: validation num_bytes: 6863533.0 num_examples: 131 - name: test num_bytes: 7911920.0 num_examples: 130 download_size: 140501251 dataset_size: 155950788.3 - config_name: explanation_from_pixels features: - name: image dtype: image - name: contest_number dtype: int32 - name: caption_choices dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 23039316.0 num_examples: 390 - name: validation num_bytes: 7956182.0 num_examples: 130 - name: test num_bytes: 6778892.0 num_examples: 131 download_size: 37552582 dataset_size: 37774390.0 - config_name: explanation_from_pixels_1 features: - name: image dtype: image - name: contest_number dtype: int32 - name: caption_choices dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 21986652.0 num_examples: 393 - name: validation num_bytes: 7831556.0 num_examples: 128 - name: test num_bytes: 7956182.0 num_examples: 130 download_size: 37534409 dataset_size: 37774390.0 - config_name: explanation_from_pixels_2 features: - name: image dtype: image - name: contest_number dtype: int32 - name: caption_choices dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 22566608.0 num_examples: 391 - name: validation num_bytes: 7376225.0 num_examples: 132 - name: test num_bytes: 7831556.0 num_examples: 128 download_size: 37544724 dataset_size: 37774389.0 - config_name: explanation_from_pixels_3 features: - name: image dtype: image - name: contest_number dtype: int32 - name: caption_choices dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 22566629.0 num_examples: 389 - name: validation num_bytes: 7831536.0 num_examples: 130 - name: test num_bytes: 7376225.0 num_examples: 132 download_size: 37573931 dataset_size: 37774390.0 - config_name: explanation_from_pixels_4 features: - name: image dtype: image - name: contest_number dtype: int32 - name: caption_choices dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 23163962.0 num_examples: 390 - name: validation num_bytes: 6778892.0 num_examples: 131 - name: test num_bytes: 7831536.0 num_examples: 130 download_size: 37582524 dataset_size: 37774390.0 - config_name: matching features: - name: image dtype: image - name: contest_number dtype: int32 - name: image_location dtype: string - name: image_description dtype: string - name: image_uncanny_description dtype: string - name: entities sequence: string - name: questions sequence: string - name: caption_choices sequence: string - name: from_description dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 618272766.36 num_examples: 9792 - name: validation num_bytes: 34157757.0 num_examples: 531 - name: test num_bytes: 29813118.0 num_examples: 528 download_size: 594460072 dataset_size: 682243641.36 - config_name: matching_1 features: - name: image dtype: image - name: contest_number dtype: int32 - name: image_location dtype: string - name: image_description dtype: string - name: image_uncanny_description dtype: string - name: entities sequence: string - name: questions sequence: string - name: caption_choices sequence: string - name: from_description dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 593200158.116 num_examples: 9684 - name: validation num_bytes: 36712942.0 num_examples: 546 - name: test num_bytes: 34157757.0 num_examples: 531 download_size: 563587231 dataset_size: 664070857.116 - config_name: matching_2 features: - name: image dtype: image - name: contest_number dtype: int32 - name: image_location dtype: string - name: image_description dtype: string - name: image_uncanny_description dtype: string - name: entities sequence: string - name: questions sequence: string - name: caption_choices sequence: string - name: from_description dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 591676321.09 num_examples: 9630 - name: validation num_bytes: 33697178.0 num_examples: 540 - name: test num_bytes: 36712942.0 num_examples: 546 download_size: 571864348 dataset_size: 662086441.09 - config_name: matching_3 features: - name: image dtype: image - name: contest_number dtype: int32 - name: image_location dtype: string - name: image_description dtype: string - name: image_uncanny_description dtype: string - name: entities sequence: string - name: questions sequence: string - name: caption_choices sequence: string - name: from_description dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 615620189.53 num_examples: 9630 - name: validation num_bytes: 34829502.0 num_examples: 546 - name: test num_bytes: 33697178.0 num_examples: 540 download_size: 571744845 dataset_size: 684146869.53 - config_name: matching_4 features: - name: image dtype: image - name: contest_number dtype: int32 - name: image_location dtype: string - name: image_description dtype: string - name: image_uncanny_description dtype: string - name: entities sequence: string - name: questions sequence: string - name: caption_choices sequence: string - name: from_description dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 609696610.648 num_examples: 9702 - name: validation num_bytes: 29813118.0 num_examples: 528 - name: test num_bytes: 34829502.0 num_examples: 546 download_size: 592174904 dataset_size: 674339230.648 - config_name: matching_from_pixels features: - name: image dtype: image - name: contest_number dtype: int32 - name: caption_choices sequence: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 101439044.384 num_examples: 1632 - name: validation num_bytes: 33714551.0 num_examples: 531 - name: test num_bytes: 29368704.0 num_examples: 528 download_size: 139733134 dataset_size: 164522299.384 - config_name: matching_from_pixels_1 features: - name: image dtype: image - name: contest_number dtype: int32 - name: caption_choices sequence: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 94090646.83 num_examples: 1614 - name: validation num_bytes: 36257141.0 num_examples: 546 - name: test num_bytes: 33714551.0 num_examples: 531 download_size: 137278691 dataset_size: 164062338.82999998 - config_name: matching_from_pixels_2 features: - name: image dtype: image - name: contest_number dtype: int32 - name: caption_choices sequence: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 96253584.505 num_examples: 1605 - name: validation num_bytes: 33236000.0 num_examples: 540 - name: test num_bytes: 36257141.0 num_examples: 546 download_size: 137890850 dataset_size: 165746725.505 - config_name: matching_from_pixels_3 features: - name: image dtype: image - name: contest_number dtype: int32 - name: caption_choices sequence: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 99928910.28 num_examples: 1605 - name: validation num_bytes: 34380303.0 num_examples: 546 - name: test num_bytes: 33236000.0 num_examples: 540 download_size: 139585876 dataset_size: 167545213.28 - config_name: matching_from_pixels_4 features: - name: image dtype: image - name: contest_number dtype: int32 - name: caption_choices sequence: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 102509197.79 num_examples: 1617 - name: validation num_bytes: 29368704.0 num_examples: 528 - name: test num_bytes: 34380303.0 num_examples: 546 download_size: 138725891 dataset_size: 166258204.79000002 - config_name: ranking features: - name: image dtype: image - name: contest_number dtype: int32 - name: image_location dtype: string - name: image_description dtype: string - name: image_uncanny_description dtype: string - name: entities sequence: string - name: questions sequence: string - name: caption_choices sequence: string - name: from_description dtype: string - name: winner_source dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 594615535.632 num_examples: 9576 - name: validation num_bytes: 32624105.0 num_examples: 507 - name: test num_bytes: 28907567.0 num_examples: 513 download_size: 571604579 dataset_size: 656147207.632 - config_name: ranking_1 features: - name: image dtype: image - name: contest_number dtype: int32 - name: image_location dtype: string - name: image_description dtype: string - name: image_uncanny_description dtype: string - name: entities sequence: string - name: questions sequence: string - name: caption_choices sequence: string - name: from_description dtype: string - name: winner_source dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 580099188.9 num_examples: 9450 - name: validation num_bytes: 35332200.0 num_examples: 534 - name: test num_bytes: 32624105.0 num_examples: 507 download_size: 546559254 dataset_size: 648055493.9 - config_name: ranking_2 features: - name: image dtype: image - name: contest_number dtype: int32 - name: image_location dtype: string - name: image_description dtype: string - name: image_uncanny_description dtype: string - name: entities sequence: string - name: questions sequence: string - name: caption_choices sequence: string - name: from_description dtype: string - name: winner_source dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 566811450.504 num_examples: 9306 - name: validation num_bytes: 32519173.0 num_examples: 531 - name: test num_bytes: 35332200.0 num_examples: 534 download_size: 544444097 dataset_size: 634662823.504 - config_name: ranking_3 features: - name: image dtype: image - name: contest_number dtype: int32 - name: image_location dtype: string - name: image_description dtype: string - name: image_uncanny_description dtype: string - name: entities sequence: string - name: questions sequence: string - name: caption_choices sequence: string - name: from_description dtype: string - name: winner_source dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 577828323.272 num_examples: 9324 - name: validation num_bytes: 34072817.0 num_examples: 531 - name: test num_bytes: 32519173.0 num_examples: 531 download_size: 548880699 dataset_size: 644420313.272 - config_name: ranking_4 features: - name: image dtype: image - name: contest_number dtype: int32 - name: image_location dtype: string - name: image_description dtype: string - name: image_uncanny_description dtype: string - name: entities sequence: string - name: questions sequence: string - name: caption_choices sequence: string - name: from_description dtype: string - name: winner_source dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 593388719.232 num_examples: 9432 - name: validation num_bytes: 28907567.0 num_examples: 513 - name: test num_bytes: 34072817.0 num_examples: 531 download_size: 562902941 dataset_size: 656369103.232 - config_name: ranking_from_pixels features: - name: image dtype: image - name: contest_number dtype: int32 - name: caption_choices sequence: string - name: winner_source dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 101282973.752 num_examples: 1596 - name: validation num_bytes: 32072331.0 num_examples: 506 - name: test num_bytes: 28550057.0 num_examples: 513 download_size: 134283256 dataset_size: 161905361.752 - config_name: ranking_from_pixels_1 features: - name: image dtype: image - name: contest_number dtype: int32 - name: caption_choices sequence: string - name: winner_source dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 93123370.15 num_examples: 1575 - name: validation num_bytes: 34965110.0 num_examples: 534 - name: test num_bytes: 32072331.0 num_examples: 506 download_size: 130879365 dataset_size: 160160811.15 - config_name: ranking_from_pixels_2 features: - name: image dtype: image - name: contest_number dtype: int32 - name: caption_choices sequence: string - name: winner_source dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 93496576.85 num_examples: 1550 - name: validation num_bytes: 32145436.0 num_examples: 531 - name: test num_bytes: 34965110.0 num_examples: 534 download_size: 131637359 dataset_size: 160607122.85 - config_name: ranking_from_pixels_3 features: - name: image dtype: image - name: contest_number dtype: int32 - name: caption_choices sequence: string - name: winner_source dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 93840620.26 num_examples: 1553 - name: validation num_bytes: 33718821.0 num_examples: 531 - name: test num_bytes: 32145436.0 num_examples: 531 download_size: 133214495 dataset_size: 159704877.26 - config_name: ranking_from_pixels_4 features: - name: image dtype: image - name: contest_number dtype: int32 - name: caption_choices sequence: string - name: winner_source dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 99008131.43 num_examples: 1571 - name: validation num_bytes: 28550057.0 num_examples: 513 - name: test num_bytes: 33718821.0 num_examples: 531 download_size: 136230399 dataset_size: 161277009.43 configs: - config_name: explanation data_files: - split: train path: explanation/train-* - split: validation path: explanation/validation-* - split: test path: explanation/test-* - config_name: explanation_1 data_files: - split: train path: explanation_1/train-* - split: validation path: explanation_1/validation-* - split: test path: explanation_1/test-* - config_name: explanation_2 data_files: - split: train path: explanation_2/train-* - split: validation path: explanation_2/validation-* - split: test path: explanation_2/test-* - config_name: explanation_3 data_files: - split: train path: explanation_3/train-* - split: validation path: explanation_3/validation-* - split: test path: explanation_3/test-* - config_name: explanation_4 data_files: - split: train path: explanation_4/train-* - split: validation path: explanation_4/validation-* - split: test path: explanation_4/test-* - config_name: explanation_from_pixels data_files: - split: train path: explanation_from_pixels/train-* - split: validation path: explanation_from_pixels/validation-* - split: test path: explanation_from_pixels/test-* - config_name: explanation_from_pixels_1 data_files: - split: train path: explanation_from_pixels_1/train-* - split: validation path: explanation_from_pixels_1/validation-* - split: test path: explanation_from_pixels_1/test-* - config_name: explanation_from_pixels_2 data_files: - split: train path: explanation_from_pixels_2/train-* - split: validation path: explanation_from_pixels_2/validation-* - split: test path: explanation_from_pixels_2/test-* - config_name: explanation_from_pixels_3 data_files: - split: train path: explanation_from_pixels_3/train-* - split: validation path: explanation_from_pixels_3/validation-* - split: test path: explanation_from_pixels_3/test-* - config_name: explanation_from_pixels_4 data_files: - split: train path: explanation_from_pixels_4/train-* - split: validation path: explanation_from_pixels_4/validation-* - split: test path: explanation_from_pixels_4/test-* - config_name: matching data_files: - split: train path: matching/train-* - split: validation path: matching/validation-* - split: test path: matching/test-* - config_name: matching_1 data_files: - split: train path: matching_1/train-* - split: validation path: matching_1/validation-* - split: test path: matching_1/test-* - config_name: matching_2 data_files: - split: train path: matching_2/train-* - split: validation path: matching_2/validation-* - split: test path: matching_2/test-* - config_name: matching_3 data_files: - split: train path: matching_3/train-* - split: validation path: matching_3/validation-* - split: test path: matching_3/test-* - config_name: matching_4 data_files: - split: train path: matching_4/train-* - split: validation path: matching_4/validation-* - split: test path: matching_4/test-* - config_name: matching_from_pixels data_files: - split: train path: matching_from_pixels/train-* - split: validation path: matching_from_pixels/validation-* - split: test path: matching_from_pixels/test-* - config_name: matching_from_pixels_1 data_files: - split: train path: matching_from_pixels_1/train-* - split: validation path: matching_from_pixels_1/validation-* - split: test path: matching_from_pixels_1/test-* - config_name: matching_from_pixels_2 data_files: - split: train path: matching_from_pixels_2/train-* - split: validation path: matching_from_pixels_2/validation-* - split: test path: matching_from_pixels_2/test-* - config_name: matching_from_pixels_3 data_files: - split: train path: matching_from_pixels_3/train-* - split: validation path: matching_from_pixels_3/validation-* - split: test path: matching_from_pixels_3/test-* - config_name: matching_from_pixels_4 data_files: - split: train path: matching_from_pixels_4/train-* - split: validation path: matching_from_pixels_4/validation-* - split: test path: matching_from_pixels_4/test-* - config_name: ranking data_files: - split: train path: ranking/train-* - split: validation path: ranking/validation-* - split: test path: ranking/test-* - config_name: ranking_1 data_files: - split: train path: ranking_1/train-* - split: validation path: ranking_1/validation-* - split: test path: ranking_1/test-* - config_name: ranking_2 data_files: - split: train path: ranking_2/train-* - split: validation path: ranking_2/validation-* - split: test path: ranking_2/test-* - config_name: ranking_3 data_files: - split: train path: ranking_3/train-* - split: validation path: ranking_3/validation-* - split: test path: ranking_3/test-* - config_name: ranking_4 data_files: - split: train path: ranking_4/train-* - split: validation path: ranking_4/validation-* - split: test path: ranking_4/test-* - config_name: ranking_from_pixels data_files: - split: train path: ranking_from_pixels/train-* - split: validation path: ranking_from_pixels/validation-* - split: test path: ranking_from_pixels/test-* - config_name: ranking_from_pixels_1 data_files: - split: train path: ranking_from_pixels_1/train-* - split: validation path: ranking_from_pixels_1/validation-* - split: test path: ranking_from_pixels_1/test-* - config_name: ranking_from_pixels_2 data_files: - split: train path: ranking_from_pixels_2/train-* - split: validation path: ranking_from_pixels_2/validation-* - split: test path: ranking_from_pixels_2/test-* - config_name: ranking_from_pixels_3 data_files: - split: train path: ranking_from_pixels_3/train-* - split: validation path: ranking_from_pixels_3/validation-* - split: test path: ranking_from_pixels_3/test-* - config_name: ranking_from_pixels_4 data_files: - split: train path: ranking_from_pixels_4/train-* - split: validation path: ranking_from_pixels_4/validation-* - split: test path: ranking_from_pixels_4/test-* --- # Dataset Card for New Yorker Caption Contest Benchmarks ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [capcon.dev](https://www.capcon.dev) - **Repository:** [https://github.com/jmhessel/caption_contest_corpus](https://github.com/jmhessel/caption_contest_corpus) - **Paper:** [Do Androids Laugh at Electric Sheep? Humor "Understanding" Benchmarks from The New Yorker Caption Contest](https://arxiv.org/abs/2209.06293) - **Leaderboard:** https://leaderboard.allenai.org/nycc-matching/ and https://leaderboard.allenai.org/nycc-ranking - **Point of Contact:** jmhessel@gmail.com ### Dataset Summary See [capcon.dev](https://www.capcon.dev) for more! Data from: [Do Androids Laugh at Electric Sheep? Humor "Understanding" Benchmarks from The New Yorker Caption Contest](https://arxiv.org/abs/2209.06293) ``` @inproceedings{hessel2023androids, title={Do Androids Laugh at Electric Sheep? {Humor} ``Understanding'' Benchmarks from {The New Yorker Caption Contest}}, author={Hessel, Jack and Marasovi{\'c}, Ana and Hwang, Jena D. and Lee, Lillian and Da, Jeff and Zellers, Rowan and Mankoff, Robert and Choi, Yejin}, booktitle={Proceedings of the ACL}, year={2023} } ``` If you use this dataset, we would appreciate you citing our work, but also -- several other papers that we build this corpus upon. See [Citation Information](#citation-information). We challenge AI models to "demonstrate understanding" of the sophisticated multimodal humor of The New Yorker Caption Contest. Concretely, we develop three carefully circumscribed tasks for which it suffices (but is not necessary) to grasp potentially complex and unexpected relationships between image and caption, and similarly complex and unexpected allusions to the wide varieties of human experience. ### Supported Tasks and Leaderboards Three tasks are supported: - "Matching:" a model must recognize a caption written about a cartoon (vs. options that were not); - "Quality ranking:" a model must evaluate the quality of a caption by scoring it more highly than a lower quality option from the same contest; - "Explanation:" a model must explain why a given joke is funny. There are no official leaderboards (yet). ### Languages English ## Dataset Structure Here's an example instance from Matching: ``` {'caption_choices': ['Tell me about your childhood very quickly.', "Believe me . . . it's what's UNDER the ground that's " 'most interesting.', "Stop me if you've heard this one.", 'I have trouble saying no.', 'Yes, I see the train but I think we can beat it.'], 'contest_number': 49, 'entities': ['https://en.wikipedia.org/wiki/Rule_of_three_(writing)', 'https://en.wikipedia.org/wiki/Bar_joke', 'https://en.wikipedia.org/wiki/Religious_institute'], 'from_description': 'scene: a bar description: Two priests and a rabbi are ' 'walking into a bar, as the bartender and another patron ' 'look on. The bartender talks on the phone while looking ' 'skeptically at the incoming crew. uncanny: The scene ' 'depicts a very stereotypical "bar joke" that would be ' 'unlikely to be encountered in real life; the skepticism ' 'of the bartender suggests that he is aware he is seeing ' 'this trope, and is explaining it to someone on the ' 'phone. entities: Rule_of_three_(writing), Bar_joke, ' 'Religious_institute. choices A: Tell me about your ' "childhood very quickly. B: Believe me . . . it's what's " "UNDER the ground that's most interesting. C: Stop me if " "you've heard this one. D: I have trouble saying no. E: " 'Yes, I see the train but I think we can beat it.', 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=L size=323x231 at 0x7F34F283E9D0>, 'image_description': 'Two priests and a rabbi are walking into a bar, as the ' 'bartender and another patron look on. The bartender ' 'talks on the phone while looking skeptically at the ' 'incoming crew.', 'image_location': 'a bar', 'image_uncanny_description': 'The scene depicts a very stereotypical "bar ' 'joke" that would be unlikely to be encountered ' 'in real life; the skepticism of the bartender ' 'suggests that he is aware he is seeing this ' 'trope, and is explaining it to someone on the ' 'phone.', 'instance_id': '21125bb8787b4e7e82aa3b0a1cba1571', 'label': 'C', 'n_tokens_label': 1, 'questions': ['What is the bartender saying on the phone in response to the ' 'living, breathing, stereotypical bar joke that is unfolding?']} ``` The label "C" indicates that the 3rd choice in the `caption_choices` is correct. Here's an example instance from Ranking (in the from pixels setting --- though, this is also available in the from description setting) ``` {'caption_choices': ['I guess I misunderstood when you said long bike ride.', 'Does your divorce lawyer have any other cool ideas?'], 'contest_number': 582, 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=L size=600x414 at 0x7F8FF9F96610>, 'instance_id': 'dd1c214a1ca3404aa4e582c9ce50795a', 'label': 'A', 'n_tokens_label': 1, 'winner_source': 'official_winner'} ``` the label indicates that the first caption choice ("A", here) in the `caption_choices` list was more highly rated. Here's an example instance from Explanation: ``` {'caption_choices': 'The classics can be so intimidating.', 'contest_number': 752, 'entities': ['https://en.wikipedia.org/wiki/Literature', 'https://en.wikipedia.org/wiki/Solicitor'], 'from_description': 'scene: a road description: Two people are walking down a ' 'path. A number of giant books have surrounded them. ' 'uncanny: There are book people in this world. entities: ' 'Literature, Solicitor. caption: The classics can be so ' 'intimidating.', 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=L size=800x706 at 0x7F90003D0BB0>, 'image_description': 'Two people are walking down a path. A number of giant ' 'books have surrounded them.', 'image_location': 'a road', 'image_uncanny_description': 'There are book people in this world.', 'instance_id': 'eef9baf450e2fab19b96facc128adf80', 'label': 'A play on the word intimidating --- usually if the classics (i.e., ' 'classic novels) were to be intimidating, this would mean that they ' 'are intimidating to read due to their length, complexity, etc. But ' 'here, they are surrounded by anthropomorphic books which look ' 'physically intimidating, i.e., they are intimidating because they ' 'may try to beat up these people.', 'n_tokens_label': 59, 'questions': ['What do the books want?']} ``` The label is an explanation of the joke, which serves as the autoregressive target. ### Data Instances See above ### Data Fields See above ### Data Splits Data splits can be accessed as: ``` from datasets import load_dataset dset = load_dataset("jmhessel/newyorker_caption_contest", "matching") dset = load_dataset("jmhessel/newyorker_caption_contest", "ranking") dset = load_dataset("jmhessel/newyorker_caption_contest", "explanation") ``` Or, in the from pixels setting, e.g., ``` from datasets import load_dataset dset = load_dataset("jmhessel/newyorker_caption_contest", "ranking_from_pixels") ``` Because the dataset is small, we reported in 5-fold cross-validation setting initially. The default splits are split 0. You can access the other splits, e.g.: ``` from datasets import load_dataset # the 4th data split dset = load_dataset("jmhessel/newyorker_caption_contest", "explanation_4") ``` ## Dataset Creation Full details are in the paper. ### Curation Rationale See the paper for rationale/motivation. ### Source Data See citation below. We combined 3 sources of data, and added significant annotations of our own. #### Initial Data Collection and Normalization Full details are in the paper. #### Who are the source language producers? We paid crowdworkers $15/hr to annotate the corpus. In addition, significant annotation efforts were conducted by the authors of this work. ### Annotations Full details are in the paper. #### Annotation process Full details are in the paper. #### Who are the annotators? A mix of crowdworks and authors of this paper. ### Personal and Sensitive Information Has been redacted from the dataset. Images are published in the New Yorker already. ## Considerations for Using the Data ### Social Impact of Dataset It's plausible that humor could perpetuate negative stereotypes. The jokes in this corpus are a mix of crowdsourced entries that are highly rated, and ones published in the new yorker. ### Discussion of Biases Humor is subjective, and some of the jokes may be considered offensive. The images may contain adult themes and minor cartoon nudity. ### Other Known Limitations More details are in the paper ## Additional Information ### Dataset Curators The dataset was curated by researchers at AI2 ### Licensing Information The annotations we provide are CC-BY-4.0. See www.capcon.dev for more info. ### Citation Information ``` @article{hessel2022androids, title={Do Androids Laugh at Electric Sheep? Humor "Understanding" Benchmarks from The New Yorker Caption Contest}, author={Hessel, Jack and Marasovi{\'c}, Ana and Hwang, Jena D and Lee, Lillian and Da, Jeff and Zellers, Rowan and Mankoff, Robert and Choi, Yejin}, journal={arXiv preprint arXiv:2209.06293}, year={2022} } ``` Our data contributions are: - The cartoon-level annotations; - The joke explanations; - and the framing of the tasks We release these data we contribute under CC-BY (see DATASET_LICENSE). If you find this data useful in your work, in addition to citing our contributions, please also cite the following, from which the cartoons/captions in our corpus are derived: ``` @misc{newyorkernextmldataset, author={Jain, Lalit and Jamieson, Kevin and Mankoff, Robert and Nowak, Robert and Sievert, Scott}, title={The {N}ew {Y}orker Cartoon Caption Contest Dataset}, year={2020}, url={https://nextml.github.io/caption-contest-data/} } @inproceedings{radev-etal-2016-humor, title = "Humor in Collective Discourse: Unsupervised Funniness Detection in The {New Yorker} Cartoon Caption Contest", author = "Radev, Dragomir and Stent, Amanda and Tetreault, Joel and Pappu, Aasish and Iliakopoulou, Aikaterini and Chanfreau, Agustin and de Juan, Paloma and Vallmitjana, Jordi and Jaimes, Alejandro and Jha, Rahul and Mankoff, Robert", booktitle = "LREC", year = "2016", } @inproceedings{shahaf2015inside, title={Inside jokes: Identifying humorous cartoon captions}, author={Shahaf, Dafna and Horvitz, Eric and Mankoff, Robert}, booktitle={KDD}, year={2015}, } ```

turkic_xwmt

--- annotations_creators: - crowdsourced language_creators: - found language: - az - ba - en - kaa - kk - ky - ru - sah - tr - uz license: - mit multilinguality: - translation pretty_name: turkic_xwmt size_categories: - n<1K task_categories: - translation task_ids: [] source_datasets: - extended|WMT 2020 News Translation Task dataset_info: - config_name: az-ba features: - name: translation dtype: translation: languages: - az - ba splits: - name: test num_bytes: 266801 num_examples: 600 download_size: 12862396 dataset_size: 266801 - config_name: az-en features: - name: translation dtype: translation: languages: - az - en splits: - name: test num_bytes: 181156 num_examples: 600 download_size: 12862396 dataset_size: 181156 - config_name: az-kaa features: - name: translation dtype: translation: languages: - az - kaa splits: - name: test num_bytes: 134071 num_examples: 300 download_size: 12862396 dataset_size: 134071 - config_name: az-kk features: - name: translation dtype: translation: languages: - az - kk splits: - name: test num_bytes: 203798 num_examples: 500 download_size: 12862396 dataset_size: 203798 - config_name: az-ky features: - name: translation dtype: translation: languages: - az - ky splits: - name: test num_bytes: 210549 num_examples: 500 download_size: 12862396 dataset_size: 210549 - config_name: az-ru features: - name: translation dtype: translation: languages: - az - ru splits: - name: test num_bytes: 262739 num_examples: 600 download_size: 12862396 dataset_size: 262739 - config_name: az-sah features: - name: translation dtype: translation: languages: - az - sah splits: - name: test num_bytes: 144198 num_examples: 300 download_size: 12862396 dataset_size: 144198 - config_name: az-tr features: - name: translation dtype: translation: languages: - az - tr splits: - name: test num_bytes: 162447 num_examples: 500 download_size: 12862396 dataset_size: 162447 - config_name: az-uz features: - name: translation dtype: translation: languages: - az - uz splits: - name: test num_bytes: 194231 num_examples: 600 download_size: 12862396 dataset_size: 194231 - config_name: ba-az features: - name: translation dtype: translation: languages: - ba - az splits: - name: test num_bytes: 266801 num_examples: 600 download_size: 12862396 dataset_size: 266801 - config_name: ba-en features: - name: translation dtype: translation: languages: - ba - en splits: - name: test num_bytes: 431223 num_examples: 1000 download_size: 12862396 dataset_size: 431223 - config_name: ba-kaa features: - name: translation dtype: translation: languages: - ba - kaa splits: - name: test num_bytes: 168895 num_examples: 300 download_size: 12862396 dataset_size: 168895 - config_name: ba-kk features: - name: translation dtype: translation: languages: - ba - kk splits: - name: test num_bytes: 374756 num_examples: 700 download_size: 12862396 dataset_size: 374756 - config_name: ba-ky features: - name: translation dtype: translation: languages: - ba - ky splits: - name: test num_bytes: 268986 num_examples: 500 download_size: 12862396 dataset_size: 268986 - config_name: ba-ru features: - name: translation dtype: translation: languages: - ba - ru splits: - name: test num_bytes: 568101 num_examples: 1000 download_size: 12862396 dataset_size: 568101 - config_name: ba-sah features: - name: translation dtype: translation: languages: - ba - sah splits: - name: test num_bytes: 179022 num_examples: 300 download_size: 12862396 dataset_size: 179022 - config_name: ba-tr features: - name: translation dtype: translation: languages: - ba - tr splits: - name: test num_bytes: 309455 num_examples: 700 download_size: 12862396 dataset_size: 309455 - config_name: ba-uz features: - name: translation dtype: translation: languages: - ba - uz splits: - name: test num_bytes: 410874 num_examples: 900 download_size: 12862396 dataset_size: 410874 - config_name: en-az features: - name: translation dtype: translation: languages: - en - az splits: - name: test num_bytes: 181156 num_examples: 600 download_size: 12862396 dataset_size: 181156 - config_name: en-ba features: - name: translation dtype: translation: languages: - en - ba splits: - name: test num_bytes: 431223 num_examples: 1000 download_size: 12862396 dataset_size: 431223 - config_name: en-kaa features: - name: translation dtype: translation: languages: - en - kaa splits: - name: test num_bytes: 126304 num_examples: 300 download_size: 12862396 dataset_size: 126304 - config_name: en-kk features: - name: translation dtype: translation: languages: - en - kk splits: - name: test num_bytes: 274728 num_examples: 700 download_size: 12862396 dataset_size: 274728 - config_name: en-ky features: - name: translation dtype: translation: languages: - en - ky splits: - name: test num_bytes: 198854 num_examples: 500 download_size: 12862396 dataset_size: 198854 - config_name: en-ru features: - name: translation dtype: translation: languages: - en - ru splits: - name: test num_bytes: 422718 num_examples: 1000 download_size: 12862396 dataset_size: 422718 - config_name: en-sah features: - name: translation dtype: translation: languages: - en - sah splits: - name: test num_bytes: 136431 num_examples: 300 download_size: 12862396 dataset_size: 136431 - config_name: en-tr features: - name: translation dtype: translation: languages: - en - tr splits: - name: test num_bytes: 210144 num_examples: 700 download_size: 12862396 dataset_size: 210144 - config_name: en-uz features: - name: translation dtype: translation: languages: - en - uz splits: - name: test num_bytes: 278971 num_examples: 900 download_size: 12862396 dataset_size: 278971 - config_name: kaa-az features: - name: translation dtype: translation: languages: - kaa - az splits: - name: test num_bytes: 134071 num_examples: 300 download_size: 12862396 dataset_size: 134071 - config_name: kaa-ba features: - name: translation dtype: translation: languages: - kaa - ba splits: - name: test num_bytes: 168895 num_examples: 300 download_size: 12862396 dataset_size: 168895 - config_name: kaa-en features: - name: translation dtype: translation: languages: - kaa - en splits: - name: test num_bytes: 126304 num_examples: 300 download_size: 12862396 dataset_size: 126304 - config_name: kaa-kk features: - name: translation dtype: translation: languages: - kaa - kk splits: - name: test num_bytes: 160022 num_examples: 300 download_size: 12862396 dataset_size: 160022 - config_name: kaa-ky features: - name: translation dtype: translation: languages: - kaa - ky splits: - name: test num_bytes: 163763 num_examples: 300 download_size: 12862396 dataset_size: 163763 - config_name: kaa-ru features: - name: translation dtype: translation: languages: - kaa - ru splits: - name: test num_bytes: 168349 num_examples: 300 download_size: 12862396 dataset_size: 168349 - config_name: kaa-sah features: - name: translation dtype: translation: languages: - kaa - sah splits: - name: test num_bytes: 177151 num_examples: 300 download_size: 12862396 dataset_size: 177151 - config_name: kaa-tr features: - name: translation dtype: translation: languages: - kaa - tr splits: - name: test num_bytes: 132055 num_examples: 300 download_size: 12862396 dataset_size: 132055 - config_name: kaa-uz features: - name: translation dtype: translation: languages: - kaa - uz splits: - name: test num_bytes: 132789 num_examples: 300 download_size: 12862396 dataset_size: 132789 - config_name: kk-az features: - name: translation dtype: translation: languages: - kk - az splits: - name: test num_bytes: 203798 num_examples: 500 download_size: 12862396 dataset_size: 203798 - config_name: kk-ba features: - name: translation dtype: translation: languages: - kk - ba splits: - name: test num_bytes: 374756 num_examples: 700 download_size: 12862396 dataset_size: 374756 - config_name: kk-en features: - name: translation dtype: translation: languages: - kk - en splits: - name: test num_bytes: 274728 num_examples: 700 download_size: 12862396 dataset_size: 274728 - config_name: kk-kaa features: - name: translation dtype: translation: languages: - kk - kaa splits: - name: test num_bytes: 160022 num_examples: 300 download_size: 12862396 dataset_size: 160022 - config_name: kk-ky features: - name: translation dtype: translation: languages: - kk - ky splits: - name: test num_bytes: 253421 num_examples: 500 download_size: 12862396 dataset_size: 253421 - config_name: kk-ru features: - name: translation dtype: translation: languages: - kk - ru splits: - name: test num_bytes: 369633 num_examples: 700 download_size: 12862396 dataset_size: 369633 - config_name: kk-sah features: - name: translation dtype: translation: languages: - kk - sah splits: - name: test num_bytes: 170149 num_examples: 300 download_size: 12862396 dataset_size: 170149 - config_name: kk-tr features: - name: translation dtype: translation: languages: - kk - tr splits: - name: test num_bytes: 204442 num_examples: 500 download_size: 12862396 dataset_size: 204442 - config_name: kk-uz features: - name: translation dtype: translation: languages: - kk - uz splits: - name: test num_bytes: 290325 num_examples: 700 download_size: 12862396 dataset_size: 290325 - config_name: ky-az features: - name: translation dtype: translation: languages: - ky - az splits: - name: test num_bytes: 210549 num_examples: 500 download_size: 12862396 dataset_size: 210549 - config_name: ky-ba features: - name: translation dtype: translation: languages: - ky - ba splits: - name: test num_bytes: 268986 num_examples: 500 download_size: 12862396 dataset_size: 268986 - config_name: ky-en features: - name: translation dtype: translation: languages: - ky - en splits: - name: test num_bytes: 198854 num_examples: 500 download_size: 12862396 dataset_size: 198854 - config_name: ky-kaa features: - name: translation dtype: translation: languages: - ky - kaa splits: - name: test num_bytes: 163763 num_examples: 300 download_size: 12862396 dataset_size: 163763 - config_name: ky-kk features: - name: translation dtype: translation: languages: - ky - kk splits: - name: test num_bytes: 253421 num_examples: 500 download_size: 12862396 dataset_size: 253421 - config_name: ky-ru features: - name: translation dtype: translation: languages: - ky - ru splits: - name: test num_bytes: 265803 num_examples: 500 download_size: 12862396 dataset_size: 265803 - config_name: ky-sah features: - name: translation dtype: translation: languages: - ky - sah splits: - name: test num_bytes: 173890 num_examples: 300 download_size: 12862396 dataset_size: 173890 - config_name: ky-tr features: - name: translation dtype: translation: languages: - ky - tr splits: - name: test num_bytes: 168026 num_examples: 400 download_size: 12862396 dataset_size: 168026 - config_name: ky-uz features: - name: translation dtype: translation: languages: - ky - uz splits: - name: test num_bytes: 209619 num_examples: 500 download_size: 12862396 dataset_size: 209619 - config_name: ru-az features: - name: translation dtype: translation: languages: - ru - az splits: - name: test num_bytes: 262739 num_examples: 600 download_size: 12862396 dataset_size: 262739 - config_name: ru-ba features: - name: translation dtype: translation: languages: - ru - ba splits: - name: test num_bytes: 568101 num_examples: 1000 download_size: 12862396 dataset_size: 568101 - config_name: ru-en features: - name: translation dtype: translation: languages: - ru - en splits: - name: test num_bytes: 422718 num_examples: 1000 download_size: 12862396 dataset_size: 422718 - config_name: ru-kaa features: - name: translation dtype: translation: languages: - ru - kaa splits: - name: test num_bytes: 168349 num_examples: 300 download_size: 12862396 dataset_size: 168349 - config_name: ru-kk features: - name: translation dtype: translation: languages: - ru - kk splits: - name: test num_bytes: 369633 num_examples: 700 download_size: 12862396 dataset_size: 369633 - config_name: ru-ky features: - name: translation dtype: translation: languages: - ru - ky splits: - name: test num_bytes: 265803 num_examples: 500 download_size: 12862396 dataset_size: 265803 - config_name: ru-sah features: - name: translation dtype: translation: languages: - ru - sah splits: - name: test num_bytes: 178476 num_examples: 300 download_size: 12862396 dataset_size: 178476 - config_name: ru-tr features: - name: translation dtype: translation: languages: - ru - tr splits: - name: test num_bytes: 304586 num_examples: 700 download_size: 12862396 dataset_size: 304586 - config_name: ru-uz features: - name: translation dtype: translation: languages: - ru - uz splits: - name: test num_bytes: 403551 num_examples: 900 download_size: 12862396 dataset_size: 403551 - config_name: sah-az features: - name: translation dtype: translation: languages: - sah - az splits: - name: test num_bytes: 144198 num_examples: 300 download_size: 12862396 dataset_size: 144198 - config_name: sah-ba features: - name: translation dtype: translation: languages: - sah - ba splits: - name: test num_bytes: 179022 num_examples: 300 download_size: 12862396 dataset_size: 179022 - config_name: sah-en features: - name: translation dtype: translation: languages: - sah - en splits: - name: test num_bytes: 136431 num_examples: 300 download_size: 12862396 dataset_size: 136431 - config_name: sah-kaa features: - name: translation dtype: translation: languages: - sah - kaa splits: - name: test num_bytes: 177151 num_examples: 300 download_size: 12862396 dataset_size: 177151 - config_name: sah-kk features: - name: translation dtype: translation: languages: - sah - kk splits: - name: test num_bytes: 170149 num_examples: 300 download_size: 12862396 dataset_size: 170149 - config_name: sah-ky features: - name: translation dtype: translation: languages: - sah - ky splits: - name: test num_bytes: 173890 num_examples: 300 download_size: 12862396 dataset_size: 173890 - config_name: sah-ru features: - name: translation dtype: translation: languages: - sah - ru splits: - name: test num_bytes: 178476 num_examples: 300 download_size: 12862396 dataset_size: 178476 - config_name: sah-tr features: - name: translation dtype: translation: languages: - sah - tr splits: - name: test num_bytes: 142182 num_examples: 300 download_size: 12862396 dataset_size: 142182 - config_name: sah-uz features: - name: translation dtype: translation: languages: - sah - uz splits: - name: test num_bytes: 142916 num_examples: 300 download_size: 12862396 dataset_size: 142916 - config_name: tr-az features: - name: translation dtype: translation: languages: - tr - az splits: - name: test num_bytes: 162447 num_examples: 500 download_size: 12862396 dataset_size: 162447 - config_name: tr-ba features: - name: translation dtype: translation: languages: - tr - ba splits: - name: test num_bytes: 309455 num_examples: 700 download_size: 12862396 dataset_size: 309455 - config_name: tr-en features: - name: translation dtype: translation: languages: - tr - en splits: - name: test num_bytes: 210144 num_examples: 700 download_size: 12862396 dataset_size: 210144 - config_name: tr-kaa features: - name: translation dtype: translation: languages: - tr - kaa splits: - name: test num_bytes: 132055 num_examples: 300 download_size: 12862396 dataset_size: 132055 - config_name: tr-kk features: - name: translation dtype: translation: languages: - tr - kk splits: - name: test num_bytes: 204442 num_examples: 500 download_size: 12862396 dataset_size: 204442 - config_name: tr-ky features: - name: translation dtype: translation: languages: - tr - ky splits: - name: test num_bytes: 168026 num_examples: 400 download_size: 12862396 dataset_size: 168026 - config_name: tr-ru features: - name: translation dtype: translation: languages: - tr - ru splits: - name: test num_bytes: 304586 num_examples: 700 download_size: 12862396 dataset_size: 304586 - config_name: tr-sah features: - name: translation dtype: translation: languages: - tr - sah splits: - name: test num_bytes: 142182 num_examples: 300 download_size: 12862396 dataset_size: 142182 - config_name: tr-uz features: - name: translation dtype: translation: languages: - tr - uz splits: - name: test num_bytes: 194761 num_examples: 600 download_size: 12862396 dataset_size: 194761 - config_name: uz-az features: - name: translation dtype: translation: languages: - uz - az splits: - name: test num_bytes: 194231 num_examples: 600 download_size: 12862396 dataset_size: 194231 - config_name: uz-ba features: - name: translation dtype: translation: languages: - uz - ba splits: - name: test num_bytes: 410874 num_examples: 900 download_size: 12862396 dataset_size: 410874 - config_name: uz-en features: - name: translation dtype: translation: languages: - uz - en splits: - name: test num_bytes: 278971 num_examples: 900 download_size: 12862396 dataset_size: 278971 - config_name: uz-kaa features: - name: translation dtype: translation: languages: - uz - kaa splits: - name: test num_bytes: 132789 num_examples: 300 download_size: 12862396 dataset_size: 132789 - config_name: uz-kk features: - name: translation dtype: translation: languages: - uz - kk splits: - name: test num_bytes: 290325 num_examples: 700 download_size: 12862396 dataset_size: 290325 - config_name: uz-ky features: - name: translation dtype: translation: languages: - uz - ky splits: - name: test num_bytes: 209619 num_examples: 500 download_size: 12862396 dataset_size: 209619 - config_name: uz-ru features: - name: translation dtype: translation: languages: - uz - ru splits: - name: test num_bytes: 403551 num_examples: 900 download_size: 12862396 dataset_size: 403551 - config_name: uz-sah features: - name: translation dtype: translation: languages: - uz - sah splits: - name: test num_bytes: 142916 num_examples: 300 download_size: 12862396 dataset_size: 142916 - config_name: uz-tr features: - name: translation dtype: translation: languages: - uz - tr splits: - name: test num_bytes: 194761 num_examples: 600 download_size: 12862396 dataset_size: 194761 config_names: - az-ba - az-en - az-kaa - az-kk - az-ky - az-ru - az-sah - az-tr - az-uz - ba-az - ba-en - ba-kaa - ba-kk - ba-ky - ba-ru - ba-sah - ba-tr - ba-uz - en-az - en-ba - en-kaa - en-kk - en-ky - en-ru - en-sah - en-tr - en-uz - kaa-az - kaa-ba - kaa-en - kaa-kk - kaa-ky - kaa-ru - kaa-sah - kaa-tr - kaa-uz - kk-az - kk-ba - kk-en - kk-kaa - kk-ky - kk-ru - kk-sah - kk-tr - kk-uz - ky-az - ky-ba - ky-en - ky-kaa - ky-kk - ky-ru - ky-sah - ky-tr - ky-uz - ru-az - ru-ba - ru-en - ru-kaa - ru-kk - ru-ky - ru-sah - ru-tr - ru-uz - sah-az - sah-ba - sah-en - sah-kaa - sah-kk - sah-ky - sah-ru - sah-tr - sah-uz - tr-az - tr-ba - tr-en - tr-kaa - tr-kk - tr-ky - tr-ru - tr-sah - tr-uz - uz-az - uz-ba - uz-en - uz-kaa - uz-kk - uz-ky - uz-ru - uz-sah - uz-tr --- # Dataset Card for turkic_xwmt ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:**[Github](https://github.com/turkic-interlingua/til-mt/tree/master/xwmt) - **Paper:** [https://arxiv.org/abs/2109.04593](https://arxiv.org/abs/2109.04593) - **Leaderboard:** [More Information Needed] - **Point of Contact:** [turkicinterlingua@gmail.com](mailto:turkicinterlingua@gmail.com) ### Dataset Summary To establish a comprehensive and challenging evaluation benchmark for Machine Translation in Turkic languages, we translate a test set originally introduced in WMT 2020 News Translation Task for English-Russian. The original dataset is profesionally translated and consists of sentences from news articles that are both English and Russian-centric. We adopt this evaluation set (X-WMT) and begin efforts to translate it into several Turkic languages. The current version of X-WMT includes covers 8 Turkic languages and 88 language directions with a minimum of 300 sentences per language direction. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Currently covered languages are (besides English and Russian): - Azerbaijani (az) - Bashkir (ba) - Karakalpak (kaa) - Kazakh (kk) - Kirghiz (ky) - Turkish (tr) - Sakha (sah) - Uzbek (uz) ## Dataset Structure ### Data Instances A random example from the Russian-Uzbek set: ``` {"translation": {'ru': 'Моника Мутсвангва , министр информации Зимбабве , утверждает , что полиция вмешалась в отъезд Магомбейи из соображений безопасности и вследствие состояния его здоровья .', 'uz': 'Zimbabvening Axborot vaziri , Monika Mutsvanva Magombeyining xavfsizligi va sog'ligi tufayli bo'lgan jo'nab ketishinida politsiya aralashuvini ushlab turadi .'}} ``` ### Data Fields Each example has one field "translation" that contains two subfields: one per language, e.g. for the Russian-Uzbek set: - **translation**: a dictionary with two subfields: - **ru**: the russian text - **uz**: the uzbek text ### Data Splits <details> <summary>Click here to show the number of examples per configuration:</summary> | | test | |:--------|-------:| | az-ba | 600 | | az-en | 600 | | az-kaa | 300 | | az-kk | 500 | | az-ky | 500 | | az-ru | 600 | | az-sah | 300 | | az-tr | 500 | | az-uz | 600 | | ba-az | 600 | | ba-en | 1000 | | ba-kaa | 300 | | ba-kk | 700 | | ba-ky | 500 | | ba-ru | 1000 | | ba-sah | 300 | | ba-tr | 700 | | ba-uz | 900 | | en-az | 600 | | en-ba | 1000 | | en-kaa | 300 | | en-kk | 700 | | en-ky | 500 | | en-ru | 1000 | | en-sah | 300 | | en-tr | 700 | | en-uz | 900 | | kaa-az | 300 | | kaa-ba | 300 | | kaa-en | 300 | | kaa-kk | 300 | | kaa-ky | 300 | | kaa-ru | 300 | | kaa-sah | 300 | | kaa-tr | 300 | | kaa-uz | 300 | | kk-az | 500 | | kk-ba | 700 | | kk-en | 700 | | kk-kaa | 300 | | kk-ky | 500 | | kk-ru | 700 | | kk-sah | 300 | | kk-tr | 500 | | kk-uz | 700 | | ky-az | 500 | | ky-ba | 500 | | ky-en | 500 | | ky-kaa | 300 | | ky-kk | 500 | | ky-ru | 500 | | ky-sah | 300 | | ky-tr | 400 | | ky-uz | 500 | | ru-az | 600 | | ru-ba | 1000 | | ru-en | 1000 | | ru-kaa | 300 | | ru-kk | 700 | | ru-ky | 500 | | ru-sah | 300 | | ru-tr | 700 | | ru-uz | 900 | | sah-az | 300 | | sah-ba | 300 | | sah-en | 300 | | sah-kaa | 300 | | sah-kk | 300 | | sah-ky | 300 | | sah-ru | 300 | | sah-tr | 300 | | sah-uz | 300 | | tr-az | 500 | | tr-ba | 700 | | tr-en | 700 | | tr-kaa | 300 | | tr-kk | 500 | | tr-ky | 400 | | tr-ru | 700 | | tr-sah | 300 | | tr-uz | 600 | | uz-az | 600 | | uz-ba | 900 | | uz-en | 900 | | uz-kaa | 300 | | uz-kk | 700 | | uz-ky | 500 | | uz-ru | 900 | | uz-sah | 300 | | uz-tr | 600 | </details> ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? **Translators, annotators and dataset contributors** (in alphabetical order) Abilxayr Zholdybai Aigiz Kunafin Akylbek Khamitov Alperen Cantez Aydos Muxammadiyarov Doniyorbek Rafikjonov Erkinbek Vokhabov Ipek Baris Iskander Shakirov Madina Zokirjonova Mohiyaxon Uzoqova Mukhammadbektosh Khaydarov Nurlan Maharramli Petr Popov Rasul Karimov Sariya Kagarmanova Ziyodabonu Qobiljon qizi ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [MIT License](https://github.com/turkic-interlingua/til-mt/blob/master/xwmt/LICENSE) ### Citation Information ``` @inproceedings{mirzakhalov2021large, title={A Large-Scale Study of Machine Translation in Turkic Languages}, author={Mirzakhalov, Jamshidbek and Babu, Anoop and Ataman, Duygu and Kariev, Sherzod and Tyers, Francis and Abduraufov, Otabek and Hajili, Mammad and Ivanova, Sardana and Khaytbaev, Abror and Laverghetta Jr, Antonio and others}, booktitle={Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing}, pages={5876--5890}, year={2021} } ``` ### Contributions This project was carried out with the help and contributions from dozens of individuals and organizations. We acknowledge and greatly appreciate each and every one of them: **Authors on the publications** (in alphabetical order) Abror Khaytbaev Ahsan Wahab Aigiz Kunafin Anoop Babu Antonio Laverghetta Jr. Behzodbek Moydinboyev Dr. Duygu Ataman Esra Onal Dr. Francis Tyers Jamshidbek Mirzakhalov Dr. John Licato Dr. Julia Kreutzer Mammad Hajili Mokhiyakhon Uzokova Dr. Orhan Firat Otabek Abduraufov Sardana Ivanova Shaxnoza Pulatova Sherzod Kariev Dr. Sriram Chellappan **Translators, annotators and dataset contributors** (in alphabetical order) Abilxayr Zholdybai Aigiz Kunafin Akylbek Khamitov Alperen Cantez Aydos Muxammadiyarov Doniyorbek Rafikjonov Erkinbek Vokhabov Ipek Baris Iskander Shakirov Madina Zokirjonova Mohiyaxon Uzoqova Mukhammadbektosh Khaydarov Nurlan Maharramli Petr Popov Rasul Karimov Sariya Kagarmanova Ziyodabonu Qobiljon qizi **Industry supporters** [Google Cloud](https://cloud.google.com/solutions/education) [Khan Academy Oʻzbek](https://uz.khanacademy.org/) [The Foundation for the Preservation and Development of the Bashkir Language](https://bsfond.ru/) Thanks to [@mirzakhalov](https://github.com/mirzakhalov) for adding this dataset.

mteb/twentynewsgroups-clustering

mteb

--- language: - en ---

hotpot_qa

--- annotations_creators: - crowdsourced language: - en language_creators: - found license: - cc-by-sa-4.0 multilinguality: - monolingual pretty_name: HotpotQA size_categories: - 100K<n<1M source_datasets: - original task_categories: - question-answering task_ids: [] paperswithcode_id: hotpotqa tags: - multi-hop dataset_info: - config_name: distractor features: - name: id dtype: string - name: question dtype: string - name: answer dtype: string - name: type dtype: string - name: level dtype: string - name: supporting_facts sequence: - name: title dtype: string - name: sent_id dtype: int32 - name: context sequence: - name: title dtype: string - name: sentences sequence: string splits: - name: train num_bytes: 552949315 num_examples: 90447 - name: validation num_bytes: 45716111 num_examples: 7405 download_size: 612746344 dataset_size: 598665426 - config_name: fullwiki features: - name: id dtype: string - name: question dtype: string - name: answer dtype: string - name: type dtype: string - name: level dtype: string - name: supporting_facts sequence: - name: title dtype: string - name: sent_id dtype: int32 - name: context sequence: - name: title dtype: string - name: sentences sequence: string splits: - name: train num_bytes: 552949315 num_examples: 90447 - name: validation num_bytes: 46848601 num_examples: 7405 - name: test num_bytes: 46000102 num_examples: 7405 download_size: 660094672 dataset_size: 645798018 --- # Dataset Card for "hotpot_qa" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://hotpotqa.github.io/](https://hotpotqa.github.io/) - **Repository:** https://github.com/hotpotqa/hotpot - **Paper:** [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://arxiv.org/abs/1809.09600) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 1.27 GB - **Size of the generated dataset:** 1.24 GB - **Total amount of disk used:** 2.52 GB ### Dataset Summary HotpotQA is a new dataset with 113k Wikipedia-based question-answer pairs with four key features: (1) the questions require finding and reasoning over multiple supporting documents to answer; (2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas; (3) we provide sentence-level supporting facts required for reasoning, allowingQA systems to reason with strong supervision and explain the predictions; (4) we offer a new type of factoid comparison questions to test QA systems’ ability to extract relevant facts and perform necessary comparison. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### distractor - **Size of downloaded dataset files:** 612.75 MB - **Size of the generated dataset:** 598.66 MB - **Total amount of disk used:** 1.21 GB An example of 'validation' looks as follows. ``` { "answer": "This is the answer", "context": { "sentences": [["Sent 1"], ["Sent 21", "Sent 22"]], "title": ["Title1", "Title 2"] }, "id": "000001", "level": "medium", "question": "What is the answer?", "supporting_facts": { "sent_id": [0, 1, 3], "title": ["Title of para 1", "Title of para 2", "Title of para 3"] }, "type": "comparison" } ``` #### fullwiki - **Size of downloaded dataset files:** 660.10 MB - **Size of the generated dataset:** 645.80 MB - **Total amount of disk used:** 1.31 GB An example of 'train' looks as follows. ``` { "answer": "This is the answer", "context": { "sentences": [["Sent 1"], ["Sent 2"]], "title": ["Title1", "Title 2"] }, "id": "000001", "level": "hard", "question": "What is the answer?", "supporting_facts": { "sent_id": [0, 1, 3], "title": ["Title of para 1", "Title of para 2", "Title of para 3"] }, "type": "bridge" } ``` ### Data Fields The data fields are the same among all splits. #### distractor - `id`: a `string` feature. - `question`: a `string` feature. - `answer`: a `string` feature. - `type`: a `string` feature. - `level`: a `string` feature. - `supporting_facts`: a dictionary feature containing: - `title`: a `string` feature. - `sent_id`: a `int32` feature. - `context`: a dictionary feature containing: - `title`: a `string` feature. - `sentences`: a `list` of `string` features. #### fullwiki - `id`: a `string` feature. - `question`: a `string` feature. - `answer`: a `string` feature. - `type`: a `string` feature. - `level`: a `string` feature. - `supporting_facts`: a dictionary feature containing: - `title`: a `string` feature. - `sent_id`: a `int32` feature. - `context`: a dictionary feature containing: - `title`: a `string` feature. - `sentences`: a `list` of `string` features. ### Data Splits #### distractor | |train|validation| |----------|----:|---------:| |distractor|90447| 7405| #### fullwiki | |train|validation|test| |--------|----:|---------:|---:| |fullwiki|90447| 7405|7405| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information HotpotQA is distributed under a [CC BY-SA 4.0 License](http://creativecommons.org/licenses/by-sa/4.0/). ### Citation Information ``` @inproceedings{yang2018hotpotqa, title={{HotpotQA}: A Dataset for Diverse, Explainable Multi-hop Question Answering}, author={Yang, Zhilin and Qi, Peng and Zhang, Saizheng and Bengio, Yoshua and Cohen, William W. and Salakhutdinov, Ruslan and Manning, Christopher D.}, booktitle={Conference on Empirical Methods in Natural Language Processing ({EMNLP})}, year={2018} } ``` ### Contributions Thanks to [@albertvillanova](https://github.com/albertvillanova), [@ghomasHudson](https://github.com/ghomasHudson) for adding this dataset.

cc100

--- annotations_creators: - no-annotation language_creators: - found language: - af - am - ar - as - az - be - bg - bn - br - bs - ca - cs - cy - da - de - el - en - eo - es - et - eu - fa - ff - fi - fr - fy - ga - gd - gl - gn - gu - ha - he - hi - hr - ht - hu - hy - id - ig - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lg - li - ln - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - 'no' - ns - om - or - pa - pl - ps - pt - qu - rm - ro - ru - sa - sc - sd - si - sk - sl - so - sq - sr - ss - su - sv - sw - ta - te - th - tl - tn - tr - ug - uk - ur - uz - vi - wo - xh - yi - yo - zh - zu language_bcp47: - bn-Latn - hi-Latn - my-x-zawgyi - ta-Latn - te-Latn - ur-Latn - zh-Hans - zh-Hant license: - unknown multilinguality: - multilingual size_categories: - 10M<n<100M - 1M<n<10M source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: cc100 pretty_name: CC100 dataset_info: - config_name: am features: - name: id dtype: string - name: text dtype: string splits: - name: train num_bytes: 935440775 num_examples: 3124561 download_size: 138821056 dataset_size: 935440775 - config_name: sr features: - name: id dtype: string - name: text dtype: string splits: - name: train num_bytes: 10299427460 num_examples: 35747957 download_size: 1578989320 dataset_size: 10299427460 - config_name: ka features: - name: id dtype: string - name: text dtype: string splits: - name: train num_bytes: 10228918845 num_examples: 31708119 download_size: 1100446372 dataset_size: 10228918845 config_names: - am - sr --- # Dataset Card for CC100 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://data.statmt.org/cc-100/ - **Repository:** None - **Paper:** https://www.aclweb.org/anthology/2020.acl-main.747.pdf, https://www.aclweb.org/anthology/2020.lrec-1.494.pdf - **Leaderboard:** [More Information Needed] - **Point of Contact:** [More Information Needed] ### Dataset Summary This corpus is an attempt to recreate the dataset used for training XLM-R. This corpus comprises of monolingual data for 100+ languages and also includes data for romanized languages (indicated by *_rom). This was constructed using the urls and paragraph indices provided by the CC-Net repository by processing January-December 2018 Commoncrawl snapshots. ### Supported Tasks and Leaderboards CC-100 is mainly inteded to pretrain language models and word represantations. ### Languages To load a language which isn't part of the config, all you need to do is specify the language code in the config. You can find the valid languages in Homepage section of Dataset Description: https://data.statmt.org/cc-100/ E.g. `dataset = load_dataset("cc100", lang="en")` ## Dataset Structure ### Data Instances An example from the `am` configuration: ``` {'id': '0', 'text': 'ተለዋዋጭ የግድግዳ አንግል ሙቅ አንቀሳቅሷል ቲ-አሞሌ አጥቅሼ ...\n'} ``` Each data point is a paragraph of text. The paragraphs are presented in the original (unshuffled) order. Documents are separated by a data point consisting of a single newline character. ### Data Fields The data fields are: - id: id of the example - text: content as a string ### Data Splits Sizes of some configurations: | name |train| |----------|----:| |am|3124561| |sr|35747957| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? The data comes from multiple web pages in a large variety of languages. ### Annotations The dataset does not contain any additional annotations. #### Annotation process [N/A] #### Who are the annotators? [N/A] ### Personal and Sensitive Information Being constructed from Common Crawl, personal and sensitive information might be present. This **must** be considered before training deep learning models with CC-100, specially in the case of text-generation models. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators This dataset was prepared by [Statistical Machine Translation at the University of Edinburgh](https://www.statmt.org/ued/) using the [CC-Net](https://github.com/facebookresearch/cc_net) toolkit by Facebook Research. ### Licensing Information Statistical Machine Translation at the University of Edinburgh makes no claims of intellectual property on the work of preparation of the corpus. By using this, you are also bound by the [Common Crawl terms of use](https://commoncrawl.org/terms-of-use/) in respect of the content contained in the dataset. ### Citation Information ```bibtex @inproceedings{conneau-etal-2020-unsupervised, title = "Unsupervised Cross-lingual Representation Learning at Scale", author = "Conneau, Alexis and Khandelwal, Kartikay and Goyal, Naman and Chaudhary, Vishrav and Wenzek, Guillaume and Guzm{\'a}n, Francisco and Grave, Edouard and Ott, Myle and Zettlemoyer, Luke and Stoyanov, Veselin", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.acl-main.747", doi = "10.18653/v1/2020.acl-main.747", pages = "8440--8451", abstract = "This paper shows that pretraining multilingual language models at scale leads to significant performance gains for a wide range of cross-lingual transfer tasks. We train a Transformer-based masked language model on one hundred languages, using more than two terabytes of filtered CommonCrawl data. Our model, dubbed XLM-R, significantly outperforms multilingual BERT (mBERT) on a variety of cross-lingual benchmarks, including +14.6{\%} average accuracy on XNLI, +13{\%} average F1 score on MLQA, and +2.4{\%} F1 score on NER. XLM-R performs particularly well on low-resource languages, improving 15.7{\%} in XNLI accuracy for Swahili and 11.4{\%} for Urdu over previous XLM models. We also present a detailed empirical analysis of the key factors that are required to achieve these gains, including the trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling without sacrificing per-language performance; XLM-R is very competitive with strong monolingual models on the GLUE and XNLI benchmarks. We will make our code and models publicly available.", } ``` ```bibtex @inproceedings{wenzek-etal-2020-ccnet, title = "{CCN}et: Extracting High Quality Monolingual Datasets from Web Crawl Data", author = "Wenzek, Guillaume and Lachaux, Marie-Anne and Conneau, Alexis and Chaudhary, Vishrav and Guzm{\'a}n, Francisco and Joulin, Armand and Grave, Edouard", booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference", month = may, year = "2020", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://www.aclweb.org/anthology/2020.lrec-1.494", pages = "4003--4012", abstract = "Pre-training text representations have led to significant improvements in many areas of natural language processing. The quality of these models benefits greatly from the size of the pretraining corpora as long as its quality is preserved. In this paper, we describe an automatic pipeline to extract massive high-quality monolingual datasets from Common Crawl for a variety of languages. Our pipeline follows the data processing introduced in fastText (Mikolov et al., 2017; Grave et al., 2018), that deduplicates documents and identifies their language. We augment this pipeline with a filtering step to select documents that are close to high quality corpora like Wikipedia.", language = "English", ISBN = "979-10-95546-34-4", } ``` ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.

mkqa

--- annotations_creators: - crowdsourced language_creators: - found language: - ar - da - de - en - es - fi - fr - he - hu - it - ja - km - ko - ms - nl - 'no' - pl - pt - ru - sv - th - tr - vi - zh license: - cc-by-3.0 multilinguality: - multilingual - translation size_categories: - 10K<n<100K source_datasets: - extended|natural_questions - original task_categories: - question-answering task_ids: - open-domain-qa paperswithcode_id: mkqa pretty_name: Multilingual Knowledge Questions and Answers dataset_info: features: - name: example_id dtype: string - name: queries struct: - name: ar dtype: string - name: da dtype: string - name: de dtype: string - name: en dtype: string - name: es dtype: string - name: fi dtype: string - name: fr dtype: string - name: he dtype: string - name: hu dtype: string - name: it dtype: string - name: ja dtype: string - name: ko dtype: string - name: km dtype: string - name: ms dtype: string - name: nl dtype: string - name: 'no' dtype: string - name: pl dtype: string - name: pt dtype: string - name: ru dtype: string - name: sv dtype: string - name: th dtype: string - name: tr dtype: string - name: vi dtype: string - name: zh_cn dtype: string - name: zh_hk dtype: string - name: zh_tw dtype: string - name: query dtype: string - name: answers struct: - name: ar list: - name: type dtype: class_label: names: '0': entity '1': long_answer '2': unanswerable '3': date '4': number '5': number_with_unit '6': short_phrase '7': binary - name: entity dtype: string - name: text dtype: string - name: aliases list: string - name: da list: - name: type dtype: class_label: names: '0': entity '1': long_answer '2': unanswerable '3': date '4': number '5': number_with_unit '6': short_phrase '7': binary - name: entity dtype: string - name: text dtype: string - name: aliases list: string - name: de list: - name: type dtype: class_label: names: '0': entity '1': long_answer '2': unanswerable '3': date '4': number '5': number_with_unit '6': short_phrase '7': binary - name: entity dtype: string - name: text dtype: string - name: aliases list: string - name: en list: - name: type dtype: class_label: names: '0': entity '1': long_answer '2': unanswerable '3': date '4': number '5': number_with_unit '6': short_phrase '7': binary - name: entity dtype: string - name: text dtype: string - name: aliases list: string - name: es list: - name: type dtype: class_label: names: '0': entity '1': long_answer '2': unanswerable '3': date '4': number '5': number_with_unit '6': short_phrase '7': binary - name: entity dtype: string - name: text dtype: string - name: aliases list: string - name: fi list: - name: type dtype: class_label: names: '0': entity '1': long_answer '2': unanswerable '3': date '4': number '5': number_with_unit '6': short_phrase '7': binary - name: entity dtype: string - name: text dtype: string - name: aliases list: string - name: fr list: - name: type dtype: class_label: names: '0': entity '1': long_answer '2': unanswerable '3': date '4': number '5': number_with_unit '6': short_phrase '7': binary - name: entity dtype: string - name: text dtype: string - name: aliases list: string - name: he list: - name: type dtype: class_label: names: '0': entity '1': long_answer '2': unanswerable '3': date '4': number '5': number_with_unit '6': short_phrase '7': binary - name: entity dtype: string - name: text dtype: string - name: aliases list: string - name: hu list: - name: type dtype: class_label: names: '0': entity '1': long_answer '2': unanswerable '3': date '4': number '5': number_with_unit '6': short_phrase '7': binary - name: entity dtype: string - name: text dtype: string - name: aliases list: string - name: it list: - name: type dtype: class_label: names: '0': entity '1': long_answer '2': unanswerable '3': date '4': number '5': number_with_unit '6': short_phrase '7': binary - name: entity dtype: string - name: text dtype: string - name: aliases list: string - name: ja list: - name: type dtype: class_label: names: '0': entity '1': long_answer '2': unanswerable '3': date '4': number '5': number_with_unit '6': short_phrase '7': binary - name: entity dtype: string - name: text dtype: string - name: aliases list: string - name: ko list: - name: type dtype: class_label: names: '0': entity '1': long_answer '2': unanswerable '3': date '4': number '5': number_with_unit '6': short_phrase '7': binary - name: entity dtype: string - name: text dtype: string - name: aliases list: string - name: km list: - name: type dtype: class_label: names: '0': entity '1': long_answer '2': unanswerable '3': date '4': number '5': number_with_unit '6': short_phrase '7': binary - name: entity dtype: string - name: text dtype: string - name: aliases list: string - name: ms list: - name: type dtype: class_label: names: '0': entity '1': long_answer '2': unanswerable '3': date '4': number '5': number_with_unit '6': short_phrase '7': binary - name: entity dtype: string - name: text dtype: string - name: aliases list: string - name: nl list: - name: type dtype: class_label: names: '0': entity '1': long_answer '2': unanswerable '3': date '4': number '5': number_with_unit '6': short_phrase '7': binary - name: entity dtype: string - name: text dtype: string - name: aliases list: string - name: 'no' list: - name: type dtype: class_label: names: '0': entity '1': long_answer '2': unanswerable '3': date '4': number '5': number_with_unit '6': short_phrase '7': binary - name: entity dtype: string - name: text dtype: string - name: aliases list: string - name: pl list: - name: type dtype: class_label: names: '0': entity '1': long_answer '2': unanswerable '3': date '4': number '5': number_with_unit '6': short_phrase '7': binary - name: entity dtype: string - name: text dtype: string - name: aliases list: string - name: pt list: - name: type dtype: class_label: names: '0': entity '1': long_answer '2': unanswerable '3': date '4': number '5': number_with_unit '6': short_phrase '7': binary - name: entity dtype: string - name: text dtype: string - name: aliases list: string - name: ru list: - name: type dtype: class_label: names: '0': entity '1': long_answer '2': unanswerable '3': date '4': number '5': number_with_unit '6': short_phrase '7': binary - name: entity dtype: string - name: text dtype: string - name: aliases list: string - name: sv list: - name: type dtype: class_label: names: '0': entity '1': long_answer '2': unanswerable '3': date '4': number '5': number_with_unit '6': short_phrase '7': binary - name: entity dtype: string - name: text dtype: string - name: aliases list: string - name: th list: - name: type dtype: class_label: names: '0': entity '1': long_answer '2': unanswerable '3': date '4': number '5': number_with_unit '6': short_phrase '7': binary - name: entity dtype: string - name: text dtype: string - name: aliases list: string - name: tr list: - name: type dtype: class_label: names: '0': entity '1': long_answer '2': unanswerable '3': date '4': number '5': number_with_unit '6': short_phrase '7': binary - name: entity dtype: string - name: text dtype: string - name: aliases list: string - name: vi list: - name: type dtype: class_label: names: '0': entity '1': long_answer '2': unanswerable '3': date '4': number '5': number_with_unit '6': short_phrase '7': binary - name: entity dtype: string - name: text dtype: string - name: aliases list: string - name: zh_cn list: - name: type dtype: class_label: names: '0': entity '1': long_answer '2': unanswerable '3': date '4': number '5': number_with_unit '6': short_phrase '7': binary - name: entity dtype: string - name: text dtype: string - name: aliases list: string - name: zh_hk list: - name: type dtype: class_label: names: '0': entity '1': long_answer '2': unanswerable '3': date '4': number '5': number_with_unit '6': short_phrase '7': binary - name: entity dtype: string - name: text dtype: string - name: aliases list: string - name: zh_tw list: - name: type dtype: class_label: names: '0': entity '1': long_answer '2': unanswerable '3': date '4': number '5': number_with_unit '6': short_phrase '7': binary - name: entity dtype: string - name: text dtype: string - name: aliases list: string config_name: mkqa splits: - name: train num_bytes: 36005650 num_examples: 10000 download_size: 11903948 dataset_size: 36005650 --- # Dataset Card for MKQA: Multilingual Knowledge Questions & Answers ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - [**Homepage:**](https://github.com/apple/ml-mkqa/) - [**Paper:**](https://arxiv.org/abs/2007.15207) ### Dataset Summary MKQA contains 10,000 queries sampled from the [Google Natural Questions dataset](https://github.com/google-research-datasets/natural-questions). For each query we collect new passage-independent answers. These queries and answers are then human translated into 25 Non-English languages. ### Supported Tasks and Leaderboards `question-answering` ### Languages | Language code | Language name | |---------------|---------------| | `ar` | `Arabic` | | `da` | `Danish` | | `de` | `German` | | `en` | `English` | | `es` | `Spanish` | | `fi` | `Finnish` | | `fr` | `French` | | `he` | `Hebrew` | | `hu` | `Hungarian` | | `it` | `Italian` | | `ja` | `Japanese` | | `ko` | `Korean` | | `km` | `Khmer` | | `ms` | `Malay` | | `nl` | `Dutch` | | `no` | `Norwegian` | | `pl` | `Polish` | | `pt` | `Portuguese` | | `ru` | `Russian` | | `sv` | `Swedish` | | `th` | `Thai` | | `tr` | `Turkish` | | `vi` | `Vietnamese` | | `zh_cn` | `Chinese (Simplified)` | | `zh_hk` | `Chinese (Hong kong)` | | `zh_tw` | `Chinese (Traditional)` | ## Dataset Structure ### Data Instances An example from the data set looks as follows: ``` { 'example_id': 563260143484355911, 'queries': { 'en': "who sings i hear you knocking but you can't come in", 'ru': "кто поет i hear you knocking but you can't come in", 'ja': '「 I hear you knocking」は誰が歌っていますか', 'zh_cn': "《i hear you knocking but you can't come in》是谁演唱的", ... }, 'query': "who sings i hear you knocking but you can't come in", 'answers': {'en': [{'type': 'entity', 'entity': 'Q545186', 'text': 'Dave Edmunds', 'aliases': []}], 'ru': [{'type': 'entity', 'entity': 'Q545186', 'text': 'Эдмундс, Дэйв', 'aliases': ['Эдмундс', 'Дэйв Эдмундс', 'Эдмундс Дэйв', 'Dave Edmunds']}], 'ja': [{'type': 'entity', 'entity': 'Q545186', 'text': 'デイヴ・エドモンズ', 'aliases': ['デーブ・エドモンズ', 'デイブ・エドモンズ']}], 'zh_cn': [{'type': 'entity', 'text': '戴维·埃德蒙兹 ', 'entity': 'Q545186'}], ... }, } ``` ### Data Fields Each example in the dataset contains the unique Natural Questions `example_id`, the original English `query`, and then `queries` and `answers` in 26 languages. Each answer is labelled with an answer type. The breakdown is: | Answer Type | Occurrence | |---------------|---------------| | `entity` | `4221` | | `long_answer` | `1815` | | `unanswerable` | `1427` | | `date` | `1174` | | `number` | `485` | | `number_with_unit` | `394` | | `short_phrase` | `346` | | `binary` | `138` | For each language, there can be more than one acceptable textual answer, in order to capture a variety of possible valid answers. Detailed explanation of fields taken from [here](https://github.com/apple/ml-mkqa/#dataset) when `entity` field is not available it is set to an empty string ''. when `aliases` field is not available it is set to an empty list []. ### Data Splits - Train: 10000 ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [Google Natural Questions dataset](https://github.com/google-research-datasets/natural-questions) #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [CC BY-SA 3.0](https://github.com/apple/ml-mkqa#license) ### Citation Information ``` @misc{mkqa, title = {MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering}, author = {Shayne Longpre and Yi Lu and Joachim Daiber}, year = {2020}, URL = {https://arxiv.org/pdf/2007.15207.pdf} } ``` ### Contributions Thanks to [@cceyda](https://github.com/cceyda) for adding this dataset.

yahma/alpaca-cleaned

yahma

--- license: cc-by-4.0 language: - en tags: - instruction-finetuning pretty_name: Alpaca-Cleaned task_categories: - text-generation --- # Dataset Card for Alpaca-Cleaned - **Repository:** https://github.com/gururise/AlpacaDataCleaned ## Dataset Description This is a cleaned version of the original Alpaca Dataset released by Stanford. The following issues have been identified in the original release and fixed in this dataset: 1. **Hallucinations:** Many instructions in the original dataset had instructions referencing data on the internet, which just caused GPT3 to hallucinate an answer. ``` "instruction":"Summarize the given article in 200 Words.", "input": "https://www.bbc.com/news/world-51461830", "output": "The recent protests in Belarus are part of a growing wave of discontent against the rule of Alexander Lukashenko, who has led the country since 1994. Demonstrations against his government have been ongoing since August, and come in the wake of an election widely seen as rigged. As police and security forces have cracked down on protesters, the movement has only grown, with hundreds of thousands of people taking to the streets to call for new leadership. Human rights activists have accused the government of using extreme violence in order prosecute protesters, leading to an international outcry and sanctions from the European Union. As the situation in Belarus continues to unfold, the international community is monitoring the situation closely - hoping for a peaceful resolution, while also standing firm against the oppressive tactics employed by the Belarusian state." "instruction": "Classify the following shape according to simple geometry.", "input": "<Picture Attached>", "output": "Triangle" "instruction": "Given a recent news article, write a headline for it.", "input": "https://www.news.example.com/article-1", "output": "\"NASA Launches a Mars 2020 Mission to Search for Signs of Ancient Life\"" ``` 2. **Merged Instructions:** There were many instructions that were merged together in the original dataset for some reason. ``` "instruction": "Should I invest in stocks?", "input": "", "output": "It depends on your individual situation. Investing in stocks carries a certain amount of risk but can also offer potential rewards. Make sure to research the different types of stocks, understand the risks and rewards, and develop a plan that works for you.\n##\n18. Instruction: Identify the noun in the following sentence:\n18. Input:\nThe large apple was delicious.\n18. Output:\nApple" ``` 3. **Empty outputs:** Some entries in the original dataset had empty outputs. ``` "instruction": "Calculate the area of the following shape in square centimeters.", "input": "rectangle of size 4 cm x 5 cm", "output": "" ``` 4. **Empty code examples:** Some descriptions in the original dataset were missing code examples, making it difficult to understand the intended behavior of the code. 5. **Instructions to generate images:** Some descriptions in the original dataset included instructions to generate images, something obviously not possible. ``` "instruction": "Create a graphic or logo that visually represents the word \"courage\".", "input": "", "output": "<No Output>" ``` 6. **N/A outputs:** Some code snippets in the original dataset had N/A outputs. 7. **Inconsistent input field:** The original dataset had inconsistent usage of the input field when it was supposed to be empty. ``` "input":"<no input>" "input":"No input" "input":"noinput" "input":"<noinput>" ``` 8. **Wrong answers:** Some instructions/questions in the original dataset had incorrect answers. About 80% of the math problems are estimated to have incorrect answers. ``` "instruction": "Calculate the median of the following data set.", "input": "1, 2, 4, 5, 8, 9", "output": "5" "instruction": "Convert 25m to km.", "input": "", "output": "25km" ``` 9. **Non-Sensical/Unclear instructions:** Many instructions are unclear, we try to clarify (or re-write) if instructions are non-sensical. Instructions that are slightly unclear, but where one could deduce the meaning are not altered. ``` "instruction": "Freeze the following sample of yogurt for 10 minutes.", "input": "Yogurt sample", "output": "<noinput>" "instruction": "Increase the font size to 12 points.", "input": "", "output": "The font size has been increased to 12 points." ``` 10. **Extraneous escape and control characters:** The original dataset had several entries with extraneous escape and control characters. ### Original Alpaca Dataset Summary Alpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's `text-davinci-003` engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better. The authors built on the data generation pipeline from [Self-Instruct framework](https://github.com/yizhongw/self-instruct) and made the following modifications: - The `text-davinci-003` engine to generate the instruction data instead of `davinci`. - A [new prompt](https://github.com/tatsu-lab/stanford_alpaca/blob/main/prompt.txt) was written that explicitly gave the requirement of instruction generation to `text-davinci-003`. - Much more aggressive batch decoding was used, i.e., generating 20 instructions at once, which significantly reduced the cost of data generation. - The data generation pipeline was simplified by discarding the difference between classification and non-classification instructions. - Only a single instance was generated for each instruction, instead of 2 to 3 instances as in Self-Instruct. This produced an instruction-following dataset with 52K examples obtained at a much lower cost (less than $500). In a preliminary study, the authors also found that the 52K generated data to be much more diverse than the data released by [Self-Instruct](https://github.com/yizhongw/self-instruct/blob/main/data/seed_tasks.jsonl). ### Supported Tasks and Leaderboards The Alpaca dataset designed for instruction training pretrained language models. ### Languages The data in Alpaca are in English (BCP-47 en). ## Dataset Structure ### Data Instances An example of "train" looks as follows: ```json { "instruction": "Create a classification task by clustering the given list of items.", "input": "Apples, oranges, bananas, strawberries, pineapples", "output": "Class 1: Apples, Oranges\nClass 2: Bananas, Strawberries\nClass 3: Pineapples", "text": "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\nCreate a classification task by clustering the given list of items.\n\n### Input:\nApples, oranges, bananas, strawberries, pineapples\n\n### Response:\nClass 1: Apples, Oranges\nClass 2: Bananas, Strawberries\nClass 3: Pineapples", } ``` ### Data Fields The data fields are as follows: * `instruction`: describes the task the model should perform. Each of the 52K instructions is unique. * `input`: optional context or input for the task. For example, when the instruction is "Summarize the following article", the input is the article. Around 40% of the examples have an input. * `output`: the answer to the instruction as generated by `text-davinci-003`. * `text`: the `instruction`, `input` and `output` formatted with the [prompt template](https://github.com/tatsu-lab/stanford_alpaca#data-release) used by the authors for fine-tuning their models. ### Data Splits | | train | |---------------|------:| | alpaca | 52002 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset Excerpt the [blog post](https://crfm.stanford.edu/2023/03/13/alpaca.html) accompanying the release of this dataset: > We believe that releasing the above assets will enable the academic community to perform controlled scientific studies on instruction-following language models, resulting in better science and ultimately new techniques to address the existing deficiencies with these models. At the same time, any release carries some risk. First, we recognize that releasing our training recipe reveals the feasibility of certain capabilities. On one hand, this enables more people (including bad actors) to create models that could cause harm (either intentionally or not). On the other hand, this awareness might incentivize swift defensive action, especially from the academic community, now empowered by the means to perform deeper safety research on such models. Overall, we believe that the benefits for the research community outweigh the risks of this particular release. Given that we are releasing the training recipe, we believe that releasing the data, model weights, and training code incur minimal further risk, given the simplicity of the recipe. At the same time, releasing these assets has enormous benefits for reproducible science, so that the academic community can use standard datasets, models, and code to perform controlled comparisons and to explore extensions. Deploying an interactive demo for Alpaca also poses potential risks, such as more widely disseminating harmful content and lowering the barrier for spam, fraud, or disinformation. We have put into place two risk mitigation strategies. First, we have implemented a content filter using OpenAI’s content moderation API, which filters out harmful content as defined by OpenAI’s usage policies. Second, we watermark all the model outputs using the method described in Kirchenbauer et al. 2023, so that others can detect (with some probability) whether an output comes from Alpaca 7B. Finally, we have strict terms and conditions for using the demo; it is restricted to non-commercial uses and to uses that follow LLaMA’s license agreement. We understand that these mitigation measures can be circumvented once we release the model weights or if users train their own instruction-following models. However, by installing these mitigations, we hope to advance the best practices and ultimately develop community norms for the responsible deployment of foundation models. ### Discussion of Biases [More Information Needed] ### Other Known Limitations The `alpaca` data is generated by a language model (`text-davinci-003`) and inevitably contains some errors or biases. We encourage users to use this data with caution and propose new methods to filter or improve the imperfections. ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information The dataset is available under the [Creative Commons NonCommercial (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/legalcode). ### Citation Information ``` @misc{alpaca, author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto }, title = {Stanford Alpaca: An Instruction-following LLaMA model}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}}, } ``` ### Contributions [More Information Needed]

GEM/xlsum

GEM

--- annotations_creators: - none language_creators: - unknown language: - und license: - cc-by-nc-sa-4.0 multilinguality: - unknown size_categories: - unknown source_datasets: - original task_categories: - summarization task_ids: [] pretty_name: xlsum --- # Dataset Card for GEM/xlsum ## Dataset Description - **Homepage:** https://github.com/csebuetnlp/xl-sum - **Repository:** https://huggingface.co/datasets/csebuetnlp/xlsum/tree/main/data - **Paper:** https://aclanthology.org/2021.findings-acl.413/ - **Leaderboard:** http://explainaboard.nlpedia.ai/leaderboard/task_xlsum/ - **Point of Contact:** Tahmid Hasan ### Link to Main Data Card You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/xlsum). ### Dataset Summary XLSum is a highly multilingual summarization dataset supporting 44 language. The data stems from BBC news articles. You can load the dataset via: ``` import datasets data = datasets.load_dataset('GEM/xlsum') ``` The data loader can be found [here](https://huggingface.co/datasets/GEM/xlsum). #### website [Github](https://github.com/csebuetnlp/xl-sum) #### paper [ACL Anthology](https://aclanthology.org/2021.findings-acl.413/) ## Dataset Overview ### Where to find the Data and its Documentation #### Webpage <!-- info: What is the webpage for the dataset (if it exists)? --> <!-- scope: telescope --> [Github](https://github.com/csebuetnlp/xl-sum) #### Download <!-- info: What is the link to where the original dataset is hosted? --> <!-- scope: telescope --> [Huggingface](https://huggingface.co/datasets/csebuetnlp/xlsum/tree/main/data) #### Paper <!-- info: What is the link to the paper describing the dataset (open access preferred)? --> <!-- scope: telescope --> [ACL Anthology](https://aclanthology.org/2021.findings-acl.413/) #### BibTex <!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. --> <!-- scope: microscope --> ``` @inproceedings{hasan-etal-2021-xl, title = "{XL}-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages", author = "Hasan, Tahmid and Bhattacharjee, Abhik and Islam, Md. Saiful and Mubasshir, Kazi and Li, Yuan-Fang and Kang, Yong-Bin and Rahman, M. Sohel and Shahriyar, Rifat", booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-acl.413", pages = "4693--4703", } ``` #### Contact Name <!-- quick --> <!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. --> <!-- scope: periscope --> Tahmid Hasan #### Contact Email <!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. --> <!-- scope: periscope --> tahmidhasan@cse.buet.ac.bd #### Has a Leaderboard? <!-- info: Does the dataset have an active leaderboard? --> <!-- scope: telescope --> yes #### Leaderboard Link <!-- info: Provide a link to the leaderboard. --> <!-- scope: periscope --> [Explainaboard](http://explainaboard.nlpedia.ai/leaderboard/task_xlsum/) #### Leaderboard Details <!-- info: Briefly describe how the leaderboard evaluates models. --> <!-- scope: microscope --> The leaderboard ranks models based on ROUGE scores (R1/R2/RL) of the generated summaries. ### Languages and Intended Use #### Multilingual? <!-- quick --> <!-- info: Is the dataset multilingual? --> <!-- scope: telescope --> yes #### Covered Languages <!-- quick --> <!-- info: What languages/dialects are covered in the dataset? --> <!-- scope: telescope --> `Amharic`, `Arabic`, `Azerbaijani`, `Bengali, Bangla`, `Burmese`, `Chinese (family)`, `English`, `French`, `Gujarati`, `Hausa`, `Hindi`, `Igbo`, `Indonesian`, `Japanese`, `Rundi`, `Korean`, `Kirghiz, Kyrgyz`, `Marathi`, `Nepali (individual language)`, `Oromo`, `Pushto, Pashto`, `Persian`, `Ghanaian Pidgin English`, `Portuguese`, `Panjabi, Punjabi`, `Russian`, `Scottish Gaelic, Gaelic`, `Serbian`, `Romano-Serbian`, `Sinhala, Sinhalese`, `Somali`, `Spanish, Castilian`, `Swahili (individual language), Kiswahili`, `Tamil`, `Telugu`, `Thai`, `Tigrinya`, `Turkish`, `Ukrainian`, `Urdu`, `Uzbek`, `Vietnamese`, `Welsh`, `Yoruba` #### License <!-- quick --> <!-- info: What is the license of the dataset? --> <!-- scope: telescope --> cc-by-nc-sa-4.0: Creative Commons Attribution Non Commercial Share Alike 4.0 International #### Intended Use <!-- info: What is the intended use of the dataset? --> <!-- scope: microscope --> Abstractive summarization has centered around the English language, as most large abstractive summarization datasets are available in English only. Though there have been some recent efforts for curating multilingual abstractive summarization datasets, they are limited in terms of the number of languages covered, the number of training samples, or both. To this end, **XL-Sum** presents a large-scale abstractive summarization dataset of 1.35 million news articles from 45 languages crawled from the British Broadcasting Corporation website. It is intended to be used for both multilingual and per-language summarization tasks. #### Primary Task <!-- info: What primary task does the dataset support? --> <!-- scope: telescope --> Summarization #### Communicative Goal <!-- quick --> <!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. --> <!-- scope: periscope --> Summarize news-like text in one of 45 languages. ### Credit #### Curation Organization Type(s) <!-- info: In what kind of organization did the dataset curation happen? --> <!-- scope: telescope --> `academic` #### Curation Organization(s) <!-- info: Name the organization(s). --> <!-- scope: periscope --> Bangladesh University of Engineering and Technology #### Who added the Dataset to GEM? <!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. --> <!-- scope: microscope --> Tahmid Hasan (Bangladesh University of Engineering and Technology), Abhik Bhattacharjee (Bangladesh University of Engineering and Technology) ### Dataset Structure #### Data Fields <!-- info: List and describe the fields present in the dataset. --> <!-- scope: telescope --> - `gem_id`: A string representing the article ID. - `url`: A string representing the article URL. - `title`: A string containing the article title. - `summary`: A string containing the article summary. - `text` : A string containing the article text. #### Example Instance <!-- info: Provide a JSON formatted example of a typical instance in the dataset. --> <!-- scope: periscope --> ``` { "gem_id": "GEM-xlsum_english-train-1589", "url": "[BBC news](https://www.bbc.com/news)/technology-17657859", "title": "Yahoo files e-book advert system patent applications", "summary": "Yahoo has signalled it is investigating e-book adverts as a way to stimulate its earnings.", "text": "Yahoo's patents suggest users could weigh the type of ads against the sizes of discount before purchase. It says in two US patent applications that ads for digital book readers have been \"less than optimal\" to date. The filings suggest that users could be offered titles at a variety of prices depending on the ads' prominence They add that the products shown could be determined by the type of book being read, or even the contents of a specific chapter, phrase or word. The paperwork was published by the US Patent and Trademark Office late last week and relates to work carried out at the firm's headquarters in Sunnyvale, California. \"Greater levels of advertising, which may be more valuable to an advertiser and potentially more distracting to an e-book reader, may warrant higher discounts,\" it states. Free books It suggests users could be offered ads as hyperlinks based within the book's text, in-laid text or even \"dynamic content\" such as video. Another idea suggests boxes at the bottom of a page could trail later chapters or quotes saying \"brought to you by Company A\". It adds that the more willing the customer is to see the ads, the greater the potential discount. \"Higher frequencies... may even be great enough to allow the e-book to be obtained for free,\" it states. The authors write that the type of ad could influence the value of the discount, with \"lower class advertising... such as teeth whitener advertisements\" offering a cheaper price than \"high\" or \"middle class\" adverts, for things like pizza. The inventors also suggest that ads could be linked to the mood or emotional state the reader is in as a they progress through a title. For example, they say if characters fall in love or show affection during a chapter, then ads for flowers or entertainment could be triggered. The patents also suggest this could applied to children's books - giving the Tom Hanks animated film Polar Express as an example. It says a scene showing a waiter giving the protagonists hot drinks \"may be an excellent opportunity to show an advertisement for hot cocoa, or a branded chocolate bar\". Another example states: \"If the setting includes young characters, a Coke advertisement could be provided, inviting the reader to enjoy a glass of Coke with his book, and providing a graphic of a cool glass.\" It adds that such targeting could be further enhanced by taking account of previous titles the owner has bought. 'Advertising-free zone' At present, several Amazon and Kobo e-book readers offer full-screen adverts when the device is switched off and show smaller ads on their menu screens, but the main text of the titles remains free of marketing. Yahoo does not currently provide ads to these devices, and a move into the area could boost its shrinking revenues. However, Philip Jones, deputy editor of the Bookseller magazine, said that the internet firm might struggle to get some of its ideas adopted. \"This has been mooted before and was fairly well decried,\" he said. \"Perhaps in a limited context it could work if the merchandise was strongly related to the title and was kept away from the text. \"But readers - particularly parents - like the fact that reading is an advertising-free zone. Authors would also want something to say about ads interrupting their narrative flow.\"" } ``` #### Data Splits <!-- info: Describe and name the splits in the dataset if there are more than one. --> <!-- scope: periscope --> The splits in the dataset are specified by the language names, which are as follows: - `amharic` - `arabic` - `azerbaijani` - `bengali` - `burmese` - `chinese_simplified` - `chinese_traditional` - `english` - `french` - `gujarati` - `hausa` - `hindi` - `igbo` - `indonesian` - `japanese` - `kirundi` - `korean` - `kyrgyz` - `marathi` - `nepali` - `oromo` - `pashto` - `persian` - `pidgin` - `portuguese` - `punjabi` - `russian` - `scottish_gaelic` - `serbian_cyrillic` - `serbian_latin` - `sinhala` - `somali` - `spanish` - `swahili` - `tamil` - `telugu` - `thai` - `tigrinya` - `turkish` - `ukrainian` - `urdu` - `uzbek` - `vietnamese` - `welsh` - `yoruba` #### Splitting Criteria <!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. --> <!-- scope: microscope --> We used a 80%-10%-10% split for all languages with a few exceptions. `English` was split 93%-3.5%-3.5% for the evaluation set size to resemble that of `CNN/DM` and `XSum`; `Scottish Gaelic`, `Kyrgyz` and `Sinhala` had relatively fewer samples, their evaluation sets were increased to 500 samples for more reliable evaluation. Same articles were used for evaluation in the two variants of Chinese and Serbian to prevent data leakage in multilingual training. Individual dataset download links with train-dev-test example counts are given below: Language | ISO 639-1 Code | BBC subdomain(s) | Train | Dev | Test | Total | --------------|----------------|------------------|-------|-----|------|-------| Amharic | am | [BBC amharic](https://www.bbc.com/amharic) | 5761 | 719 | 719 | 7199 | Arabic | ar | [BBC arabic](https://www.bbc.com/arabic) | 37519 | 4689 | 4689 | 46897 | Azerbaijani | az | [BBC azeri](https://www.bbc.com/azeri) | 6478 | 809 | 809 | 8096 | Bengali | bn | [BBC bengali](https://www.bbc.com/bengali) | 8102 | 1012 | 1012 | 10126 | Burmese | my | [BBC burmese](https://www.bbc.com/burmese) | 4569 | 570 | 570 | 5709 | Chinese (Simplified) | zh-CN | [BBC ukchina](https://www.bbc.com/ukchina)/simp, [BBC zhongwen](https://www.bbc.com/zhongwen)/simp | 37362 | 4670 | 4670 | 46702 | Chinese (Traditional) | zh-TW | [BBC ukchina](https://www.bbc.com/ukchina)/trad, [BBC zhongwen](https://www.bbc.com/zhongwen)/trad | 37373 | 4670 | 4670 | 46713 | English | en | [BBC english](https://www.bbc.com/english), [BBC sinhala](https://www.bbc.com/sinhala) `*` | 306522 | 11535 | 11535 | 329592 | French | fr | [BBC afrique](https://www.bbc.com/afrique) | 8697 | 1086 | 1086 | 10869 | Gujarati | gu | [BBC gujarati](https://www.bbc.com/gujarati) | 9119 | 1139 | 1139 | 11397 | Hausa | ha | [BBC hausa](https://www.bbc.com/hausa) | 6418 | 802 | 802 | 8022 | Hindi | hi | [BBC hindi](https://www.bbc.com/hindi) | 70778 | 8847 | 8847 | 88472 | Igbo | ig | [BBC igbo](https://www.bbc.com/igbo) | 4183 | 522 | 522 | 5227 | Indonesian | id | [BBC indonesia](https://www.bbc.com/indonesia) | 38242 | 4780 | 4780 | 47802 | Japanese | ja | [BBC japanese](https://www.bbc.com/japanese) | 7113 | 889 | 889 | 8891 | Kirundi | rn | [BBC gahuza](https://www.bbc.com/gahuza) | 5746 | 718 | 718 | 7182 | Korean | ko | [BBC korean](https://www.bbc.com/korean) | 4407 | 550 | 550 | 5507 | Kyrgyz | ky | [BBC kyrgyz](https://www.bbc.com/kyrgyz) | 2266 | 500 | 500 | 3266 | Marathi | mr | [BBC marathi](https://www.bbc.com/marathi) | 10903 | 1362 | 1362 | 13627 | Nepali | np | [BBC nepali](https://www.bbc.com/nepali) | 5808 | 725 | 725 | 7258 | Oromo | om | [BBC afaanoromoo](https://www.bbc.com/afaanoromoo) | 6063 | 757 | 757 | 7577 | Pashto | ps | [BBC pashto](https://www.bbc.com/pashto) | 14353 | 1794 | 1794 | 17941 | Persian | fa | [BBC persian](https://www.bbc.com/persian) | 47251 | 5906 | 5906 | 59063 | Pidgin`**` | pcm | [BBC pidgin](https://www.bbc.com/pidgin) | 9208 | 1151 | 1151 | 11510 | Portuguese | pt | [BBC portuguese](https://www.bbc.com/portuguese) | 57402 | 7175 | 7175 | 71752 | Punjabi | pa | [BBC punjabi](https://www.bbc.com/punjabi) | 8215 | 1026 | 1026 | 10267 | Russian | ru | [BBC russian](https://www.bbc.com/russian), [BBC ukrainian](https://www.bbc.com/ukrainian) `*` | 62243 | 7780 | 7780 | 77803 | Scottish Gaelic | gd | [BBC naidheachdan](https://www.bbc.com/naidheachdan) | 1313 | 500 | 500 | 2313 | Serbian (Cyrillic) | sr | [BBC serbian](https://www.bbc.com/serbian)/cyr | 7275 | 909 | 909 | 9093 | Serbian (Latin) | sr | [BBC serbian](https://www.bbc.com/serbian)/lat | 7276 | 909 | 909 | 9094 | Sinhala | si | [BBC sinhala](https://www.bbc.com/sinhala) | 3249 | 500 | 500 | 4249 | Somali | so | [BBC somali](https://www.bbc.com/somali) | 5962 | 745 | 745 | 7452 | Spanish | es | [BBC mundo](https://www.bbc.com/mundo) | 38110 | 4763 | 4763 | 47636 | Swahili | sw | [BBC swahili](https://www.bbc.com/swahili) | 7898 | 987 | 987 | 9872 | Tamil | ta | [BBC tamil](https://www.bbc.com/tamil) | 16222 | 2027 | 2027 | 20276 | Telugu | te | [BBC telugu](https://www.bbc.com/telugu) | 10421 | 1302 | 1302 | 13025 | Thai | th | [BBC thai](https://www.bbc.com/thai) | 6616 | 826 | 826 | 8268 | Tigrinya | ti | [BBC tigrinya](https://www.bbc.com/tigrinya) | 5451 | 681 | 681 | 6813 | Turkish | tr | [BBC turkce](https://www.bbc.com/turkce) | 27176 | 3397 | 3397 | 33970 | Ukrainian | uk | [BBC ukrainian](https://www.bbc.com/ukrainian) | 43201 | 5399 | 5399 | 53999 | Urdu | ur | [BBC urdu](https://www.bbc.com/urdu) | 67665 | 8458 | 8458 | 84581 | Uzbek | uz | [BBC uzbek](https://www.bbc.com/uzbek) | 4728 | 590 | 590 | 5908 | Vietnamese | vi | [BBC vietnamese](https://www.bbc.com/vietnamese) | 32111 | 4013 | 4013 | 40137 | Welsh | cy | [BBC cymrufyw](https://www.bbc.com/cymrufyw) | 9732 | 1216 | 1216 | 12164 | Yoruba | yo | [BBC yoruba](https://www.bbc.com/yoruba) | 6350 | 793 | 793 | 7936 | `*` A lot of articles in BBC Sinhala and BBC Ukrainian were written in English and Russian respectively. They were identified using [Fasttext](https://arxiv.org/abs/1607.01759) and moved accordingly. `**` West African Pidgin English ## Dataset in GEM ### Rationale for Inclusion in GEM #### Why is the Dataset in GEM? <!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? --> <!-- scope: microscope --> Traditional abstractive text summarization has been centered around English and other high-resource languages. **XL-Sum** provides a large collection of high-quality article-summary pairs for 45 languages where the languages range from high-resource to extremely low-resource. This enables the research community to explore the summarization capabilities of different models for multiple languages and languages in isolation. We believe the addition of **XL-Sum** to GEM makes the domain of abstractive text summarization more diversified and inclusive to the research community. We hope our efforts in this work will encourage the community to push the boundaries of abstractive text summarization beyond the English language, especially for low and mid-resource languages, bringing technological advances to communities of these languages that have been traditionally under-served. #### Similar Datasets <!-- info: Do other datasets for the high level task exist? --> <!-- scope: telescope --> yes #### Unique Language Coverage <!-- info: Does this dataset cover other languages than other datasets for the same task? --> <!-- scope: periscope --> yes #### Difference from other GEM datasets <!-- info: What else sets this dataset apart from other similar datasets in GEM? --> <!-- scope: microscope --> The summaries are highly concise and abstractive. #### Ability that the Dataset measures <!-- info: What aspect of model ability can be measured with this dataset? --> <!-- scope: periscope --> Conciseness, abstractiveness, and overall summarization capability. ### GEM-Specific Curation #### Modificatied for GEM? <!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? --> <!-- scope: telescope --> no #### Additional Splits? <!-- info: Does GEM provide additional splits to the dataset? --> <!-- scope: telescope --> no ### Getting Started with the Task ## Previous Results ### Previous Results #### Measured Model Abilities <!-- info: What aspect of model ability can be measured with this dataset? --> <!-- scope: telescope --> Conciseness, abstractiveness, and overall summarization capability. #### Metrics <!-- info: What metrics are typically used for this task? --> <!-- scope: periscope --> `ROUGE` #### Proposed Evaluation <!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. --> <!-- scope: microscope --> ROUGE is the de facto evaluation metric used for text summarization. However, it was designed specifically for evaluating English texts. Due to the nature of the metric, scores are heavily dependent on text tokenization / stemming / unnecessary character removal, etc. Some modifications to the original ROUGE evaluation were done such as punctuation only removal, language specific tokenization/stemming to enable reliable comparison of source and target summaries across different scripts. #### Previous results available? <!-- info: Are previous results available? --> <!-- scope: telescope --> no ## Dataset Curation ### Original Curation #### Original Curation Rationale <!-- info: Original curation rationale --> <!-- scope: telescope --> State-of-the-art text summarization models are heavily data-driven, i.e., a large number of article-summary pairs are required to train them effectively. As a result, abstractive summarization has centered around the English language, as most large abstractive summarization datasets are available in English only. Though there have been some recent efforts for curating multilingual abstractive summarization datasets, they are limited in terms of the number of languages covered, the number of training samples, or both. To this end, we curate **XL-Sum**, a large-scale abstractive summarization dataset of 1.35 million news articles from 45 languages crawled from the British Broadcasting Corporation website. #### Communicative Goal <!-- info: What was the communicative goal? --> <!-- scope: periscope --> Introduce new languages in the english-centric domain of abstractive text summarization and enable both multilingual and per-language summarization. #### Sourced from Different Sources <!-- info: Is the dataset aggregated from different data sources? --> <!-- scope: telescope --> yes #### Source Details <!-- info: List the sources (one per line) --> <!-- scope: periscope --> British Broadcasting Corporation (BBC) news websites. ### Language Data #### How was Language Data Obtained? <!-- info: How was the language data obtained? --> <!-- scope: telescope --> `Found` #### Where was it found? <!-- info: If found, where from? --> <!-- scope: telescope --> `Multiple websites` #### Language Producers <!-- info: What further information do we have on the language producers? --> <!-- scope: microscope --> The language content was written by professional news editors hired by BBC. #### Topics Covered <!-- info: Does the language in the dataset focus on specific topics? How would you describe them? --> <!-- scope: periscope --> News #### Data Validation <!-- info: Was the text validated by a different worker or a data curator? --> <!-- scope: telescope --> not validated #### Data Preprocessing <!-- info: How was the text data pre-processed? (Enter N/A if the text was not pre-processed) --> <!-- scope: microscope --> We used 'NFKC' normalization on all text instances. #### Was Data Filtered? <!-- info: Were text instances selected or filtered? --> <!-- scope: telescope --> algorithmically #### Filter Criteria <!-- info: What were the selection criteria? --> <!-- scope: microscope --> We designed a crawler to recursively crawl pages starting from the homepage by visiting different article links present in each page visited. We were able to take advantage of the fact that all BBC sites have somewhat similar structures, and were able to scrape articles from all sites. We discarded pages with no textual contents (mostly pages consisting of multimedia contents) before further processing. We designed a number of heuristics to make the extraction effective by carefully examining the HTML structures of the crawled pages: 1. The desired summary must be present within the beginning two paragraphs of an article. 2. The summary paragraph must have some portion of texts in bold format. 3. The summary paragraph may contain some hyperlinks that may not be bold. The proportion of bold texts and hyperlinked texts to the total length of the paragraph in consideration must be at least 95\%. 4. All texts except the summary and the headline must be included in the input text (including image captions). 5. The input text must be at least twice as large as the summary. ### Structured Annotations #### Additional Annotations? <!-- quick --> <!-- info: Does the dataset have additional annotations for each instance? --> <!-- scope: telescope --> none #### Annotation Service? <!-- info: Was an annotation service used? --> <!-- scope: telescope --> no ### Consent #### Any Consent Policy? <!-- info: Was there a consent policy involved when gathering the data? --> <!-- scope: telescope --> yes #### Consent Policy Details <!-- info: What was the consent policy? --> <!-- scope: microscope --> BBC's policy specifies that the text content within its websites can be used for non-commercial research only. ### Private Identifying Information (PII) #### Contains PII? <!-- quick --> <!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? --> <!-- scope: telescope --> likely #### Categories of PII <!-- info: What categories of PII are present or suspected in the data? --> <!-- scope: periscope --> `generic PII` #### Any PII Identification? <!-- info: Did the curators use any automatic/manual method to identify PII in the dataset? --> <!-- scope: periscope --> no identification ### Maintenance #### Any Maintenance Plan? <!-- info: Does the original dataset have a maintenance plan? --> <!-- scope: telescope --> no ## Broader Social Context ### Previous Work on the Social Impact of the Dataset #### Usage of Models based on the Data <!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? --> <!-- scope: telescope --> no ### Impact on Under-Served Communities #### Addresses needs of underserved Communities? <!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). --> <!-- scope: telescope --> yes #### Details on how Dataset Addresses the Needs <!-- info: Describe how this dataset addresses the needs of underserved communities. --> <!-- scope: microscope --> This dataset introduces summarization corpus for many languages where there weren't any datasets like this curated before. ### Discussion of Biases #### Any Documented Social Biases? <!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. --> <!-- scope: telescope --> no #### Are the Language Producers Representative of the Language? <!-- info: Does the distribution of language producers in the dataset accurately represent the full distribution of speakers of the language world-wide? If not, how does it differ? --> <!-- scope: periscope --> Yes ## Considerations for Using the Data ### PII Risks and Liability ### Licenses #### Copyright Restrictions on the Dataset <!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? --> <!-- scope: periscope --> `research use only`, `non-commercial use only` #### Copyright Restrictions on the Language Data <!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? --> <!-- scope: periscope --> `research use only`, `non-commercial use only` ### Known Technical Limitations #### Technical Limitations <!-- info: Describe any known technical limitations, such as spurrious correlations, train/test overlap, annotation biases, or mis-annotations, and cite the works that first identified these limitations when possible. --> <!-- scope: microscope --> Human evaluation showed most languages had a high percentage of good summaries in the upper nineties, almost none of the summaries contained any conflicting information, while about one-third on average had information that was not directly inferrable from the source article. Since generally multiple articles are written regarding an important event, there could be an overlap between the training and evaluation data in terms on content. #### Unsuited Applications <!-- info: When using a model trained on this dataset in a setting where users or the public may interact with its predictions, what are some pitfalls to look out for? In particular, describe some applications of the general task featured in this dataset that its curation or properties make it less suitable for. --> <!-- scope: microscope --> The dataset is limited to news domain only. Hence it wouldn't be advisable to use a model trained on this dataset for summarizing texts from a different domain i.e. literature, scientific text etc. Another pitfall could be hallucinations in the model generated summary. #### Discouraged Use Cases <!-- info: What are some discouraged use cases of a model trained to maximize the proposed metrics on this dataset? In particular, think about settings where decisions made by a model that performs reasonably well on the metric my still have strong negative consequences for user or members of the public. --> <!-- scope: microscope --> ROUGE evaluates the quality of the summary as a whole by considering up to 4-gram overlaps. Therefore, in an article about India if the word "India" in the generated summary gets replaced by "Pakistan" due to model hallucination, the overall score wouldn't be reduced significantly, but the entire meaning could get changed.

common_voice

--- pretty_name: Common Voice annotations_creators: - crowdsourced language_creators: - crowdsourced language: - ab - ar - as - br - ca - cnh - cs - cv - cy - de - dv - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - hi - hsb - hu - ia - id - it - ja - ka - kab - ky - lg - lt - lv - mn - mt - nl - or - pa - pl - pt - rm - ro - ru - rw - sah - sl - sv - ta - th - tr - tt - uk - vi - vot - zh language_bcp47: - fy-NL - ga-IE - pa-IN - rm-sursilv - rm-vallader - sv-SE - zh-CN - zh-HK - zh-TW license: - cc0-1.0 multilinguality: - multilingual size_categories: - 100K<n<1M - 10K<n<100K - 1K<n<10K - n<1K source_datasets: - extended|common_voice task_categories: - automatic-speech-recognition task_ids: [] paperswithcode_id: common-voice dataset_info: - config_name: ab features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 1295622 num_examples: 22 - name: test num_bytes: 411844 num_examples: 9 - name: validation - name: other num_bytes: 40023390 num_examples: 752 - name: validated num_bytes: 1707426 num_examples: 31 - name: invalidated num_bytes: 361626 num_examples: 8 download_size: 41038412 dataset_size: 43799908 - config_name: ar features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 359335168 num_examples: 14227 - name: test num_bytes: 237546641 num_examples: 7622 - name: validation num_bytes: 209606861 num_examples: 7517 - name: other num_bytes: 515822404 num_examples: 18283 - name: validated num_bytes: 1182522872 num_examples: 43291 - name: invalidated num_bytes: 194805036 num_examples: 6333 download_size: 1756264615 dataset_size: 2699638982 - config_name: as features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 11442279 num_examples: 270 - name: test num_bytes: 5071343 num_examples: 110 - name: validation num_bytes: 5480156 num_examples: 124 - name: other - name: validated num_bytes: 21993698 num_examples: 504 - name: invalidated num_bytes: 886145 num_examples: 31 download_size: 22226465 dataset_size: 44873621 - config_name: br features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 62238289 num_examples: 2780 - name: test num_bytes: 54461339 num_examples: 2087 - name: validation num_bytes: 46995570 num_examples: 1997 - name: other num_bytes: 269858143 num_examples: 10912 - name: validated num_bytes: 203503622 num_examples: 8560 - name: invalidated num_bytes: 20861017 num_examples: 623 download_size: 465276982 dataset_size: 657917980 - config_name: ca features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 12966939466 num_examples: 285584 - name: test num_bytes: 745761890 num_examples: 15724 - name: validation num_bytes: 716442038 num_examples: 15724 - name: other num_bytes: 2693542910 num_examples: 64446 - name: validated num_bytes: 18115833966 num_examples: 416701 - name: invalidated num_bytes: 850402888 num_examples: 18846 download_size: 20743110341 dataset_size: 36088923158 - config_name: cnh features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 18866674 num_examples: 807 - name: test num_bytes: 24675321 num_examples: 752 - name: validation num_bytes: 22162315 num_examples: 756 - name: other num_bytes: 84878963 num_examples: 2934 - name: validated num_bytes: 69330148 num_examples: 2432 - name: invalidated num_bytes: 13642724 num_examples: 433 download_size: 161331331 dataset_size: 233556145 - config_name: cs features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 215205282 num_examples: 5655 - name: test num_bytes: 148499476 num_examples: 4144 - name: validation num_bytes: 148312130 num_examples: 4118 - name: other num_bytes: 282225475 num_examples: 7475 - name: validated num_bytes: 1019817024 num_examples: 30431 - name: invalidated num_bytes: 24717823 num_examples: 685 download_size: 1271909933 dataset_size: 1838777210 - config_name: cv features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 31649510 num_examples: 931 - name: test num_bytes: 32513061 num_examples: 788 - name: validation num_bytes: 28429779 num_examples: 818 - name: other num_bytes: 288294623 num_examples: 6927 - name: validated num_bytes: 126717875 num_examples: 3496 - name: invalidated num_bytes: 57923138 num_examples: 1282 download_size: 439329081 dataset_size: 565527986 - config_name: cy features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - 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name: invalidated num_bytes: 1440604803 num_examples: 32789 download_size: 23283812097 dataset_size: 37244945467 - config_name: dv features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 118576140 num_examples: 2680 - name: test num_bytes: 94281409 num_examples: 2202 - name: validation num_bytes: 94117088 num_examples: 2077 - name: other - name: validated num_bytes: 528571107 num_examples: 11866 - name: invalidated num_bytes: 37694847 num_examples: 840 download_size: 540488041 dataset_size: 873240591 - config_name: el features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 80759076 num_examples: 2316 - name: test num_bytes: 53820491 num_examples: 1522 - name: validation num_bytes: 44818565 num_examples: 1401 - name: other num_bytes: 186861175 num_examples: 5659 - name: validated num_bytes: 204446790 num_examples: 5996 - name: invalidated num_bytes: 6023769 num_examples: 185 download_size: 381570611 dataset_size: 576729866 - config_name: en features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 26088826658 num_examples: 564337 - name: test num_bytes: 758718688 num_examples: 16164 - name: validation num_bytes: 795638801 num_examples: 16164 - name: other num_bytes: 5796244022 num_examples: 169895 - name: validated num_bytes: 48425872575 num_examples: 1224864 - name: invalidated num_bytes: 9122973965 num_examples: 189562 download_size: 60613063630 dataset_size: 90988274709 - config_name: eo features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 993655930 num_examples: 19587 - name: test num_bytes: 420153812 num_examples: 8969 - name: validation num_bytes: 391427586 num_examples: 8987 - name: other num_bytes: 142476819 num_examples: 2946 - name: validated num_bytes: 2603249289 num_examples: 58094 - name: invalidated num_bytes: 238105462 num_examples: 4736 download_size: 2883560869 dataset_size: 4789068898 - config_name: es features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 6918333205 num_examples: 161813 - name: test num_bytes: 754049291 num_examples: 15089 - name: validation num_bytes: 735558084 num_examples: 15089 - name: other num_bytes: 5528972205 num_examples: 144791 - name: validated num_bytes: 9623788388 num_examples: 236314 - name: invalidated num_bytes: 1664876264 num_examples: 40640 download_size: 16188844718 dataset_size: 25225577437 - config_name: et features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 161124199 num_examples: 2966 - name: test num_bytes: 133183135 num_examples: 2509 - name: validation num_bytes: 137604813 num_examples: 2507 - name: other num_bytes: 30339130 num_examples: 569 - name: validated num_bytes: 573417188 num_examples: 10683 - name: invalidated num_bytes: 193019544 num_examples: 3557 download_size: 767174465 dataset_size: 1228688009 - config_name: eu features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - 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name: invalidated num_bytes: 499570226 num_examples: 11698 download_size: 8884585819 dataset_size: 10010522039 - config_name: fi features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 16017393 num_examples: 460 - name: test num_bytes: 16117529 num_examples: 428 - name: validation num_bytes: 15471757 num_examples: 415 - name: other num_bytes: 5836400 num_examples: 149 - name: validated num_bytes: 47669391 num_examples: 1305 - name: invalidated num_bytes: 2228215 num_examples: 59 download_size: 49882909 dataset_size: 103340685 - config_name: fr features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 12439892070 num_examples: 298982 - name: test num_bytes: 733943163 num_examples: 15763 - name: validation num_bytes: 703801114 num_examples: 15763 - name: other num_bytes: 117998889 num_examples: 3222 - name: validated num_bytes: 17921836252 num_examples: 461004 - name: invalidated num_bytes: 1794149368 num_examples: 40351 download_size: 19130141984 dataset_size: 33711620856 - config_name: fy-NL features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 159116360 num_examples: 3927 - name: test num_bytes: 126913262 num_examples: 3020 - name: validation num_bytes: 112288554 num_examples: 2790 - name: other num_bytes: 893887467 num_examples: 21569 - name: validated num_bytes: 429651922 num_examples: 10495 - name: invalidated num_bytes: 38985422 num_examples: 1031 download_size: 1237743070 dataset_size: 1760842987 - config_name: ga-IE features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 15396820 num_examples: 541 - name: test num_bytes: 16611739 num_examples: 506 - name: validation num_bytes: 14897739 num_examples: 497 - name: other num_bytes: 61948768 num_examples: 2130 - name: validated num_bytes: 93371649 num_examples: 3352 - name: invalidated num_bytes: 10993268 num_examples: 409 download_size: 156553447 dataset_size: 213219983 - config_name: hi features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 4860737 num_examples: 157 - name: test num_bytes: 4728043 num_examples: 127 - name: validation num_bytes: 5569352 num_examples: 135 - name: other num_bytes: 4176110 num_examples: 139 - name: validated num_bytes: 15158052 num_examples: 419 - name: invalidated num_bytes: 2801051 num_examples: 60 download_size: 21424045 dataset_size: 37293345 - config_name: hsb features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 43049910 num_examples: 808 - name: test num_bytes: 20929094 num_examples: 387 - name: validation num_bytes: 8769458 num_examples: 172 - name: other num_bytes: 3173841 num_examples: 62 - name: validated num_bytes: 72748422 num_examples: 1367 - name: invalidated num_bytes: 5589972 num_examples: 227 download_size: 79362060 dataset_size: 154260697 - config_name: hu features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 126163153 num_examples: 3348 - name: test num_bytes: 57056435 num_examples: 1649 - name: validation num_bytes: 50306925 num_examples: 1434 - name: other num_bytes: 12051094 num_examples: 295 - name: validated num_bytes: 234307671 num_examples: 6457 - name: invalidated num_bytes: 5881521 num_examples: 169 download_size: 242758708 dataset_size: 485766799 - config_name: ia features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 96577153 num_examples: 3477 - name: test num_bytes: 33204678 num_examples: 899 - name: validation num_bytes: 67436779 num_examples: 1601 - name: other num_bytes: 30937041 num_examples: 1095 - name: validated num_bytes: 197248304 num_examples: 5978 - name: invalidated num_bytes: 6769573 num_examples: 192 download_size: 226499645 dataset_size: 432173528 - config_name: id features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 63515863 num_examples: 2130 - name: test num_bytes: 60711104 num_examples: 1844 - name: validation num_bytes: 56963520 num_examples: 1835 - name: other num_bytes: 206578628 num_examples: 6782 - name: validated num_bytes: 272570942 num_examples: 8696 - name: invalidated num_bytes: 16566129 num_examples: 470 download_size: 475918233 dataset_size: 676906186 - config_name: it features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 2555546829 num_examples: 58015 - name: test num_bytes: 656285877 num_examples: 12928 - name: validation num_bytes: 621955330 num_examples: 12928 - name: other num_bytes: 671213467 num_examples: 14549 - name: validated num_bytes: 4552252754 num_examples: 102579 - name: invalidated num_bytes: 564610354 num_examples: 12189 download_size: 5585781573 dataset_size: 9621864611 - config_name: ja features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 27600264 num_examples: 722 - name: test num_bytes: 26475556 num_examples: 632 - name: validation num_bytes: 22098940 num_examples: 586 - name: other num_bytes: 34588931 num_examples: 885 - name: validated num_bytes: 106916400 num_examples: 3072 - name: invalidated num_bytes: 17819020 num_examples: 504 download_size: 152879796 dataset_size: 235499111 - config_name: ka features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 47790695 num_examples: 1058 - name: test num_bytes: 30301524 num_examples: 656 - name: validation num_bytes: 24951079 num_examples: 527 - name: other num_bytes: 2144603 num_examples: 44 - name: validated num_bytes: 104135978 num_examples: 2275 - name: invalidated num_bytes: 7004160 num_examples: 139 download_size: 104280554 dataset_size: 216328039 - config_name: kab features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 3219289101 num_examples: 120530 - name: test num_bytes: 446453041 num_examples: 14622 - name: validation num_bytes: 414159937 num_examples: 14622 - name: other num_bytes: 2282481767 num_examples: 88021 - name: validated num_bytes: 15310455176 num_examples: 573718 - name: invalidated num_bytes: 581587104 num_examples: 18134 download_size: 17171606918 dataset_size: 22254426126 - config_name: ky features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 75460488 num_examples: 1955 - name: test num_bytes: 57116561 num_examples: 1503 - name: validation num_bytes: 61393867 num_examples: 1511 - name: other num_bytes: 258081579 num_examples: 7223 - name: validated num_bytes: 355742823 num_examples: 9236 - name: invalidated num_bytes: 41007711 num_examples: 926 download_size: 579440853 dataset_size: 848803029 - config_name: lg features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 46910479 num_examples: 1250 - name: test num_bytes: 26951803 num_examples: 584 - name: validation num_bytes: 16709367 num_examples: 384 - name: other num_bytes: 111180838 num_examples: 3110 - name: validated num_bytes: 90606863 num_examples: 2220 - name: invalidated num_bytes: 14069959 num_examples: 290 download_size: 208197149 dataset_size: 306429309 - config_name: lt features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 34605356 num_examples: 931 - name: test num_bytes: 19940391 num_examples: 466 - name: validation num_bytes: 10462851 num_examples: 244 - name: other num_bytes: 71150206 num_examples: 1629 - name: validated num_bytes: 65138550 num_examples: 1644 - name: invalidated num_bytes: 4414780 num_examples: 102 download_size: 135299706 dataset_size: 205712134 - config_name: lv features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 67269173 num_examples: 2552 - name: test num_bytes: 56937435 num_examples: 1882 - name: validation num_bytes: 55289058 num_examples: 2002 - name: other num_bytes: 40259801 num_examples: 1560 - name: validated num_bytes: 179726893 num_examples: 6444 - name: invalidated num_bytes: 4383319 num_examples: 143 download_size: 208307691 dataset_size: 403865679 - config_name: mn features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 89913910 num_examples: 2183 - name: test num_bytes: 86737041 num_examples: 1862 - name: validation num_bytes: 82343275 num_examples: 1837 - name: other num_bytes: 146365394 num_examples: 3272 - name: validated num_bytes: 327264827 num_examples: 7487 - name: invalidated num_bytes: 31764232 num_examples: 667 download_size: 486369317 dataset_size: 764388679 - config_name: mt features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 73850815 num_examples: 2036 - name: test num_bytes: 66520195 num_examples: 1617 - name: validation num_bytes: 56412066 num_examples: 1516 - name: other num_bytes: 220666971 num_examples: 5714 - name: validated num_bytes: 218212969 num_examples: 5747 - name: invalidated num_bytes: 12328068 num_examples: 314 download_size: 425114242 dataset_size: 647991084 - config_name: nl features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 321946148 num_examples: 9460 - name: test num_bytes: 205287443 num_examples: 5708 - name: validation num_bytes: 186095353 num_examples: 4938 - name: other num_bytes: 801418 num_examples: 27 - name: validated num_bytes: 1710636990 num_examples: 52488 - name: invalidated num_bytes: 115133112 num_examples: 3308 download_size: 1741827548 dataset_size: 2539900464 - config_name: or features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 16067910 num_examples: 388 - name: test num_bytes: 4270651 num_examples: 98 - name: validation num_bytes: 5485937 num_examples: 129 - name: other num_bytes: 177775963 num_examples: 4302 - name: validated num_bytes: 25824418 num_examples: 615 - name: invalidated num_bytes: 2701922 num_examples: 62 download_size: 199077358 dataset_size: 232126801 - config_name: pa-IN features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 7572499 num_examples: 211 - name: test num_bytes: 4375532 num_examples: 116 - name: validation num_bytes: 1702492 num_examples: 44 - name: other num_bytes: 56683312 num_examples: 1411 - name: validated num_bytes: 13650443 num_examples: 371 - name: invalidated num_bytes: 1690766 num_examples: 43 download_size: 69748265 dataset_size: 85675044 - config_name: pl features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 273394509 num_examples: 7468 - name: test num_bytes: 205047541 num_examples: 5153 - name: validation num_bytes: 195917307 num_examples: 5153 - name: other num_bytes: 442144781 num_examples: 12848 - name: validated num_bytes: 3150860197 num_examples: 90791 - name: invalidated num_bytes: 180801918 num_examples: 4601 download_size: 3537012341 dataset_size: 4448166253 - config_name: pt features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 231451724 num_examples: 6514 - name: test num_bytes: 180108694 num_examples: 4641 - name: validation num_bytes: 165966139 num_examples: 4592 - name: other num_bytes: 283497435 num_examples: 8390 - name: validated num_bytes: 1480529669 num_examples: 41584 - name: invalidated num_bytes: 67948392 num_examples: 1740 download_size: 1704252567 dataset_size: 2409502053 - config_name: rm-sursilv features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 62396326 num_examples: 1384 - name: test num_bytes: 51707733 num_examples: 1194 - name: validation num_bytes: 52114252 num_examples: 1205 - name: other num_bytes: 93351293 num_examples: 2102 - name: validated num_bytes: 166218231 num_examples: 3783 - name: invalidated num_bytes: 30593270 num_examples: 639 download_size: 275950479 dataset_size: 456381105 - config_name: rm-vallader features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 29528457 num_examples: 574 - name: test num_bytes: 18805466 num_examples: 378 - name: validation num_bytes: 17012341 num_examples: 357 - name: other num_bytes: 36890435 num_examples: 727 - name: validated num_bytes: 65711922 num_examples: 1316 - name: invalidated num_bytes: 9356204 num_examples: 374 download_size: 108113989 dataset_size: 177304825 - config_name: ro features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 107235430 num_examples: 3399 - name: test num_bytes: 60106568 num_examples: 1778 - name: validation num_bytes: 30358457 num_examples: 858 - name: other num_bytes: 65805210 num_examples: 1945 - name: validated num_bytes: 197820619 num_examples: 6039 - name: invalidated num_bytes: 11108104 num_examples: 485 download_size: 261978702 dataset_size: 472434388 - config_name: ru features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 686168722 num_examples: 15481 - name: test num_bytes: 385349488 num_examples: 8007 - name: validation num_bytes: 361164462 num_examples: 7963 - name: other num_bytes: 450644862 num_examples: 10247 - name: validated num_bytes: 3212213931 num_examples: 74256 - name: invalidated num_bytes: 145739451 num_examples: 3056 download_size: 3655676916 dataset_size: 5241280916 - config_name: rw features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 21645788973 num_examples: 515197 - name: test num_bytes: 707959382 num_examples: 15724 - name: validation num_bytes: 698662384 num_examples: 15032 - name: other num_bytes: 923146896 num_examples: 22923 - name: validated num_bytes: 35011249432 num_examples: 832929 - name: invalidated num_bytes: 7969286423 num_examples: 206790 download_size: 42545189583 dataset_size: 66956093490 - config_name: sah features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 68286985 num_examples: 1442 - name: test num_bytes: 38534020 num_examples: 757 - name: validation num_bytes: 17900397 num_examples: 405 - name: other num_bytes: 62594222 num_examples: 1275 - name: validated num_bytes: 124800352 num_examples: 2606 - name: invalidated num_bytes: 3594160 num_examples: 66 download_size: 181245626 dataset_size: 315710136 - config_name: sl features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 66122967 num_examples: 2038 - name: test num_bytes: 26872195 num_examples: 881 - name: validation num_bytes: 16353097 num_examples: 556 - name: other num_bytes: 79268518 num_examples: 2502 - name: validated num_bytes: 148371273 num_examples: 4669 - name: invalidated num_bytes: 3048301 num_examples: 92 download_size: 222751292 dataset_size: 340036351 - config_name: sv-SE features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 62727263 num_examples: 2331 - name: test num_bytes: 59127381 num_examples: 2027 - name: validation num_bytes: 53846355 num_examples: 2019 - name: other num_bytes: 109970049 num_examples: 3043 - name: validated num_bytes: 327049001 num_examples: 12552 - name: invalidated num_bytes: 13462567 num_examples: 462 download_size: 421434184 dataset_size: 626182616 - config_name: ta features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 69052658 num_examples: 2009 - name: test num_bytes: 67616865 num_examples: 1781 - name: validation num_bytes: 63248009 num_examples: 1779 - name: other num_bytes: 246650792 num_examples: 7428 - name: validated num_bytes: 438961956 num_examples: 12652 - name: invalidated num_bytes: 23587453 num_examples: 594 download_size: 679766097 dataset_size: 909117733 - config_name: th features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 100435725 num_examples: 2917 - name: test num_bytes: 82030679 num_examples: 2188 - name: validation num_bytes: 63237632 num_examples: 1922 - name: other num_bytes: 95235301 num_examples: 2671 - name: validated num_bytes: 245734783 num_examples: 7028 - name: invalidated num_bytes: 18247080 num_examples: 467 download_size: 341305736 dataset_size: 604921200 - config_name: tr features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 57879052 num_examples: 1831 - name: test num_bytes: 60268059 num_examples: 1647 - name: validation num_bytes: 54914798 num_examples: 1647 - name: other num_bytes: 10954154 num_examples: 325 - name: validated num_bytes: 585777527 num_examples: 18685 - name: invalidated num_bytes: 59288266 num_examples: 1726 download_size: 620848700 dataset_size: 829081856 - config_name: tt features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 348132697 num_examples: 11211 - name: test num_bytes: 135120057 num_examples: 4485 - name: validation num_bytes: 61690964 num_examples: 2127 - name: other num_bytes: 62158038 num_examples: 1798 - name: validated num_bytes: 767791517 num_examples: 25781 - name: invalidated num_bytes: 10403128 num_examples: 287 download_size: 777153207 dataset_size: 1385296401 - config_name: uk features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 161925063 num_examples: 4035 - name: test num_bytes: 138422211 num_examples: 3235 - name: validation num_bytes: 135483169 num_examples: 3236 - name: other num_bytes: 327979131 num_examples: 8161 - name: validated num_bytes: 889863965 num_examples: 22337 - name: invalidated num_bytes: 55745301 num_examples: 1255 download_size: 1218559031 dataset_size: 1709418840 - config_name: vi features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 6244454 num_examples: 221 - name: test num_bytes: 6656365 num_examples: 198 - name: validation num_bytes: 6531856 num_examples: 200 - name: other num_bytes: 31315434 num_examples: 870 - name: validated num_bytes: 19432595 num_examples: 619 - name: invalidated num_bytes: 2981661 num_examples: 78 download_size: 51929480 dataset_size: 73162365 - config_name: vot features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 146467 num_examples: 3 - name: test - name: validation - name: other num_bytes: 7963322 num_examples: 411 - name: validated num_bytes: 146467 num_examples: 3 - name: invalidated num_bytes: 107949 num_examples: 6 download_size: 7792602 dataset_size: 8364205 - config_name: zh-CN features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 793667379 num_examples: 18541 - name: test num_bytes: 420202544 num_examples: 8760 - name: validation num_bytes: 396096323 num_examples: 8743 - name: other num_bytes: 381264783 num_examples: 8948 - name: validated num_bytes: 1618113625 num_examples: 36405 - name: invalidated num_bytes: 266234479 num_examples: 5305 download_size: 2184602350 dataset_size: 3875579133 - config_name: zh-HK features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 221459521 num_examples: 7506 - name: test num_bytes: 217627041 num_examples: 5172 - name: validation num_bytes: 196071110 num_examples: 5172 - name: other num_bytes: 1319233252 num_examples: 38830 - name: validated num_bytes: 1482087591 num_examples: 41835 - name: invalidated num_bytes: 124170969 num_examples: 2999 download_size: 2774145806 dataset_size: 3560649484 - config_name: zh-TW features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 97323787 num_examples: 3507 - name: test num_bytes: 85512325 num_examples: 2895 - name: validation num_bytes: 80402637 num_examples: 2895 - name: other num_bytes: 623801957 num_examples: 22477 - name: validated num_bytes: 1568842090 num_examples: 61232 - name: invalidated num_bytes: 100241443 num_examples: 3584 download_size: 2182836295 dataset_size: 2556124239 config_names: - ab - ar - as - br - ca - cnh - cs - cv - cy - de - dv - el - en - eo - es - et - eu - fa - fi - fr - fy-NL - ga-IE - hi - hsb - hu - ia - id - it - ja - ka - kab - ky - lg - lt - lv - mn - mt - nl - or - pa-IN - pl - pt - rm-sursilv - rm-vallader - ro - ru - rw - sah - sl - sv-SE - ta - th - tr - tt - uk - vi - vot - zh-CN - zh-HK - zh-TW --- # Dataset Card for common_voice <div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"> <p><b>Deprecated:</b> Dataset "common_voice" is deprecated and will soon be deleted. Use datasets under <a href="https://huggingface.co/mozilla-foundation">mozilla-foundation</a> organisation instead. For example, you can load <a href="https://huggingface.co/datasets/mozilla-foundation/common_voice_13_0">Common Voice 13</a> dataset via <code>load_dataset("mozilla-foundation/common_voice_13_0", "en")</code></p> </div> ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://commonvoice.mozilla.org/en/datasets - **Repository:** https://github.com/common-voice/common-voice - **Paper:** https://commonvoice.mozilla.org/en/datasets - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary The Common Voice dataset consists of a unique MP3 and corresponding text file. Many of the 9,283 recorded hours in the dataset also include demographic metadata like age, sex, and accent that can help train the accuracy of speech recognition engines. The dataset currently consists of 7,335 validated hours in 60 languages, but were always adding more voices and languages. Take a look at our Languages page to request a language or start contributing. ### Supported Tasks and Leaderboards [Needs More Information] ### Languages English ## Dataset Structure ### Data Instances A typical data point comprises the path to the audio file, called path and its sentence. Additional fields include accent, age, client_id, up_votes down_votes, gender, locale and segment. ` {'accent': 'netherlands', 'age': 'fourties', 'client_id': 'bbbcb732e0f422150c30ff3654bbab572e2a617da107bca22ff8b89ab2e4f124d03b6a92c48322862f60bd0179ae07baf0f9b4f9c4e11d581e0cec70f703ba54', 'down_votes': 0, 'gender': 'male', 'locale': 'nl', 'path': 'nl/clips/common_voice_nl_23522441.mp3', 'segment': "''", 'sentence': 'Ik vind dat een dubieuze procedure.', 'up_votes': 2, 'audio': {'path': `nl/clips/common_voice_nl_23522441.mp3', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 48000} ` ### Data Fields client_id: An id for which client (voice) made the recording path: The path to the audio file audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`. sentence: The sentence the user was prompted to speak up_votes: How many upvotes the audio file has received from reviewers down_votes: How many downvotes the audio file has received from reviewers age: The age of the speaker. gender: The gender of the speaker accent: Accent of the speaker locale: The locale of the speaker segment: Usually empty field ### Data Splits The speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other. The validated data is data that has been validated with reviewers and recieved upvotes that the data is of high quality. The invalidated data is data has been invalidated by reviewers and recieved downvotes that the data is of low quality. The reported data is data that has been reported, for different reasons. The other data is data that has not yet been reviewed. The dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train. ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset. ## Considerations for Using the Data ### Social Impact of Dataset The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset. ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Public Domain, [CC-0](https://creativecommons.org/share-your-work/public-domain/cc0/) ### Citation Information ``` @inproceedings{commonvoice:2020, author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.}, title = {Common Voice: A Massively-Multilingual Speech Corpus}, booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)}, pages = {4211--4215}, year = 2020 } ``` ### Contributions Thanks to [@BirgerMoell](https://github.com/BirgerMoell) for adding this dataset.

Dahoas/rm-static

Dahoas

--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 113850006 num_examples: 76256 - name: test num_bytes: 7649255 num_examples: 5103 download_size: 73006535 dataset_size: 121499261 --- # Dataset Card for "rm-static" Split of [hh-static](https://huggingface.co/datasets/Dahoas/static-hh) used for training reward models after supervised fine-tuning.

naver-clova-ix/cord-v2

naver-clova-ix

--- license: cc-by-4.0 ---

multi_nli

--- annotations_creators: - crowdsourced language_creators: - crowdsourced - found language: - en license: - cc-by-3.0 - cc-by-sa-3.0 - mit - other multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - natural-language-inference - multi-input-text-classification paperswithcode_id: multinli pretty_name: Multi-Genre Natural Language Inference license_details: Open Portion of the American National Corpus dataset_info: features: - name: promptID dtype: int32 - name: pairID dtype: string - name: premise dtype: string - name: premise_binary_parse dtype: string - name: premise_parse dtype: string - name: hypothesis dtype: string - name: hypothesis_binary_parse dtype: string - name: hypothesis_parse dtype: string - name: genre dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: train num_bytes: 410211586 num_examples: 392702 - name: validation_matched num_bytes: 10063939 num_examples: 9815 - name: validation_mismatched num_bytes: 10610221 num_examples: 9832 download_size: 226850426 dataset_size: 430885746 --- # Dataset Card for Multi-Genre Natural Language Inference (MultiNLI) ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://www.nyu.edu/projects/bowman/multinli/](https://www.nyu.edu/projects/bowman/multinli/) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 226.85 MB - **Size of the generated dataset:** 76.95 MB - **Total amount of disk used:** 303.81 MB ### Dataset Summary The Multi-Genre Natural Language Inference (MultiNLI) corpus is a crowd-sourced collection of 433k sentence pairs annotated with textual entailment information. The corpus is modeled on the SNLI corpus, but differs in that covers a range of genres of spoken and written text, and supports a distinctive cross-genre generalization evaluation. The corpus served as the basis for the shared task of the RepEval 2017 Workshop at EMNLP in Copenhagen. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages The dataset contains samples in English only. ## Dataset Structure ### Data Instances - **Size of downloaded dataset files:** 226.85 MB - **Size of the generated dataset:** 76.95 MB - **Total amount of disk used:** 303.81 MB Example of a data instance: ``` { "promptID": 31193, "pairID": "31193n", "premise": "Conceptually cream skimming has two basic dimensions - product and geography.", "premise_binary_parse": "( ( Conceptually ( cream skimming ) ) ( ( has ( ( ( two ( basic dimensions ) ) - ) ( ( product and ) geography ) ) ) . ) )", "premise_parse": "(ROOT (S (NP (JJ Conceptually) (NN cream) (NN skimming)) (VP (VBZ has) (NP (NP (CD two) (JJ basic) (NNS dimensions)) (: -) (NP (NN product) (CC and) (NN geography)))) (. .)))", "hypothesis": "Product and geography are what make cream skimming work. ", "hypothesis_binary_parse": "( ( ( Product and ) geography ) ( ( are ( what ( make ( cream ( skimming work ) ) ) ) ) . ) )", "hypothesis_parse": "(ROOT (S (NP (NN Product) (CC and) (NN geography)) (VP (VBP are) (SBAR (WHNP (WP what)) (S (VP (VBP make) (NP (NP (NN cream)) (VP (VBG skimming) (NP (NN work)))))))) (. .)))", "genre": "government", "label": 1 } ``` ### Data Fields The data fields are the same among all splits. - `promptID`: Unique identifier for prompt - `pairID`: Unique identifier for pair - `{premise,hypothesis}`: combination of `premise` and `hypothesis` - `{premise,hypothesis} parse`: Each sentence as parsed by the Stanford PCFG Parser 3.5.2 - `{premise,hypothesis} binary parse`: parses in unlabeled binary-branching format - `genre`: a `string` feature. - `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2). Dataset instances which don't have any gold label are marked with -1 label. Make sure you filter them before starting the training using `datasets.Dataset.filter`. ### Data Splits |train |validation_matched|validation_mismatched| |-----:|-----------------:|--------------------:| |392702| 9815| 9832| ## Dataset Creation ### Curation Rationale They constructed MultiNLI so as to make it possible to explicitly evaluate models both on the quality of their sentence representations within the training domain and on their ability to derive reasonable representations in unfamiliar domains. ### Source Data #### Initial Data Collection and Normalization They created each sentence pair by selecting a premise sentence from a preexisting text source and asked a human annotator to compose a novel sentence to pair with it as a hypothesis. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information The majority of the corpus is released under the OANC’s license, which allows all content to be freely used, modified, and shared under permissive terms. The data in the FICTION section falls under several permissive licenses; Seven Swords is available under a Creative Commons Share-Alike 3.0 Unported License, and with the explicit permission of the author, Living History and Password Incorrect are available under Creative Commons Attribution 3.0 Unported Licenses; the remaining works of fiction are in the public domain in the United States (but may be licensed differently elsewhere). ### Citation Information ``` @InProceedings{N18-1101, author = "Williams, Adina and Nangia, Nikita and Bowman, Samuel", title = "A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference", booktitle = "Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)", year = "2018", publisher = "Association for Computational Linguistics", pages = "1112--1122", location = "New Orleans, Louisiana", url = "http://aclweb.org/anthology/N18-1101" } ``` ### Contributions Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset.

hate_speech18

--- annotations_creators: - found language_creators: - found language: - en license: - cc-by-sa-3.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - intent-classification paperswithcode_id: hate-speech pretty_name: Hate Speech dataset_info: features: - name: text dtype: string - name: user_id dtype: int64 - name: subforum_id dtype: int64 - name: num_contexts dtype: int64 - name: label dtype: class_label: names: '0': noHate '1': hate '2': idk/skip '3': relation splits: - name: train num_bytes: 1375340 num_examples: 10944 download_size: 3664530 dataset_size: 1375340 train-eval-index: - config: default task: text-classification task_id: multi_class_classification splits: train_split: train col_mapping: text: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/Vicomtech/hate-speech-dataset - **Repository:** https://github.com/Vicomtech/hate-speech-dataset - **Paper:** https://www.aclweb.org/anthology/W18-51.pdf - **Leaderboard:** - **Point of Contact:** ### Dataset Summary These files contain text extracted from Stormfront, a white supremacist forum. A random set of forums posts have been sampled from several subforums and split into sentences. Those sentences have been manually labelled as containing hate speech or not, according to certain annotation guidelines. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - text: the provided sentence - user_id: information to make it possible to re-build the conversations these sentences belong to - subforum_id: information to make it possible to re-build the conversations these sentences belong to - num_contexts: number of previous posts the annotator had to read before making a decision over the category of the sentence - label: hate, noHate, relation (sentence in the post doesn't contain hate speech on their own, but combination of serveral sentences does) or idk/skip (sentences that are not written in English or that don't contain information as to be classified into hate or noHate) ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{gibert2018hate, title = "{Hate Speech Dataset from a White Supremacy Forum}", author = "de Gibert, Ona and Perez, Naiara and Garc{\'\i}a-Pablos, Aitor and Cuadros, Montse", booktitle = "Proceedings of the 2nd Workshop on Abusive Language Online ({ALW}2)", month = oct, year = "2018", address = "Brussels, Belgium", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/W18-5102", doi = "10.18653/v1/W18-5102", pages = "11--20", } ``` ### Contributions Thanks to [@czabo](https://github.com/czabo) for adding this dataset.

tapaco

--- annotations_creators: - machine-generated language_creators: - crowdsourced language: - af - ar - az - be - ber - bg - bn - br - ca - cbk - cmn - cs - da - de - el - en - eo - es - et - eu - fi - fr - gl - gos - he - hi - hr - hu - hy - ia - id - ie - io - is - it - ja - jbo - kab - ko - kw - la - lfn - lt - mk - mr - nb - nds - nl - orv - ota - pes - pl - pt - rn - ro - ru - sl - sr - sv - tk - tl - tlh - tok - tr - tt - ug - uk - ur - vi - vo - war - wuu - yue license: - cc-by-2.0 multilinguality: - multilingual size_categories: - 100K<n<1M - 10K<n<100K - 1K<n<10K - 1M<n<10M - n<1K source_datasets: - extended|other-tatoeba task_categories: - text2text-generation - translation - text-classification task_ids: - semantic-similarity-classification paperswithcode_id: tapaco pretty_name: TaPaCo Corpus tags: - paraphrase-generation dataset_info: - config_name: all_languages features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 162802556 num_examples: 1926192 download_size: 32213126 dataset_size: 162802556 - config_name: af features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 21219 num_examples: 307 download_size: 32213126 dataset_size: 21219 - config_name: ar features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 546200 num_examples: 6446 download_size: 32213126 dataset_size: 546200 - config_name: az features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 44461 num_examples: 624 download_size: 32213126 dataset_size: 44461 - config_name: be features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 140376 num_examples: 1512 download_size: 32213126 dataset_size: 140376 - config_name: ber features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 5118620 num_examples: 67484 download_size: 32213126 dataset_size: 5118620 - config_name: bg features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 590535 num_examples: 6324 download_size: 32213126 dataset_size: 590535 - config_name: bn features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 146654 num_examples: 1440 download_size: 32213126 dataset_size: 146654 - config_name: br features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 177919 num_examples: 2536 download_size: 32213126 dataset_size: 177919 - config_name: ca features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 39404 num_examples: 518 download_size: 32213126 dataset_size: 39404 - config_name: cbk features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 19404 num_examples: 262 download_size: 32213126 dataset_size: 19404 - config_name: cmn features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 964514 num_examples: 12549 download_size: 32213126 dataset_size: 964514 - config_name: cs features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 482292 num_examples: 6659 download_size: 32213126 dataset_size: 482292 - config_name: da features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 848886 num_examples: 11220 download_size: 32213126 dataset_size: 848886 - config_name: de features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 10593377 num_examples: 125091 download_size: 32213126 dataset_size: 10593377 - config_name: el features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 926054 num_examples: 10072 download_size: 32213126 dataset_size: 926054 - config_name: en features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 15070349 num_examples: 158053 download_size: 32213126 dataset_size: 15070349 - config_name: eo features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 16810965 num_examples: 207105 download_size: 32213126 dataset_size: 16810965 - config_name: es features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 6851135 num_examples: 85064 download_size: 32213126 dataset_size: 6851135 - config_name: et features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 17127 num_examples: 241 download_size: 32213126 dataset_size: 17127 - config_name: eu features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 42702 num_examples: 573 download_size: 32213126 dataset_size: 42702 - config_name: fi features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 2520167 num_examples: 31753 download_size: 32213126 dataset_size: 2520167 - config_name: fr features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 9481426 num_examples: 116733 download_size: 32213126 dataset_size: 9481426 - config_name: gl features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 26551 num_examples: 351 download_size: 32213126 dataset_size: 26551 - config_name: gos features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 18442 num_examples: 279 download_size: 32213126 dataset_size: 18442 - config_name: he features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 6024345 num_examples: 68350 download_size: 32213126 dataset_size: 6024345 - config_name: hi features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 209382 num_examples: 1913 download_size: 32213126 dataset_size: 209382 - config_name: hr features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 36638 num_examples: 505 download_size: 32213126 dataset_size: 36638 - config_name: hu features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 5289610 num_examples: 67964 download_size: 32213126 dataset_size: 5289610 - config_name: hy features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 49230 num_examples: 603 download_size: 32213126 dataset_size: 49230 - config_name: ia features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 194035 num_examples: 2548 download_size: 32213126 dataset_size: 194035 - config_name: id features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 124568 num_examples: 1602 download_size: 32213126 dataset_size: 124568 - config_name: ie features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 31956 num_examples: 488 download_size: 32213126 dataset_size: 31956 - config_name: io features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 33892 num_examples: 480 download_size: 32213126 dataset_size: 33892 - config_name: is features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 132062 num_examples: 1641 download_size: 32213126 dataset_size: 132062 - config_name: it features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 15073750 num_examples: 198919 download_size: 32213126 dataset_size: 15073750 - config_name: ja features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 4314423 num_examples: 44267 download_size: 32213126 dataset_size: 4314423 - config_name: jbo features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 201564 num_examples: 2704 download_size: 32213126 dataset_size: 201564 - config_name: kab features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 1211051 num_examples: 15944 download_size: 32213126 dataset_size: 1211051 - config_name: ko features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 40458 num_examples: 503 download_size: 32213126 dataset_size: 40458 - config_name: kw features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 88577 num_examples: 1328 download_size: 32213126 dataset_size: 88577 - config_name: la features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 485749 num_examples: 6889 download_size: 32213126 dataset_size: 485749 - config_name: lfn features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 203383 num_examples: 2313 download_size: 32213126 dataset_size: 203383 - config_name: lt features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 599166 num_examples: 8042 download_size: 32213126 dataset_size: 599166 - config_name: mk features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 1240185 num_examples: 14678 download_size: 32213126 dataset_size: 1240185 - config_name: mr features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 1838921 num_examples: 16413 download_size: 32213126 dataset_size: 1838921 - config_name: nb features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 85371 num_examples: 1094 download_size: 32213126 dataset_size: 85371 - config_name: nds features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 195021 num_examples: 2633 download_size: 32213126 dataset_size: 195021 - config_name: nl features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 1790975 num_examples: 23561 download_size: 32213126 dataset_size: 1790975 - config_name: orv features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 40484 num_examples: 471 download_size: 32213126 dataset_size: 40484 - config_name: ota features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 44996 num_examples: 486 download_size: 32213126 dataset_size: 44996 - config_name: pes features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 433406 num_examples: 4285 download_size: 32213126 dataset_size: 433406 - config_name: pl features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 1722188 num_examples: 22391 download_size: 32213126 dataset_size: 1722188 - config_name: pt features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 6141178 num_examples: 78430 download_size: 32213126 dataset_size: 6141178 - config_name: rn features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 47387 num_examples: 648 download_size: 32213126 dataset_size: 47387 - config_name: ro features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 162955 num_examples: 2092 download_size: 32213126 dataset_size: 162955 - config_name: ru features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 24540667 num_examples: 251263 download_size: 32213126 dataset_size: 24540667 - config_name: sl features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 49610 num_examples: 706 download_size: 32213126 dataset_size: 49610 - config_name: sr features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 667308 num_examples: 8175 download_size: 32213126 dataset_size: 667308 - config_name: sv features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 509884 num_examples: 7005 download_size: 32213126 dataset_size: 509884 - config_name: tk features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 95047 num_examples: 1165 download_size: 32213126 dataset_size: 95047 - config_name: tl features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 76059 num_examples: 1017 download_size: 32213126 dataset_size: 76059 - config_name: tlh features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 185309 num_examples: 2804 download_size: 32213126 dataset_size: 185309 - config_name: toki features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 310864 num_examples: 3738 download_size: 32213126 dataset_size: 310864 - config_name: tr features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 11271158 num_examples: 142088 download_size: 32213126 dataset_size: 11271158 - config_name: tt features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 277269 num_examples: 2398 download_size: 32213126 dataset_size: 277269 - config_name: ug features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 118474 num_examples: 1183 download_size: 32213126 dataset_size: 118474 - config_name: uk features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 4885677 num_examples: 54431 download_size: 32213126 dataset_size: 4885677 - config_name: ur features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 24075 num_examples: 252 download_size: 32213126 dataset_size: 24075 - config_name: vi features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 84773 num_examples: 962 download_size: 32213126 dataset_size: 84773 - config_name: vo features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 22164 num_examples: 328 download_size: 32213126 dataset_size: 22164 - config_name: war features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 25759 num_examples: 327 download_size: 32213126 dataset_size: 25759 - config_name: wuu features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 31640 num_examples: 408 download_size: 32213126 dataset_size: 31640 - config_name: yue features: - name: paraphrase_set_id dtype: string - name: sentence_id dtype: string - name: paraphrase dtype: string - name: lists sequence: string - name: tags sequence: string - name: language dtype: string splits: - name: train num_bytes: 42766 num_examples: 561 download_size: 32213126 dataset_size: 42766 config_names: - af - all_languages - ar - az - be - ber - bg - bn - br - ca - cbk - cmn - cs - da - de - el - en - eo - es - et - eu - fi - fr - gl - gos - he - hi - hr - hu - hy - ia - id - ie - io - is - it - ja - jbo - kab - ko - kw - la - lfn - lt - mk - mr - nb - nds - nl - orv - ota - pes - pl - pt - rn - ro - ru - sl - sr - sv - tk - tl - tlh - tok - tr - tt - ug - uk - ur - vi - vo - war - wuu - yue --- # Dataset Card for TaPaCo Corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [TaPaCo: A Corpus of Sentential Paraphrases for 73 Languages](https://zenodo.org/record/3707949#.X9Dh0cYza3I) - **Paper:** [TaPaCo: A Corpus of Sentential Paraphrases for 73 Languages](https://www.aclweb.org/anthology/2020.lrec-1.848.pdf) - **Data:** https://doi.org/10.5281/zenodo.3707949 - **Point of Contact:** [Yves Scherrer](https://blogs.helsinki.fi/yvesscherrer/) ### Dataset Summary A freely available paraphrase corpus for 73 languages extracted from the Tatoeba database. Tatoeba is a crowdsourcing project mainly geared towards language learners. Its aim is to provide example sentences and translations for particular linguistic constructions and words. The paraphrase corpus is created by populating a graph with Tatoeba sentences and equivalence links between sentences “meaning the same thing”. This graph is then traversed to extract sets of paraphrases. Several language-independent filters and pruning steps are applied to remove uninteresting sentences. A manual evaluation performed on three languages shows that between half and three quarters of inferred paraphrases are correct and that most remaining ones are either correct but trivial, or near-paraphrases that neutralize a morphological distinction. The corpus contains a total of 1.9 million sentences, with 200 – 250 000 sentences per language. It covers a range of languages for which, to our knowledge, no other paraphrase dataset exists. ### Supported Tasks and Leaderboards Paraphrase detection and generation have become popular tasks in NLP and are increasingly integrated into a wide variety of common downstream tasks such as machine translation , information retrieval, question answering, and semantic parsing. Most of the existing datasets cover only a single language – in most cases English – or a small number of languages. Furthermore, some paraphrase datasets focus on lexical and phrasal rather than sentential paraphrases, while others are created (semi -)automatically using machine translation. The number of sentences per language ranges from 200 to 250 000, which makes the dataset more suitable for fine-tuning and evaluation purposes than for training. It is well-suited for multi-reference evaluation of paraphrase generation models, as there is generally not a single correct way of paraphrasing a given input sentence. ### Languages The dataset contains paraphrases in Afrikaans, Arabic, Azerbaijani, Belarusian, Berber languages, Bulgarian, Bengali , Breton, Catalan; Valencian, Chavacano, Mandarin, Czech, Danish, German, Greek, Modern (1453-), English, Esperanto , Spanish; Castilian, Estonian, Basque, Finnish, French, Galician, Gronings, Hebrew, Hindi, Croatian, Hungarian , Armenian, Interlingua (International Auxiliary Language Association), Indonesian, Interlingue; Occidental, Ido , Icelandic, Italian, Japanese, Lojban, Kabyle, Korean, Cornish, Latin, Lingua Franca Nova\t, Lithuanian, Macedonian , Marathi, Bokmål, Norwegian; Norwegian Bokmål, Low German; Low Saxon; German, Low; Saxon, Low, Dutch; Flemish, ]Old Russian, Turkish, Ottoman (1500-1928), Iranian Persian, Polish, Portuguese, Rundi, Romanian; Moldavian; Moldovan, Russian, Slovenian, Serbian, Swedish, Turkmen, Tagalog, Klingon; tlhIngan-Hol, Toki Pona, Turkish, Tatar, Uighur; Uyghur, Ukrainian, Urdu, Vietnamese, Volapük, Waray, Wu Chinese and Yue Chinese ## Dataset Structure ### Data Instances Each data instance corresponds to a paraphrase, e.g.: ``` { 'paraphrase_set_id': '1483', 'sentence_id': '5778896', 'paraphrase': 'Ɣremt adlis-a.', 'lists': ['7546'], 'tags': [''], 'language': 'ber' } ``` ### Data Fields Each dialogue instance has the following fields: - `paraphrase_set_id`: a running number that groups together all sentences that are considered paraphrases of each other - `sentence_id`: OPUS sentence id - `paraphrase`: Sentential paraphrase in a given language for a given paraphrase_set_id - `lists`: Contributors can add sentences to list in order to specify the original source of the data - `tags`: Indicates morphological or phonological properties of the sentence when available - `language`: Language identifier, one of the 73 languages that belong to this dataset. ### Data Splits The dataset is having a single `train` split, contains a total of 1.9 million sentences, with 200 – 250 000 sentences per language ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Creative Commons Attribution 2.0 Generic ### Citation Information ``` @dataset{scherrer_yves_2020_3707949, author = {Scherrer, Yves}, title = {{TaPaCo: A Corpus of Sentential Paraphrases for 73 Languages}}, month = mar, year = 2020, publisher = {Zenodo}, version = {1.0}, doi = {10.5281/zenodo.3707949}, url = {https://doi.org/10.5281/zenodo.3707949} } ``` ### Contributions Thanks to [@pacman100](https://github.com/pacman100) for adding this dataset.

cifar100

--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended|other-80-Million-Tiny-Images task_categories: - image-classification task_ids: [] paperswithcode_id: cifar-100 pretty_name: Cifar100 dataset_info: features: - name: img dtype: image - name: fine_label dtype: class_label: names: '0': apple '1': aquarium_fish '2': baby '3': bear '4': beaver '5': bed '6': bee '7': beetle '8': bicycle '9': bottle '10': bowl '11': boy '12': bridge '13': bus '14': butterfly '15': camel '16': can '17': castle '18': caterpillar '19': cattle '20': chair '21': chimpanzee '22': clock '23': cloud '24': cockroach '25': couch '26': cra '27': crocodile '28': cup '29': dinosaur '30': dolphin '31': elephant '32': flatfish '33': forest '34': fox '35': girl '36': hamster '37': house '38': kangaroo '39': keyboard '40': lamp '41': lawn_mower '42': leopard '43': lion '44': lizard '45': lobster '46': man '47': maple_tree '48': motorcycle '49': mountain '50': mouse '51': mushroom '52': oak_tree '53': orange '54': orchid '55': otter '56': palm_tree '57': pear '58': pickup_truck '59': pine_tree '60': plain '61': plate '62': poppy '63': porcupine '64': possum '65': rabbit '66': raccoon '67': ray '68': road '69': rocket '70': rose '71': sea '72': seal '73': shark '74': shrew '75': skunk '76': skyscraper '77': snail '78': snake '79': spider '80': squirrel '81': streetcar '82': sunflower '83': sweet_pepper '84': table '85': tank '86': telephone '87': television '88': tiger '89': tractor '90': train '91': trout '92': tulip '93': turtle '94': wardrobe '95': whale '96': willow_tree '97': wolf '98': woman '99': worm - name: coarse_label dtype: class_label: names: '0': aquatic_mammals '1': fish '2': flowers '3': food_containers '4': fruit_and_vegetables '5': household_electrical_devices '6': household_furniture '7': insects '8': large_carnivores '9': large_man-made_outdoor_things '10': large_natural_outdoor_scenes '11': large_omnivores_and_herbivores '12': medium_mammals '13': non-insect_invertebrates '14': people '15': reptiles '16': small_mammals '17': trees '18': vehicles_1 '19': vehicles_2 config_name: cifar100 splits: - name: train num_bytes: 112751396 num_examples: 50000 - name: test num_bytes: 22605519 num_examples: 10000 download_size: 169001437 dataset_size: 135356915 --- # Dataset Card for CIFAR-100 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [CIFAR Datasets](https://www.cs.toronto.edu/~kriz/cifar.html) - **Repository:** - **Paper:** [Paper](https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The CIFAR-100 dataset consists of 60000 32x32 colour images in 100 classes, with 600 images per class. There are 500 training images and 100 testing images per class. There are 50000 training images and 10000 test images. The 100 classes are grouped into 20 superclasses. There are two labels per image - fine label (actual class) and coarse label (superclass). ### Supported Tasks and Leaderboards - `image-classification`: The goal of this task is to classify a given image into one of 100 classes. The leaderboard is available [here](https://paperswithcode.com/sota/image-classification-on-cifar-100). ### Languages English ## Dataset Structure ### Data Instances A sample from the training set is provided below: ``` { 'img': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32 at 0x2767F58E080>, 'fine_label': 19, 'coarse_label': 11 } ``` ### Data Fields - `img`: A `PIL.Image.Image` object containing the 32x32 image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]` - `fine_label`: an `int` classification label with the following mapping: `0`: apple `1`: aquarium_fish `2`: baby `3`: bear `4`: beaver `5`: bed `6`: bee `7`: beetle `8`: bicycle `9`: bottle `10`: bowl `11`: boy `12`: bridge `13`: bus `14`: butterfly `15`: camel `16`: can `17`: castle `18`: caterpillar `19`: cattle `20`: chair `21`: chimpanzee `22`: clock `23`: cloud `24`: cockroach `25`: couch `26`: cra `27`: crocodile `28`: cup `29`: dinosaur `30`: dolphin `31`: elephant `32`: flatfish `33`: forest `34`: fox `35`: girl `36`: hamster `37`: house `38`: kangaroo `39`: keyboard `40`: lamp `41`: lawn_mower `42`: leopard `43`: lion `44`: lizard `45`: lobster `46`: man `47`: maple_tree `48`: motorcycle `49`: mountain `50`: mouse `51`: mushroom `52`: oak_tree `53`: orange `54`: orchid `55`: otter `56`: palm_tree `57`: pear `58`: pickup_truck `59`: pine_tree `60`: plain `61`: plate `62`: poppy `63`: porcupine `64`: possum `65`: rabbit `66`: raccoon `67`: ray `68`: road `69`: rocket `70`: rose `71`: sea `72`: seal `73`: shark `74`: shrew `75`: skunk `76`: skyscraper `77`: snail `78`: snake `79`: spider `80`: squirrel `81`: streetcar `82`: sunflower `83`: sweet_pepper `84`: table `85`: tank `86`: telephone `87`: television `88`: tiger `89`: tractor `90`: train `91`: trout `92`: tulip `93`: turtle `94`: wardrobe `95`: whale `96`: willow_tree `97`: wolf `98`: woman `99`: worm - `coarse_label`: an `int` coarse classification label with following mapping: `0`: aquatic_mammals `1`: fish `2`: flowers `3`: food_containers `4`: fruit_and_vegetables `5`: household_electrical_devices `6`: household_furniture `7`: insects `8`: large_carnivores `9`: large_man-made_outdoor_things `10`: large_natural_outdoor_scenes `11`: large_omnivores_and_herbivores `12`: medium_mammals `13`: non-insect_invertebrates `14`: people `15`: reptiles `16`: small_mammals `17`: trees `18`: vehicles_1 `19`: vehicles_2 ### Data Splits | name |train|test| |----------|----:|---------:| |cifar100|50000| 10000| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @TECHREPORT{Krizhevsky09learningmultiple, author = {Alex Krizhevsky}, title = {Learning multiple layers of features from tiny images}, institution = {}, year = {2009} } ``` ### Contributions Thanks to [@gchhablani](https://github.com/gchablani) for adding this dataset.

news_commentary

--- annotations_creators: - found language_creators: - found language: - ar - cs - de - en - es - fr - it - ja - nl - pt - ru - zh license: - unknown multilinguality: - multilingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: null pretty_name: NewsCommentary dataset_info: - config_name: ar-cs features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - cs splits: - name: train num_bytes: 51546460 num_examples: 52128 download_size: 16242918 dataset_size: 51546460 - config_name: ar-de features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - de splits: - name: train num_bytes: 69681419 num_examples: 68916 download_size: 21446768 dataset_size: 69681419 - config_name: cs-de features: - name: id dtype: string - name: translation dtype: translation: languages: - cs - de splits: - name: train num_bytes: 57470799 num_examples: 172706 download_size: 21623462 dataset_size: 57470799 - config_name: ar-en features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - en splits: - name: train num_bytes: 80655273 num_examples: 83187 download_size: 24714354 dataset_size: 80655273 - config_name: cs-en features: - name: id dtype: string - name: translation dtype: translation: languages: - cs - en splits: - name: train num_bytes: 54487874 num_examples: 177278 download_size: 20636368 dataset_size: 54487874 - config_name: de-en features: - name: id dtype: string - name: translation dtype: translation: languages: - de - en splits: - name: train num_bytes: 73085451 num_examples: 223153 download_size: 26694093 dataset_size: 73085451 - config_name: ar-es features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - es splits: - name: train num_bytes: 79255985 num_examples: 78074 download_size: 24027435 dataset_size: 79255985 - config_name: cs-es features: - name: id dtype: string - name: translation dtype: translation: languages: - cs - es splits: - name: train num_bytes: 56794825 num_examples: 170489 download_size: 20994380 dataset_size: 56794825 - config_name: de-es features: - name: id dtype: string - name: translation dtype: translation: languages: - de - es splits: - name: train num_bytes: 74708740 num_examples: 209839 download_size: 26653320 dataset_size: 74708740 - config_name: en-es features: - name: id dtype: string - name: translation dtype: translation: languages: - en - es splits: - name: train num_bytes: 78600789 num_examples: 238872 download_size: 28106064 dataset_size: 78600789 - config_name: ar-fr features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - fr splits: - name: train num_bytes: 71035061 num_examples: 69157 download_size: 21465481 dataset_size: 71035061 - config_name: cs-fr features: - name: id dtype: string - name: translation dtype: translation: languages: - cs - fr splits: - name: train num_bytes: 50364837 num_examples: 148578 download_size: 18483528 dataset_size: 50364837 - config_name: de-fr features: - name: id dtype: string - name: translation dtype: translation: languages: - de - fr splits: - name: train num_bytes: 67083899 num_examples: 185442 download_size: 23779967 dataset_size: 67083899 - config_name: en-fr features: - name: id dtype: string - name: translation dtype: translation: languages: - en - fr splits: - name: train num_bytes: 70340014 num_examples: 209479 download_size: 24982452 dataset_size: 70340014 - config_name: es-fr features: - name: id dtype: string - name: translation dtype: translation: languages: - es - fr splits: - name: train num_bytes: 71025933 num_examples: 195241 download_size: 24693126 dataset_size: 71025933 - config_name: ar-it features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - it splits: - name: train num_bytes: 17413450 num_examples: 17227 download_size: 5186438 dataset_size: 17413450 - config_name: cs-it features: - name: id dtype: string - name: translation dtype: translation: languages: - cs - it splits: - name: train num_bytes: 10441845 num_examples: 30547 download_size: 3813656 dataset_size: 10441845 - config_name: de-it features: - name: id dtype: string - name: translation dtype: translation: languages: - de - it splits: - name: train num_bytes: 13993454 num_examples: 38961 download_size: 4933419 dataset_size: 13993454 - config_name: en-it features: - name: id dtype: string - name: translation dtype: translation: languages: - en - it splits: - name: train num_bytes: 14213972 num_examples: 40009 download_size: 4960768 dataset_size: 14213972 - config_name: es-it features: - name: id dtype: string - name: translation dtype: translation: languages: - es - it splits: - name: train num_bytes: 15139636 num_examples: 41497 download_size: 5215173 dataset_size: 15139636 - config_name: fr-it features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - it splits: - name: train num_bytes: 14216079 num_examples: 38485 download_size: 4867267 dataset_size: 14216079 - config_name: ar-ja features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - ja splits: - name: train num_bytes: 661992 num_examples: 569 download_size: 206664 dataset_size: 661992 - config_name: cs-ja features: - name: id dtype: string - name: translation dtype: translation: languages: - cs - ja splits: - name: train num_bytes: 487902 num_examples: 622 download_size: 184374 dataset_size: 487902 - config_name: de-ja features: - name: id dtype: string - name: translation dtype: translation: languages: - de - ja splits: - name: train num_bytes: 465575 num_examples: 582 download_size: 171371 dataset_size: 465575 - config_name: en-ja features: - name: id dtype: string - name: translation dtype: translation: languages: - en - ja splits: - name: train num_bytes: 485484 num_examples: 637 download_size: 178451 dataset_size: 485484 - config_name: es-ja features: - name: id dtype: string - name: translation dtype: translation: languages: - es - ja splits: - name: train num_bytes: 484463 num_examples: 602 download_size: 175281 dataset_size: 484463 - config_name: fr-ja features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - ja splits: - name: train num_bytes: 418188 num_examples: 519 download_size: 151400 dataset_size: 418188 - config_name: ar-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - nl splits: - name: train num_bytes: 9054134 num_examples: 9047 download_size: 2765542 dataset_size: 9054134 - config_name: cs-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - cs - nl splits: - name: train num_bytes: 5860976 num_examples: 17358 download_size: 2174494 dataset_size: 5860976 - config_name: de-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - de - nl splits: - name: train num_bytes: 7645565 num_examples: 21439 download_size: 2757414 dataset_size: 7645565 - config_name: en-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - en - nl splits: - name: train num_bytes: 7316599 num_examples: 19399 download_size: 2575916 dataset_size: 7316599 - config_name: es-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - es - nl splits: - name: train num_bytes: 7560123 num_examples: 21012 download_size: 2674557 dataset_size: 7560123 - config_name: fr-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - nl splits: - name: train num_bytes: 7603503 num_examples: 20898 download_size: 2659946 dataset_size: 7603503 - config_name: it-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - it - nl splits: - name: train num_bytes: 5380912 num_examples: 15428 download_size: 1899094 dataset_size: 5380912 - config_name: ar-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - pt splits: - name: train num_bytes: 11340074 num_examples: 11433 download_size: 3504173 dataset_size: 11340074 - config_name: cs-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - cs - pt splits: - name: train num_bytes: 6183725 num_examples: 18356 download_size: 2310039 dataset_size: 6183725 - config_name: de-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - de - pt splits: - name: train num_bytes: 7699083 num_examples: 21884 download_size: 2794173 dataset_size: 7699083 - config_name: en-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - en - pt splits: - name: train num_bytes: 9238819 num_examples: 25929 download_size: 3310748 dataset_size: 9238819 - config_name: es-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - es - pt splits: - name: train num_bytes: 9195685 num_examples: 25551 download_size: 3278814 dataset_size: 9195685 - config_name: fr-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - pt splits: - name: train num_bytes: 9261169 num_examples: 25642 download_size: 3254925 dataset_size: 9261169 - config_name: it-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - it - pt splits: - name: train num_bytes: 3988570 num_examples: 11407 download_size: 1397344 dataset_size: 3988570 - config_name: nl-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - nl - pt splits: - name: train num_bytes: 3612339 num_examples: 10598 download_size: 1290715 dataset_size: 3612339 - config_name: ar-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - ru splits: - name: train num_bytes: 105804303 num_examples: 84455 download_size: 28643600 dataset_size: 105804303 - config_name: cs-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - cs - ru splits: - name: train num_bytes: 71185695 num_examples: 161133 download_size: 21917168 dataset_size: 71185695 - config_name: de-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - de - ru splits: - name: train num_bytes: 81812014 num_examples: 175905 download_size: 24610973 dataset_size: 81812014 - config_name: en-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - en - ru splits: - name: train num_bytes: 83282480 num_examples: 190104 download_size: 24849511 dataset_size: 83282480 - config_name: es-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - es - ru splits: - name: train num_bytes: 84345850 num_examples: 180217 download_size: 24883942 dataset_size: 84345850 - config_name: fr-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - ru splits: - name: train num_bytes: 75967253 num_examples: 160740 download_size: 22385777 dataset_size: 75967253 - config_name: it-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - it - ru splits: - name: train num_bytes: 12915073 num_examples: 27267 download_size: 3781318 dataset_size: 12915073 - config_name: ja-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - ja - ru splits: - name: train num_bytes: 596166 num_examples: 586 download_size: 184791 dataset_size: 596166 - config_name: nl-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - nl - ru splits: - name: train num_bytes: 8933805 num_examples: 19112 download_size: 2662250 dataset_size: 8933805 - config_name: pt-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - pt - ru splits: - name: train num_bytes: 8645475 num_examples: 18458 download_size: 2584012 dataset_size: 8645475 - config_name: ar-zh features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - zh splits: - name: train num_bytes: 65483204 num_examples: 66021 download_size: 21625859 dataset_size: 65483204 - config_name: cs-zh features: - name: id dtype: string - name: translation dtype: translation: languages: - cs - zh splits: - name: train num_bytes: 29971192 num_examples: 45424 download_size: 12495392 dataset_size: 29971192 - config_name: de-zh features: - name: id dtype: string - name: translation dtype: translation: languages: - de - zh splits: - name: train num_bytes: 39044704 num_examples: 59020 download_size: 15773631 dataset_size: 39044704 - config_name: en-zh features: - name: id dtype: string - name: translation dtype: translation: languages: - en - zh splits: - name: train num_bytes: 44596087 num_examples: 69206 download_size: 18101984 dataset_size: 44596087 - config_name: es-zh features: - name: id dtype: string - name: translation dtype: translation: languages: - es - zh splits: - name: train num_bytes: 43940013 num_examples: 65424 download_size: 17424938 dataset_size: 43940013 - config_name: fr-zh features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - zh splits: - name: train num_bytes: 40144071 num_examples: 59060 download_size: 15817862 dataset_size: 40144071 - config_name: it-zh features: - name: id dtype: string - name: translation dtype: translation: languages: - it - zh splits: - name: train num_bytes: 9676756 num_examples: 14652 download_size: 3799012 dataset_size: 9676756 - config_name: ja-zh features: - name: id dtype: string - name: translation dtype: translation: languages: - ja - zh splits: - name: train num_bytes: 462685 num_examples: 570 download_size: 181924 dataset_size: 462685 - config_name: nl-zh features: - name: id dtype: string - name: translation dtype: translation: languages: - nl - zh splits: - name: train num_bytes: 5509070 num_examples: 8433 download_size: 2218937 dataset_size: 5509070 - config_name: pt-zh features: - name: id dtype: string - name: translation dtype: translation: languages: - pt - zh splits: - name: train num_bytes: 7152774 num_examples: 10873 download_size: 2889296 dataset_size: 7152774 - config_name: ru-zh features: - name: id dtype: string - name: translation dtype: translation: languages: - ru - zh splits: - name: train num_bytes: 43112824 num_examples: 47687 download_size: 14225498 dataset_size: 43112824 --- # Dataset Card for NewsCommentary ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://opus.nlpl.eu/News-Commentary.php - **Repository:** None - **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf - **Leaderboard:** [More Information Needed] - **Point of Contact:** [More Information Needed] ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.

MichiganNLP/svo_probes

MichiganNLP

--- license: cc-by-4.0 language: - en pretty_name: SVO-Probes size_categories: - 10K<n<100K --- # SVO-Probes This dataset comes from https://github.com/deepmind/svo_probes. ## Usage ```python from datasets import load_dataset # Note that the following line says "train" split, but there are actually no splits in this dataset. dataset = load_dataset("MichiganNLP/svo_probes", split="train") # To see an example, access the first element of the dataset with `dataset[0]`. ```

togethercomputer/RedPajama-Data-1T-Sample

togethercomputer

--- task_categories: - text-generation language: - en pretty_name: Red Pajama 1T Sample --- # Dataset Card for Dataset Name ### Dataset Summary RedPajama is a clean-room, fully open-source implementation of the LLaMa dataset. This HuggingFace repo contains a 1B-token sample of the RedPajama dataset. The full dataset has the following token counts and is available for [download]( https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T): | Dataset | Token Count | |---------------|-------------| | Commoncrawl | 878 Billion | | C4 | 175 Billion | | GitHub | 59 Billion | | Books | 26 Billion | | ArXiv | 28 Billion | | Wikipedia | 24 Billion | | StackExchange | 20 Billion | | Total | 1.2 Trillion | A full set of scripts to recreate the dataset from scratch can be found [here](https://github.com/togethercomputer/RedPajama-Data). ### Languages Primarily English, though the Wikipedia slice contains multiple languages. ## Dataset Structure The dataset structure is as follows: ``` { "text": ..., "meta": {"url": "...", "timestamp": "...", "source": "...", "language": "...", ...} } ``` ## Dataset Creation This dataset was created to follow the LLaMa paper as closely as possible to try to reproduce its recipe. ### Source Data #### Commoncrawl We download five dumps from Commoncrawl, and run the dumps through the official `cc_net` pipeline. We then deduplicate on the paragraph level, and filter out low quality text using a linear classifier trained to classify paragraphs as Wikipedia references or random Commoncrawl samples. #### C4 C4 is downloaded from Huggingface. The only preprocessing step is to bring the data into our own format. #### GitHub The raw GitHub data is downloaded from Google BigQuery. We deduplicate on the file level and filter out low quality files and only keep projects that are distributed under the MIT, BSD, or Apache license. #### Wikipedia We use the Wikipedia dataset available on Huggingface, which is based on the Wikipedia dump from 2023-03-20 and contains text in 20 different languages. The dataset comes in preprocessed format, so that hyperlinks, comments and other formatting boilerplate has been removed. #### Gutenberg and Books3 The PG19 subset of the Gutenberg Project and Books3 datasets are downloaded from Huggingface. After downloading, we use simhash to remove near duplicates. #### ArXiv ArXiv data is downloaded from Amazon S3 in the `arxiv` requester pays bucket. We only keep latex source files and remove preambles, comments, macros and bibliographies. #### Stackexchange The Stack Exchange split of the dataset is download from the [Internet Archive](https://archive.org/download/stackexchange). Here we only keep the posts from the 28 largest sites, remove html tags, group the posts into question-answer pairs, and order answers by their score. <!-- ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed] -->

wiki_dpr

--- annotations_creators: - no-annotation language_creators: - crowdsourced language: - en license: - cc-by-sa-3.0 - gfdl multilinguality: - multilingual size_categories: - 10M<n<100M source_datasets: - original task_categories: - fill-mask - text-generation task_ids: - language-modeling - masked-language-modeling pretty_name: Wiki-DPR tags: - text-search dataset_info: - config_name: psgs_w100.nq.exact features: - name: id dtype: string - name: text dtype: string - name: title dtype: string - name: embeddings sequence: float32 splits: - name: train num_bytes: 78419281788 num_examples: 21015300 download_size: 70965697456 dataset_size: 78419281788 - config_name: psgs_w100.nq.compressed features: - name: id dtype: string - name: text dtype: string - name: title dtype: string - name: embeddings sequence: float32 splits: - name: train num_bytes: 78419281788 num_examples: 21015300 download_size: 70965697456 dataset_size: 78419281788 - config_name: psgs_w100.nq.no_index features: - name: id dtype: string - name: text dtype: string - name: title dtype: string - name: embeddings sequence: float32 splits: - name: train num_bytes: 78419281788 num_examples: 21015300 download_size: 70965697456 dataset_size: 78419281788 - config_name: psgs_w100.multiset.exact features: - name: id dtype: string - name: text dtype: string - name: title dtype: string - name: embeddings sequence: float32 splits: - name: train num_bytes: 78419281788 num_examples: 21015300 download_size: 70965697456 dataset_size: 78419281788 - config_name: psgs_w100.multiset.compressed features: - name: id dtype: string - name: text dtype: string - name: title dtype: string - name: embeddings sequence: float32 splits: - name: train num_bytes: 78419281788 num_examples: 21015300 download_size: 70965697456 dataset_size: 78419281788 - config_name: psgs_w100.multiset.no_index features: - name: id dtype: string - name: text dtype: string - name: title dtype: string - name: embeddings sequence: float32 splits: - name: train num_bytes: 78419281788 num_examples: 21015300 download_size: 70965697456 dataset_size: 78419281788 --- # Dataset Card for "wiki_dpr" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://github.com/facebookresearch/DPR](https://github.com/facebookresearch/DPR) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 425.79 GB - **Size of the generated dataset:** 470.52 GB - **Total amount of disk used:** 978.05 GB ### Dataset Summary This is the wikipedia split used to evaluate the Dense Passage Retrieval (DPR) model. It contains 21M passages from wikipedia along with their DPR embeddings. The wikipedia articles were split into multiple, disjoint text blocks of 100 words as passages. The wikipedia dump is the one from Dec. 20, 2018. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances Each instance contains a paragraph of at most 100 words, as well as the title of the wikipedia page it comes from, and the DPR embedding (a 768-d vector). #### psgs_w100.multiset.compressed - **Size of downloaded dataset files:** 70.97 GB - **Size of the generated dataset:** 78.42 GB - **Total amount of disk used:** 152.26 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: {'id': '1', 'text': 'Aaron Aaron ( or ; "Ahärôn") is a prophet, high priest, and the brother of Moses in the Abrahamic religions. Knowledge of Aaron, along with his brother Moses, comes exclusively from religious texts, such as the Bible and Quran. The Hebrew Bible relates that, unlike Moses, who grew up in the Egyptian royal court, Aaron and his elder sister Miriam remained with their kinsmen in the eastern border-land of Egypt (Goshen). When Moses first confronted the Egyptian king about the Israelites, Aaron served as his brother\'s spokesman ("prophet") to the Pharaoh. Part of the Law (Torah) that Moses received from'], 'title': 'Aaron', 'embeddings': [-0.07233893871307373, 0.48035329580307007, 0.18650995194911957, -0.5287084579467773, -0.37329429388046265, 0.37622880935668945, 0.25524479150772095, ... -0.336689829826355, 0.6313082575798035, -0.7025573253631592]} ``` #### psgs_w100.multiset.exact - **Size of downloaded dataset files:** 70.97 GB - **Size of the generated dataset:** 78.42 GB - **Total amount of disk used:** 187.38 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: {'id': '1', 'text': 'Aaron Aaron ( or ; "Ahärôn") is a prophet, high priest, and the brother of Moses in the Abrahamic religions. Knowledge of Aaron, along with his brother Moses, comes exclusively from religious texts, such as the Bible and Quran. The Hebrew Bible relates that, unlike Moses, who grew up in the Egyptian royal court, Aaron and his elder sister Miriam remained with their kinsmen in the eastern border-land of Egypt (Goshen). When Moses first confronted the Egyptian king about the Israelites, Aaron served as his brother\'s spokesman ("prophet") to the Pharaoh. Part of the Law (Torah) that Moses received from'], 'title': 'Aaron', 'embeddings': [-0.07233893871307373, 0.48035329580307007, 0.18650995194911957, -0.5287084579467773, -0.37329429388046265, 0.37622880935668945, 0.25524479150772095, ... -0.336689829826355, 0.6313082575798035, -0.7025573253631592]} ``` #### psgs_w100.multiset.no_index - **Size of downloaded dataset files:** 70.97 GB - **Size of the generated dataset:** 78.42 GB - **Total amount of disk used:** 149.38 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: {'id': '1', 'text': 'Aaron Aaron ( or ; "Ahärôn") is a prophet, high priest, and the brother of Moses in the Abrahamic religions. Knowledge of Aaron, along with his brother Moses, comes exclusively from religious texts, such as the Bible and Quran. The Hebrew Bible relates that, unlike Moses, who grew up in the Egyptian royal court, Aaron and his elder sister Miriam remained with their kinsmen in the eastern border-land of Egypt (Goshen). When Moses first confronted the Egyptian king about the Israelites, Aaron served as his brother\'s spokesman ("prophet") to the Pharaoh. Part of the Law (Torah) that Moses received from'], 'title': 'Aaron', 'embeddings': [-0.07233893871307373, 0.48035329580307007, 0.18650995194911957, -0.5287084579467773, -0.37329429388046265, 0.37622880935668945, 0.25524479150772095, ... -0.336689829826355, 0.6313082575798035, -0.7025573253631592]} ``` #### psgs_w100.nq.compressed - **Size of downloaded dataset files:** 70.97 GB - **Size of the generated dataset:** 78.42 GB - **Total amount of disk used:** 152.26 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: {'id': '1', 'text': 'Aaron Aaron ( or ; "Ahärôn") is a prophet, high priest, and the brother of Moses in the Abrahamic religions. Knowledge of Aaron, along with his brother Moses, comes exclusively from religious texts, such as the Bible and Quran. The Hebrew Bible relates that, unlike Moses, who grew up in the Egyptian royal court, Aaron and his elder sister Miriam remained with their kinsmen in the eastern border-land of Egypt (Goshen). When Moses first confronted the Egyptian king about the Israelites, Aaron served as his brother\'s spokesman ("prophet") to the Pharaoh. Part of the Law (Torah) that Moses received from'], 'title': 'Aaron', 'embeddings': [0.013342111371457577, 0.582173764705658, -0.31309744715690613, -0.6991612911224365, -0.5583199858665466, 0.5187504887580872, 0.7152731418609619, ... -0.5385938286781311, 0.8093984127044678, -0.4741983711719513]} ``` #### psgs_w100.nq.exact - **Size of downloaded dataset files:** 70.97 GB - **Size of the generated dataset:** 78.42 GB - **Total amount of disk used:** 187.38 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: {'id': '1', 'text': 'Aaron Aaron ( or ; "Ahärôn") is a prophet, high priest, and the brother of Moses in the Abrahamic religions. Knowledge of Aaron, along with his brother Moses, comes exclusively from religious texts, such as the Bible and Quran. The Hebrew Bible relates that, unlike Moses, who grew up in the Egyptian royal court, Aaron and his elder sister Miriam remained with their kinsmen in the eastern border-land of Egypt (Goshen). When Moses first confronted the Egyptian king about the Israelites, Aaron served as his brother\'s spokesman ("prophet") to the Pharaoh. Part of the Law (Torah) that Moses received from'], 'title': 'Aaron', 'embeddings': [0.013342111371457577, 0.582173764705658, -0.31309744715690613, -0.6991612911224365, -0.5583199858665466, 0.5187504887580872, 0.7152731418609619, ... -0.5385938286781311, 0.8093984127044678, -0.4741983711719513]} ``` ### Data Fields The data fields are the same among all splits. #### psgs_w100.multiset.compressed - `id`: a `string` feature. - `text`: a `string` feature. - `title`: a `string` feature. - `embeddings`: a `list` of `float32` features. #### psgs_w100.multiset.exact - `id`: a `string` feature. - `text`: a `string` feature. - `title`: a `string` feature. - `embeddings`: a `list` of `float32` features. #### psgs_w100.multiset.no_index - `id`: a `string` feature. - `text`: a `string` feature. - `title`: a `string` feature. - `embeddings`: a `list` of `float32` features. #### psgs_w100.nq.compressed - `id`: a `string` feature. - `text`: a `string` feature. - `title`: a `string` feature. - `embeddings`: a `list` of `float32` features. #### psgs_w100.nq.exact - `id`: a `string` feature. - `text`: a `string` feature. - `title`: a `string` feature. - `embeddings`: a `list` of `float32` features. ### Data Splits | name | train | |-----------------------------|-------:| |psgs_w100.multiset.compressed|21015300| |psgs_w100.multiset.exact |21015300| |psgs_w100.multiset.no_index |21015300| |psgs_w100.nq.compressed |21015300| |psgs_w100.nq.exact |21015300| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @misc{karpukhin2020dense, title={Dense Passage Retrieval for Open-Domain Question Answering}, author={Vladimir Karpukhin and Barlas Oğuz and Sewon Min and Patrick Lewis and Ledell Wu and Sergey Edunov and Danqi Chen and Wen-tau Yih}, year={2020}, eprint={2004.04906}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@lhoestq](https://github.com/lhoestq) for adding this dataset.

wmt14

--- annotations_creators: - no-annotation language_creators: - found language: - cs - de - en - fr - hi - ru license: - unknown multilinguality: - translation size_categories: - 10M<n<100M source_datasets: - extended|europarl_bilingual - extended|giga_fren - extended|news_commentary - extended|un_multi - extended|hind_encorp task_categories: - translation task_ids: [] pretty_name: WMT14 paperswithcode_id: wmt-2014 dataset_info: - config_name: cs-en features: - name: translation dtype: translation: languages: - cs - en splits: - name: train num_bytes: 280992794 num_examples: 953621 - name: validation num_bytes: 702473 num_examples: 3000 - name: test num_bytes: 757817 num_examples: 3003 download_size: 1696003559 dataset_size: 282453084 - config_name: de-en features: - name: translation dtype: translation: languages: - de - en splits: - name: train num_bytes: 1358410408 num_examples: 4508785 - name: validation num_bytes: 736415 num_examples: 3000 - name: test num_bytes: 777334 num_examples: 3003 download_size: 1696003559 dataset_size: 1359924157 - config_name: fr-en features: - name: translation dtype: translation: languages: - fr - en splits: - name: train num_bytes: 14752554924 num_examples: 40836715 - name: validation num_bytes: 744447 num_examples: 3000 - name: test num_bytes: 838857 num_examples: 3003 download_size: 6658118909 dataset_size: 14754138228 - config_name: hi-en features: - name: translation dtype: translation: languages: - hi - en splits: - name: train num_bytes: 1936035 num_examples: 32863 - name: validation num_bytes: 181465 num_examples: 520 - name: test num_bytes: 1075016 num_examples: 2507 download_size: 46879684 dataset_size: 3192516 - config_name: ru-en features: - name: translation dtype: translation: languages: - ru - en splits: - name: train num_bytes: 433210270 num_examples: 1486965 - name: validation num_bytes: 977946 num_examples: 3000 - name: test num_bytes: 1087746 num_examples: 3003 download_size: 1047396736 dataset_size: 435275962 --- # Dataset Card for "wmt14" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [http://www.statmt.org/wmt14/translation-task.html](http://www.statmt.org/wmt14/translation-task.html) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 1.70 GB - **Size of the generated dataset:** 282.95 MB - **Total amount of disk used:** 1.98 GB ### Dataset Summary <div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"> <p><b>Warning:</b> There are issues with the Common Crawl corpus data (<a href="https://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz">training-parallel-commoncrawl.tgz</a>):</p> <ul> <li>Non-English files contain many English sentences.</li> <li>Their "parallel" sentences in English are not aligned: they are uncorrelated with their counterpart.</li> </ul> <p>We have contacted the WMT organizers.</p> </div> Translation dataset based on the data from statmt.org. Versions exist for different years using a combination of data sources. The base `wmt` allows you to create a custom dataset by choosing your own data/language pair. This can be done as follows: ```python from datasets import inspect_dataset, load_dataset_builder inspect_dataset("wmt14", "path/to/scripts") builder = load_dataset_builder( "path/to/scripts/wmt_utils.py", language_pair=("fr", "de"), subsets={ datasets.Split.TRAIN: ["commoncrawl_frde"], datasets.Split.VALIDATION: ["euelections_dev2019"], }, ) # Standard version builder.download_and_prepare() ds = builder.as_dataset() # Streamable version ds = builder.as_streaming_dataset() ``` ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### cs-en - **Size of downloaded dataset files:** 1.70 GB - **Size of the generated dataset:** 282.95 MB - **Total amount of disk used:** 1.98 GB An example of 'train' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### cs-en - `translation`: a multilingual `string` variable, with possible languages including `cs`, `en`. ### Data Splits |name |train |validation|test| |-----|-----:|---------:|---:| |cs-en|953621| 3000|3003| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{bojar-EtAl:2014:W14-33, author = {Bojar, Ondrej and Buck, Christian and Federmann, Christian and Haddow, Barry and Koehn, Philipp and Leveling, Johannes and Monz, Christof and Pecina, Pavel and Post, Matt and Saint-Amand, Herve and Soricut, Radu and Specia, Lucia and Tamchyna, Ale {s}}, title = {Findings of the 2014 Workshop on Statistical Machine Translation}, booktitle = {Proceedings of the Ninth Workshop on Statistical Machine Translation}, month = {June}, year = {2014}, address = {Baltimore, Maryland, USA}, publisher = {Association for Computational Linguistics}, pages = {12--58}, url = {http://www.aclweb.org/anthology/W/W14/W14-3302} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

opus_infopankki

--- annotations_creators: - found language_creators: - found language: - ar - en - es - et - fa - fi - fr - ru - so - sv - tr - zh license: - unknown multilinguality: - multilingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: null pretty_name: OpusInfopankki dataset_info: - config_name: ar-en features: - name: translation dtype: translation: languages: - ar - en splits: - name: train num_bytes: 10133385 num_examples: 50769 download_size: 1675642 dataset_size: 10133385 - config_name: ar-es features: - name: translation dtype: translation: languages: - ar - es splits: - name: train num_bytes: 8665395 num_examples: 40514 download_size: 1481047 dataset_size: 8665395 - config_name: ar-et features: - name: translation dtype: translation: languages: - ar - et splits: - name: train num_bytes: 9087595 num_examples: 46573 download_size: 1526418 dataset_size: 9087595 - config_name: ar-fa features: - name: translation dtype: translation: languages: - ar - fa splits: - name: train num_bytes: 12220236 num_examples: 47007 download_size: 1817143 dataset_size: 12220236 - config_name: ar-fi features: - name: translation dtype: translation: languages: - ar - fi splits: - name: train num_bytes: 9524305 num_examples: 49608 download_size: 1599735 dataset_size: 9524305 - config_name: ar-fr features: - name: translation dtype: translation: languages: - ar - fr splits: - name: train num_bytes: 8877669 num_examples: 41061 download_size: 1516374 dataset_size: 8877669 - config_name: ar-ru features: - name: translation dtype: translation: languages: - ar - ru splits: - name: train num_bytes: 13648242 num_examples: 50286 download_size: 1970843 dataset_size: 13648242 - config_name: ar-so features: - name: translation dtype: translation: languages: - ar - so splits: - name: train num_bytes: 9555588 num_examples: 44736 download_size: 1630676 dataset_size: 9555588 - config_name: ar-sv features: - name: translation dtype: translation: languages: - ar - sv splits: - name: train num_bytes: 8585175 num_examples: 43085 download_size: 1469533 dataset_size: 8585175 - config_name: ar-tr features: - name: translation dtype: translation: languages: - ar - tr splits: - name: train num_bytes: 8691117 num_examples: 41710 download_size: 1481787 dataset_size: 8691117 - config_name: ar-zh features: - name: translation dtype: translation: languages: - ar - zh splits: - name: train num_bytes: 5973658 num_examples: 29943 download_size: 1084404 dataset_size: 5973658 - config_name: en-es features: - name: translation dtype: translation: languages: - en - es splits: - name: train num_bytes: 6934023 num_examples: 42657 download_size: 1333020 dataset_size: 6934023 - config_name: en-et features: - name: translation dtype: translation: languages: - en - et splits: - name: train num_bytes: 8211610 num_examples: 58410 download_size: 1509893 dataset_size: 8211610 - config_name: en-fa features: - name: translation dtype: translation: languages: - en - fa splits: - name: train num_bytes: 10166345 num_examples: 48277 download_size: 1657826 dataset_size: 10166345 - config_name: en-fi features: - name: translation dtype: translation: languages: - en - fi splits: - name: train num_bytes: 10913673 num_examples: 84645 download_size: 1860908 dataset_size: 10913673 - config_name: en-fr features: - name: translation dtype: translation: languages: - en - fr splits: - name: train num_bytes: 8903231 num_examples: 56120 download_size: 1572554 dataset_size: 8903231 - config_name: en-ru features: - name: translation dtype: translation: languages: - en - ru splits: - name: train num_bytes: 15918259 num_examples: 75305 download_size: 2220544 dataset_size: 15918259 - config_name: en-so features: - name: translation dtype: translation: languages: - en - so splits: - name: train num_bytes: 7602330 num_examples: 47220 download_size: 1467156 dataset_size: 7602330 - config_name: en-sv features: - name: translation dtype: translation: languages: - en - sv splits: - name: train num_bytes: 7411023 num_examples: 51749 download_size: 1384139 dataset_size: 7411023 - config_name: en-tr features: - name: translation dtype: translation: languages: - en - tr splits: - name: train num_bytes: 6929194 num_examples: 44030 download_size: 1329853 dataset_size: 6929194 - config_name: en-zh features: - name: translation dtype: translation: languages: - en - zh splits: - name: train num_bytes: 4666987 num_examples: 29907 download_size: 894750 dataset_size: 4666987 - config_name: es-et features: - name: translation dtype: translation: languages: - es - et splits: - name: train num_bytes: 6611996 num_examples: 42342 download_size: 1301067 dataset_size: 6611996 - config_name: es-fa features: - name: translation dtype: translation: languages: - es - fa splits: - name: train num_bytes: 9338250 num_examples: 41218 download_size: 1558933 dataset_size: 9338250 - config_name: es-fi features: - name: translation dtype: translation: languages: - es - fi splits: - name: train num_bytes: 6436338 num_examples: 41479 download_size: 1253298 dataset_size: 6436338 - config_name: es-fr features: - name: translation dtype: translation: languages: - es - fr splits: - name: train num_bytes: 7368764 num_examples: 41940 download_size: 1406167 dataset_size: 7368764 - config_name: es-ru features: - name: translation dtype: translation: languages: - es - ru splits: - name: train num_bytes: 9844977 num_examples: 41061 download_size: 1595928 dataset_size: 9844977 - config_name: es-so features: - name: translation dtype: translation: languages: - es - so splits: - name: train num_bytes: 7257078 num_examples: 41752 download_size: 1438303 dataset_size: 7257078 - config_name: es-sv features: - name: translation dtype: translation: languages: - es - sv splits: - name: train num_bytes: 6650692 num_examples: 41256 download_size: 1291291 dataset_size: 6650692 - config_name: es-tr features: - name: translation dtype: translation: languages: - es - tr splits: - name: train num_bytes: 7144105 num_examples: 42191 download_size: 1372312 dataset_size: 7144105 - config_name: es-zh features: - name: translation dtype: translation: languages: - es - zh splits: - name: train num_bytes: 4358775 num_examples: 26004 download_size: 810902 dataset_size: 4358775 - config_name: et-fa features: - name: translation dtype: translation: languages: - et - fa splits: - name: train num_bytes: 9796036 num_examples: 47633 download_size: 1603405 dataset_size: 9796036 - config_name: et-fi features: - name: translation dtype: translation: languages: - et - fi splits: - name: train num_bytes: 7657037 num_examples: 57353 download_size: 1425641 dataset_size: 7657037 - config_name: et-fr features: - name: translation dtype: translation: languages: - et - fr splits: - name: train num_bytes: 7012470 num_examples: 44753 download_size: 1355458 dataset_size: 7012470 - config_name: et-ru features: - name: translation dtype: translation: languages: - et - ru splits: - name: train num_bytes: 12001439 num_examples: 55901 download_size: 1812764 dataset_size: 12001439 - config_name: et-so features: - name: translation dtype: translation: languages: - et - so splits: - name: train num_bytes: 7260837 num_examples: 46933 download_size: 1432147 dataset_size: 7260837 - config_name: et-sv features: - name: translation dtype: translation: languages: - et - sv splits: - name: train num_bytes: 6523081 num_examples: 46775 download_size: 1268616 dataset_size: 6523081 - config_name: et-tr features: - name: translation dtype: translation: languages: - et - tr splits: - name: train num_bytes: 6621705 num_examples: 43729 download_size: 1299911 dataset_size: 6621705 - config_name: et-zh features: - name: translation dtype: translation: languages: - et - zh splits: - name: train num_bytes: 4305297 num_examples: 27826 download_size: 808812 dataset_size: 4305297 - config_name: fa-fi features: - name: translation dtype: translation: languages: - fa - fi splits: - name: train num_bytes: 9579297 num_examples: 46924 download_size: 1574886 dataset_size: 9579297 - config_name: fa-fr features: - name: translation dtype: translation: languages: - fa - fr splits: - name: train num_bytes: 9574294 num_examples: 41975 download_size: 1591112 dataset_size: 9574294 - config_name: fa-ru features: - name: translation dtype: translation: languages: - fa - ru splits: - name: train num_bytes: 13544491 num_examples: 47814 download_size: 1947217 dataset_size: 13544491 - config_name: fa-so features: - name: translation dtype: translation: languages: - fa - so splits: - name: train num_bytes: 10254763 num_examples: 45571 download_size: 1722085 dataset_size: 10254763 - config_name: fa-sv features: - name: translation dtype: translation: languages: - fa - sv splits: - name: train num_bytes: 9153792 num_examples: 43510 download_size: 1519092 dataset_size: 9153792 - config_name: fa-tr features: - name: translation dtype: translation: languages: - fa - tr splits: - name: train num_bytes: 9393249 num_examples: 42708 download_size: 1559312 dataset_size: 9393249 - config_name: fa-zh features: - name: translation dtype: translation: languages: - fa - zh splits: - name: train num_bytes: 5792463 num_examples: 27748 download_size: 1027887 dataset_size: 5792463 - config_name: fi-fr features: - name: translation dtype: translation: languages: - fi - fr splits: - name: train num_bytes: 8310899 num_examples: 55087 download_size: 1488763 dataset_size: 8310899 - config_name: fi-ru features: - name: translation dtype: translation: languages: - fi - ru splits: - name: train num_bytes: 15188232 num_examples: 74699 download_size: 2142712 dataset_size: 15188232 - config_name: fi-so features: - name: translation dtype: translation: languages: - fi - so splits: - name: train num_bytes: 7076261 num_examples: 46032 download_size: 1387424 dataset_size: 7076261 - config_name: fi-sv features: - name: translation dtype: translation: languages: - fi - sv splits: - name: train num_bytes: 6947272 num_examples: 51506 download_size: 1312272 dataset_size: 6947272 - config_name: fi-tr features: - name: translation dtype: translation: languages: - fi - tr splits: - name: train num_bytes: 6438756 num_examples: 42781 download_size: 1251294 dataset_size: 6438756 - config_name: fi-zh features: - name: translation dtype: translation: languages: - fi - zh splits: - name: train num_bytes: 4434192 num_examples: 29503 download_size: 864043 dataset_size: 4434192 - config_name: fr-ru features: - name: translation dtype: translation: languages: - fr - ru splits: - name: train num_bytes: 12564244 num_examples: 54213 download_size: 1862751 dataset_size: 12564244 - config_name: fr-so features: - name: translation dtype: translation: languages: - fr - so splits: - name: train num_bytes: 7473599 num_examples: 42652 download_size: 1471709 dataset_size: 7473599 - config_name: fr-sv features: - name: translation dtype: translation: languages: - fr - sv splits: - name: train num_bytes: 7027603 num_examples: 43524 download_size: 1343061 dataset_size: 7027603 - config_name: fr-tr features: - name: translation dtype: translation: languages: - fr - tr splits: - name: train num_bytes: 7341118 num_examples: 43036 download_size: 1399175 dataset_size: 7341118 - config_name: fr-zh features: - name: translation dtype: translation: languages: - fr - zh splits: - name: train num_bytes: 4525133 num_examples: 26654 download_size: 850456 dataset_size: 4525133 - config_name: ru-so features: - name: translation dtype: translation: languages: - ru - so splits: - name: train num_bytes: 10809233 num_examples: 45430 download_size: 1742599 dataset_size: 10809233 - config_name: ru-sv features: - name: translation dtype: translation: languages: - ru - sv splits: - name: train num_bytes: 10517473 num_examples: 47672 download_size: 1634682 dataset_size: 10517473 - config_name: ru-tr features: - name: translation dtype: translation: languages: - ru - tr splits: - name: train num_bytes: 9930632 num_examples: 42587 download_size: 1591805 dataset_size: 9930632 - config_name: ru-zh features: - name: translation dtype: translation: languages: - ru - zh splits: - name: train num_bytes: 6417832 num_examples: 29523 download_size: 1109274 dataset_size: 6417832 - config_name: so-sv features: - name: translation dtype: translation: languages: - so - sv splits: - name: train num_bytes: 6763794 num_examples: 42384 download_size: 1353892 dataset_size: 6763794 - config_name: so-tr features: - name: translation dtype: translation: languages: - so - tr splits: - name: train num_bytes: 7272389 num_examples: 43242 download_size: 1440287 dataset_size: 7272389 - config_name: so-zh features: - name: translation dtype: translation: languages: - so - zh splits: - name: train num_bytes: 4535979 num_examples: 27090 download_size: 859149 dataset_size: 4535979 - config_name: sv-tr features: - name: translation dtype: translation: languages: - sv - tr splits: - name: train num_bytes: 6637784 num_examples: 42555 download_size: 1288209 dataset_size: 6637784 - config_name: sv-zh features: - name: translation dtype: translation: languages: - sv - zh splits: - name: train num_bytes: 4216429 num_examples: 26898 download_size: 779012 dataset_size: 4216429 - config_name: tr-zh features: - name: translation dtype: translation: languages: - tr - zh splits: - name: train num_bytes: 4494095 num_examples: 27323 download_size: 841988 dataset_size: 4494095 config_names: - ar-en - ar-es - ar-et - ar-fa - ar-fi - ar-fr - ar-ru - ar-so - ar-sv - ar-tr - ar-zh - en-es - en-et - en-fa - en-fi - en-fr - en-ru - en-so - en-sv - en-tr - en-zh - es-et - es-fa - es-fi - es-fr - es-ru - es-so - es-sv - es-tr - es-zh - et-fa - et-fi - et-fr - et-ru - et-so - et-sv - et-tr - et-zh - fa-fi - fa-fr - fa-ru - fa-so - fa-sv - fa-tr - fa-zh - fi-fr - fi-ru - fi-so - fi-sv - fi-tr - fi-zh - fr-ru - fr-so - fr-sv - fr-tr - fr-zh - ru-so - ru-sv - ru-tr - ru-zh - so-sv - so-tr - so-zh - sv-tr - sv-zh - tr-zh --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:**[infopankki](http://opus.nlpl.eu/infopankki-v1.php) - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary A parallel corpus of 12 languages, 66 bitexts. ### Supported Tasks and Leaderboards The underlying task is machine translation. ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @InProceedings{TIEDEMANN12.463, author = {J�rg Tiedemann}, title = {Parallel Data, Tools and Interfaces in OPUS}, booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, month = {may}, date = {23-25}, address = {Istanbul, Turkey}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-7-7}, language = {english} } ``` ### Contributions Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.

zh-plus/tiny-imagenet

zh-plus

--- annotations_creators: - crowdsourced extra_gated_prompt: "By clicking on \u201CAccess repository\u201D below, you also\ \ agree to ImageNet Terms of Access:\n[RESEARCHER_FULLNAME] (the \"Researcher\"\ ) has requested permission to use the ImageNet database (the \"Database\") at Princeton\ \ University and Stanford University. In exchange for such permission, Researcher\ \ hereby agrees to the following terms and conditions:\n1. Researcher shall use\ \ the Database only for non-commercial research and educational purposes.\n2. Princeton\ \ University, Stanford University and Hugging Face make no representations or warranties\ \ regarding the Database, including but not limited to warranties of non-infringement\ \ or fitness for a particular purpose.\n3. Researcher accepts full responsibility\ \ for his or her use of the Database and shall defend and indemnify the ImageNet\ \ team, Princeton University, Stanford University and Hugging Face, including their\ \ employees, Trustees, officers and agents, against any and all claims arising from\ \ Researcher's use of the Database, including but not limited to Researcher's use\ \ of any copies of copyrighted images that he or she may create from the Database.\n\ 4. Researcher may provide research associates and colleagues with access to the\ \ Database provided that they first agree to be bound by these terms and conditions.\n\ 5. Princeton University, Stanford University and Hugging Face reserve the right\ \ to terminate Researcher's access to the Database at any time.\n6. If Researcher\ \ is employed by a for-profit, commercial entity, Researcher's employer shall also\ \ be bound by these terms and conditions, and Researcher hereby represents that\ \ he or she is fully authorized to enter into this agreement on behalf of such employer.\n\ 7. The law of the State of New Jersey shall apply to all disputes under this agreement." language: - en language_creators: - crowdsourced license: [] multilinguality: - monolingual paperswithcode_id: imagenet pretty_name: Tiny-ImageNet size_categories: - 100K<n<1M source_datasets: - extended|imagenet-1k task_categories: - image-classification task_ids: - multi-class-image-classification --- # Dataset Card for tiny-imagenet ## Dataset Description - **Homepage:** https://www.kaggle.com/c/tiny-imagenet - **Repository:** [Needs More Information] - **Paper:** http://cs231n.stanford.edu/reports/2017/pdfs/930.pdf - **Leaderboard:** https://paperswithcode.com/sota/image-classification-on-tiny-imagenet-1 ### Dataset Summary Tiny ImageNet contains 100000 images of 200 classes (500 for each class) downsized to 64×64 colored images. Each class has 500 training images, 50 validation images, and 50 test images. ### Languages The class labels in the dataset are in English. ## Dataset Structure ### Data Instances ```json { 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=64x64 at 0x1A800E8E190, 'label': 15 } ``` ### Data Fields - image: A PIL.Image.Image object containing the image. Note that when accessing the image column: dataset[0]["image"] the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the "image" column, i.e. dataset[0]["image"] should always be preferred over dataset["image"][0]. - label: an int classification label. -1 for test set as the labels are missing. Check `classes.py` for the map of numbers & labels. ### Data Splits | | Train | Valid | | ------------ | ------ | ----- | | # of samples | 100000 | 10000 | ## Usage ### Example #### Load Dataset ```python def example_usage(): tiny_imagenet = load_dataset('Maysee/tiny-imagenet', split='train') print(tiny_imagenet[0]) if __name__ == '__main__': example_usage() ```

financial_phrasebank

--- annotations_creators: - expert-generated language_creators: - found language: - en license: - cc-by-nc-sa-3.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification - sentiment-classification pretty_name: FinancialPhrasebank dataset_info: - config_name: sentences_allagree features: - name: sentence dtype: string - name: label dtype: class_label: names: '0': negative '1': neutral '2': positive splits: - name: train num_bytes: 303371 num_examples: 2264 download_size: 681890 dataset_size: 303371 - config_name: sentences_75agree features: - name: sentence dtype: string - name: label dtype: class_label: names: '0': negative '1': neutral '2': positive splits: - name: train num_bytes: 472703 num_examples: 3453 download_size: 681890 dataset_size: 472703 - config_name: sentences_66agree features: - name: sentence dtype: string - name: label dtype: class_label: names: '0': negative '1': neutral '2': positive splits: - name: train num_bytes: 587152 num_examples: 4217 download_size: 681890 dataset_size: 587152 - config_name: sentences_50agree features: - name: sentence dtype: string - name: label dtype: class_label: names: '0': negative '1': neutral '2': positive splits: - name: train num_bytes: 679240 num_examples: 4846 download_size: 681890 dataset_size: 679240 tags: - finance --- # Dataset Card for financial_phrasebank ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Kaggle](https://www.kaggle.com/ankurzing/sentiment-analysis-for-financial-news) [ResearchGate](https://www.researchgate.net/publication/251231364_FinancialPhraseBank-v10) - **Repository:** - **Paper:** [Arxiv](https://arxiv.org/abs/1307.5336) - **Leaderboard:** [Kaggle](https://www.kaggle.com/ankurzing/sentiment-analysis-for-financial-news/code) [PapersWithCode](https://paperswithcode.com/sota/sentiment-analysis-on-financial-phrasebank) = - **Point of Contact:** [Pekka Malo](mailto:pekka.malo@aalto.fi) [Ankur Sinha](mailto:ankur.sinha@aalto.fi) ### Dataset Summary Polar sentiment dataset of sentences from financial news. The dataset consists of 4840 sentences from English language financial news categorised by sentiment. The dataset is divided by agreement rate of 5-8 annotators. ### Supported Tasks and Leaderboards Sentiment Classification ### Languages English ## Dataset Structure ### Data Instances ``` { "sentence": "Pharmaceuticals group Orion Corp reported a fall in its third-quarter earnings that were hit by larger expenditures on R&D and marketing .", "label": "negative" } ``` ### Data Fields - sentence: a tokenized line from the dataset - label: a label corresponding to the class as a string: 'positive', 'negative' or 'neutral' ### Data Splits There's no train/validation/test split. However the dataset is available in four possible configurations depending on the percentage of agreement of annotators: `sentences_50agree`; Number of instances with >=50% annotator agreement: 4846 `sentences_66agree`: Number of instances with >=66% annotator agreement: 4217 `sentences_75agree`: Number of instances with >=75% annotator agreement: 3453 `sentences_allagree`: Number of instances with 100% annotator agreement: 2264 ## Dataset Creation ### Curation Rationale The key arguments for the low utilization of statistical techniques in financial sentiment analysis have been the difficulty of implementation for practical applications and the lack of high quality training data for building such models. Especially in the case of finance and economic texts, annotated collections are a scarce resource and many are reserved for proprietary use only. To resolve the missing training data problem, we present a collection of ∼ 5000 sentences to establish human-annotated standards for benchmarking alternative modeling techniques. The objective of the phrase level annotation task was to classify each example sentence into a positive, negative or neutral category by considering only the information explicitly available in the given sentence. Since the study is focused only on financial and economic domains, the annotators were asked to consider the sentences from the view point of an investor only; i.e. whether the news may have positive, negative or neutral influence on the stock price. As a result, sentences which have a sentiment that is not relevant from an economic or financial perspective are considered neutral. ### Source Data #### Initial Data Collection and Normalization The corpus used in this paper is made out of English news on all listed companies in OMX Helsinki. The news has been downloaded from the LexisNexis database using an automated web scraper. Out of this news database, a random subset of 10,000 articles was selected to obtain good coverage across small and large companies, companies in different industries, as well as different news sources. Following the approach taken by Maks and Vossen (2010), we excluded all sentences which did not contain any of the lexicon entities. This reduced the overall sample to 53,400 sentences, where each has at least one or more recognized lexicon entity. The sentences were then classified according to the types of entity sequences detected. Finally, a random sample of ∼5000 sentences was chosen to represent the overall news database. #### Who are the source language producers? The source data was written by various financial journalists. ### Annotations #### Annotation process This release of the financial phrase bank covers a collection of 4840 sentences. The selected collection of phrases was annotated by 16 people with adequate background knowledge on financial markets. Given the large number of overlapping annotations (5 to 8 annotations per sentence), there are several ways to define a majority vote based gold standard. To provide an objective comparison, we have formed 4 alternative reference datasets based on the strength of majority agreement: #### Who are the annotators? Three of the annotators were researchers and the remaining 13 annotators were master's students at Aalto University School of Business with majors primarily in finance, accounting, and economics. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases All annotators were from the same institution and so interannotator agreement should be understood with this taken into account. ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/. If you are interested in commercial use of the data, please contact the following authors for an appropriate license: - [Pekka Malo](mailto:pekka.malo@aalto.fi) - [Ankur Sinha](mailto:ankur.sinha@aalto.fi) ### Citation Information ``` @article{Malo2014GoodDO, title={Good debt or bad debt: Detecting semantic orientations in economic texts}, author={P. Malo and A. Sinha and P. Korhonen and J. Wallenius and P. Takala}, journal={Journal of the Association for Information Science and Technology}, year={2014}, volume={65} } ``` ### Contributions Thanks to [@frankier](https://github.com/frankier) for adding this dataset.

mhenrichsen/alpaca_2k_test

mhenrichsen

--- license: apache-2.0 ---

distil-whisper/librispeech_long

distil-whisper

--- dataset_info: config_name: clean features: - name: audio dtype: audio splits: - name: validation num_bytes: 1998609.0 num_examples: 1 download_size: 1984721 dataset_size: 1998609.0 configs: - config_name: clean data_files: - split: validation path: clean/validation-* --- # Dataset Card for "librispeech_long" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

newsgroup

--- annotations_creators: - found language: - en language_creators: - found license: - unknown multilinguality: - monolingual pretty_name: 20 Newsgroups size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification paperswithcode_id: 20-newsgroups dataset_info: - config_name: 18828_alt.atheism features: - name: text dtype: string splits: - name: train num_bytes: 1669511 num_examples: 799 download_size: 14666916 dataset_size: 1669511 - config_name: 18828_comp.graphics features: - name: text dtype: string splits: - name: train num_bytes: 1661199 num_examples: 973 download_size: 14666916 dataset_size: 1661199 - config_name: 18828_comp.os.ms-windows.misc features: - name: text dtype: string splits: - name: train num_bytes: 2378739 num_examples: 985 download_size: 14666916 dataset_size: 2378739 - config_name: 18828_comp.sys.ibm.pc.hardware features: - name: text dtype: string splits: - name: train num_bytes: 1185187 num_examples: 982 download_size: 14666916 dataset_size: 1185187 - config_name: 18828_comp.sys.mac.hardware features: - name: text dtype: string splits: - name: train num_bytes: 1056264 num_examples: 961 download_size: 14666916 dataset_size: 1056264 - config_name: 18828_comp.windows.x features: - name: text dtype: string splits: - name: train num_bytes: 1876297 num_examples: 980 download_size: 14666916 dataset_size: 1876297 - config_name: 18828_misc.forsale features: - name: text dtype: string splits: - name: train num_bytes: 925124 num_examples: 972 download_size: 14666916 dataset_size: 925124 - config_name: 18828_rec.autos features: - name: text dtype: string splits: - name: train num_bytes: 1295307 num_examples: 990 download_size: 14666916 dataset_size: 1295307 - config_name: 18828_rec.motorcycles features: - name: text dtype: string splits: - name: train num_bytes: 1206491 num_examples: 994 download_size: 14666916 dataset_size: 1206491 - config_name: 18828_rec.sport.baseball features: - name: text dtype: string splits: - name: train num_bytes: 1369551 num_examples: 994 download_size: 14666916 dataset_size: 1369551 - config_name: 18828_rec.sport.hockey features: - name: text dtype: string splits: - name: train num_bytes: 1758094 num_examples: 999 download_size: 14666916 dataset_size: 1758094 - config_name: 18828_sci.crypt features: - name: text dtype: string splits: - name: train num_bytes: 2050727 num_examples: 991 download_size: 14666916 dataset_size: 2050727 - config_name: 18828_sci.electronics features: - name: text dtype: string splits: - name: train num_bytes: 1237175 num_examples: 981 download_size: 14666916 dataset_size: 1237175 - config_name: 18828_sci.med features: - name: text dtype: string splits: - name: train num_bytes: 1886363 num_examples: 990 download_size: 14666916 dataset_size: 1886363 - config_name: 18828_sci.space features: - name: text dtype: string splits: - name: train num_bytes: 1812803 num_examples: 987 download_size: 14666916 dataset_size: 1812803 - config_name: 18828_soc.religion.christian features: - name: text dtype: string splits: - name: train num_bytes: 2307486 num_examples: 997 download_size: 14666916 dataset_size: 2307486 - config_name: 18828_talk.politics.guns features: - name: text dtype: string splits: - name: train num_bytes: 1922992 num_examples: 910 download_size: 14666916 dataset_size: 1922992 - config_name: 18828_talk.politics.mideast features: - name: text dtype: string splits: - name: train num_bytes: 2910324 num_examples: 940 download_size: 14666916 dataset_size: 2910324 - config_name: 18828_talk.politics.misc features: - name: text dtype: string splits: - name: train num_bytes: 2102809 num_examples: 775 download_size: 14666916 dataset_size: 2102809 - config_name: 18828_talk.religion.misc features: - name: text dtype: string splits: - name: train num_bytes: 1374261 num_examples: 628 download_size: 14666916 dataset_size: 1374261 - config_name: 19997_alt.atheism features: - name: text dtype: string splits: - name: train num_bytes: 2562277 num_examples: 1000 download_size: 17332201 dataset_size: 2562277 - config_name: 19997_comp.graphics features: - name: text dtype: string splits: - name: train num_bytes: 2181673 num_examples: 1000 download_size: 17332201 dataset_size: 2181673 - config_name: 19997_comp.os.ms-windows.misc features: - name: text dtype: string splits: - name: train num_bytes: 2898760 num_examples: 1000 download_size: 17332201 dataset_size: 2898760 - config_name: 19997_comp.sys.ibm.pc.hardware features: - name: text dtype: string splits: - name: train num_bytes: 1671166 num_examples: 1000 download_size: 17332201 dataset_size: 1671166 - config_name: 19997_comp.sys.mac.hardware features: - name: text dtype: string splits: - name: train num_bytes: 1580881 num_examples: 1000 download_size: 17332201 dataset_size: 1580881 - config_name: 19997_comp.windows.x features: - name: text dtype: string splits: - name: train num_bytes: 2418273 num_examples: 1000 download_size: 17332201 dataset_size: 2418273 - config_name: 19997_misc.forsale features: - name: text dtype: string splits: - name: train num_bytes: 1412012 num_examples: 1000 download_size: 17332201 dataset_size: 1412012 - config_name: 19997_rec.autos features: - name: text dtype: string splits: - name: train num_bytes: 1780502 num_examples: 1000 download_size: 17332201 dataset_size: 1780502 - config_name: 19997_rec.motorcycles features: - name: text dtype: string splits: - name: train num_bytes: 1677964 num_examples: 1000 download_size: 17332201 dataset_size: 1677964 - config_name: 19997_rec.sport.baseball features: - name: text dtype: string splits: - name: train num_bytes: 1835432 num_examples: 1000 download_size: 17332201 dataset_size: 1835432 - config_name: 19997_rec.sport.hockey features: - name: text dtype: string splits: - name: train num_bytes: 2207282 num_examples: 1000 download_size: 17332201 dataset_size: 2207282 - config_name: 19997_sci.crypt features: - name: text dtype: string splits: - name: train num_bytes: 2607835 num_examples: 1000 download_size: 17332201 dataset_size: 2607835 - config_name: 19997_sci.electronics features: - name: text dtype: string splits: - name: train num_bytes: 1732199 num_examples: 1000 download_size: 17332201 dataset_size: 1732199 - config_name: 19997_sci.med features: - name: text dtype: string splits: - name: train num_bytes: 2388789 num_examples: 1000 download_size: 17332201 dataset_size: 2388789 - config_name: 19997_sci.space features: - name: text dtype: string splits: - name: train num_bytes: 2351411 num_examples: 1000 download_size: 17332201 dataset_size: 2351411 - config_name: 19997_soc.religion.christian features: - name: text dtype: string splits: - name: train num_bytes: 2743018 num_examples: 997 download_size: 17332201 dataset_size: 2743018 - config_name: 19997_talk.politics.guns features: - name: text dtype: string splits: - name: train num_bytes: 2639343 num_examples: 1000 download_size: 17332201 dataset_size: 2639343 - config_name: 19997_talk.politics.mideast features: - name: text dtype: string splits: - name: train num_bytes: 3695931 num_examples: 1000 download_size: 17332201 dataset_size: 3695931 - config_name: 19997_talk.politics.misc features: - name: text dtype: string splits: - name: train num_bytes: 3169183 num_examples: 1000 download_size: 17332201 dataset_size: 3169183 - config_name: 19997_talk.religion.misc features: - name: text dtype: string splits: - name: train num_bytes: 2658700 num_examples: 1000 download_size: 17332201 dataset_size: 2658700 - config_name: bydate_alt.atheism features: - name: text dtype: string splits: - name: train num_bytes: 1042224 num_examples: 480 - name: test num_bytes: 702920 num_examples: 319 download_size: 14464277 dataset_size: 1745144 - config_name: bydate_comp.graphics features: - name: text dtype: string splits: - name: train num_bytes: 911665 num_examples: 584 - name: test num_bytes: 849632 num_examples: 389 download_size: 14464277 dataset_size: 1761297 - config_name: bydate_comp.os.ms-windows.misc features: - name: text dtype: string splits: - name: train num_bytes: 1770988 num_examples: 591 - name: test num_bytes: 706676 num_examples: 394 download_size: 14464277 dataset_size: 2477664 - config_name: bydate_comp.sys.ibm.pc.hardware features: - name: text dtype: string splits: - name: train num_bytes: 800446 num_examples: 590 - name: test num_bytes: 485310 num_examples: 392 download_size: 14464277 dataset_size: 1285756 - config_name: bydate_comp.sys.mac.hardware features: - name: text dtype: string splits: - name: train num_bytes: 696311 num_examples: 578 - name: test num_bytes: 468791 num_examples: 385 download_size: 14464277 dataset_size: 1165102 - config_name: bydate_comp.windows.x features: - name: text dtype: string splits: - name: train num_bytes: 1243463 num_examples: 593 - name: test num_bytes: 795366 num_examples: 395 download_size: 14464277 dataset_size: 2038829 - config_name: bydate_misc.forsale features: - name: text dtype: string splits: - name: train num_bytes: 611210 num_examples: 585 - name: test num_bytes: 415902 num_examples: 390 download_size: 14464277 dataset_size: 1027112 - config_name: bydate_rec.autos features: - name: text dtype: string splits: - name: train num_bytes: 860646 num_examples: 594 - name: test num_bytes: 535378 num_examples: 396 download_size: 14464277 dataset_size: 1396024 - config_name: bydate_rec.motorcycles features: - name: text dtype: string splits: - name: train num_bytes: 811151 num_examples: 598 - name: test num_bytes: 497735 num_examples: 398 download_size: 14464277 dataset_size: 1308886 - config_name: bydate_rec.sport.baseball features: - name: text dtype: string splits: - name: train num_bytes: 850740 num_examples: 597 - name: test num_bytes: 618609 num_examples: 397 download_size: 14464277 dataset_size: 1469349 - config_name: bydate_rec.sport.hockey features: - name: text dtype: string splits: - name: train num_bytes: 1189652 num_examples: 600 - name: test num_bytes: 666358 num_examples: 399 download_size: 14464277 dataset_size: 1856010 - config_name: bydate_sci.crypt features: - name: text dtype: string splits: - name: train num_bytes: 1502448 num_examples: 595 - name: test num_bytes: 657727 num_examples: 396 download_size: 14464277 dataset_size: 2160175 - config_name: bydate_sci.electronics features: - name: text dtype: string splits: - name: train num_bytes: 814856 num_examples: 591 - name: test num_bytes: 523095 num_examples: 393 download_size: 14464277 dataset_size: 1337951 - config_name: bydate_sci.med features: - name: text dtype: string splits: - name: train num_bytes: 1195201 num_examples: 594 - name: test num_bytes: 791826 num_examples: 396 download_size: 14464277 dataset_size: 1987027 - config_name: bydate_sci.space features: - name: text dtype: string splits: - name: train num_bytes: 1197965 num_examples: 593 - name: test num_bytes: 721771 num_examples: 394 download_size: 14464277 dataset_size: 1919736 - config_name: bydate_soc.religion.christian features: - name: text dtype: string splits: - name: train num_bytes: 1358047 num_examples: 599 - name: test num_bytes: 1003668 num_examples: 398 download_size: 14464277 dataset_size: 2361715 - config_name: bydate_talk.politics.guns features: - name: text dtype: string splits: - name: train num_bytes: 1313019 num_examples: 546 - name: test num_bytes: 701477 num_examples: 364 download_size: 14464277 dataset_size: 2014496 - config_name: bydate_talk.politics.mideast features: - name: text dtype: string splits: - name: train num_bytes: 1765833 num_examples: 564 - name: test num_bytes: 1236435 num_examples: 376 download_size: 14464277 dataset_size: 3002268 - config_name: bydate_talk.politics.misc features: - name: text dtype: string splits: - name: train num_bytes: 1328057 num_examples: 465 - name: test num_bytes: 853395 num_examples: 310 download_size: 14464277 dataset_size: 2181452 - config_name: bydate_talk.religion.misc features: - name: text dtype: string splits: - name: train num_bytes: 835761 num_examples: 377 - name: test num_bytes: 598452 num_examples: 251 download_size: 14464277 dataset_size: 1434213 --- # Dataset Card for "newsgroup" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [http://qwone.com/~jason/20Newsgroups/](http://qwone.com/~jason/20Newsgroups/) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [NewsWeeder: Learning to Filter Netnews](https://doi.org/10.1016/B978-1-55860-377-6.50048-7) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 929.27 MB - **Size of the generated dataset:** 124.41 MB - **Total amount of disk used:** 1.05 GB ### Dataset Summary The 20 Newsgroups data set is a collection of approximately 20,000 newsgroup documents, partitioned (nearly) evenly across 20 different newsgroups. To the best of my knowledge, it was originally collected by Ken Lang, probably for his Newsweeder: Learning to filter netnews paper, though he does not explicitly mention this collection. The 20 newsgroups collection has become a popular data set for experiments in text applications of machine learning techniques, such as text classification and text clustering. does not include cross-posts and includes only the "From" and "Subject" headers. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### 18828_alt.atheism - **Size of downloaded dataset files:** 14.67 MB - **Size of the generated dataset:** 1.67 MB - **Total amount of disk used:** 16.34 MB An example of 'train' looks as follows. ``` ``` #### 18828_comp.graphics - **Size of downloaded dataset files:** 14.67 MB - **Size of the generated dataset:** 1.66 MB - **Total amount of disk used:** 16.33 MB An example of 'train' looks as follows. ``` ``` #### 18828_comp.os.ms-windows.misc - **Size of downloaded dataset files:** 14.67 MB - **Size of the generated dataset:** 2.38 MB - **Total amount of disk used:** 17.05 MB An example of 'train' looks as follows. ``` ``` #### 18828_comp.sys.ibm.pc.hardware - **Size of downloaded dataset files:** 14.67 MB - **Size of the generated dataset:** 1.18 MB - **Total amount of disk used:** 15.85 MB An example of 'train' looks as follows. ``` ``` #### 18828_comp.sys.mac.hardware - **Size of downloaded dataset files:** 14.67 MB - **Size of the generated dataset:** 1.06 MB - **Total amount of disk used:** 15.73 MB An example of 'train' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### 18828_alt.atheism - `text`: a `string` feature. #### 18828_comp.graphics - `text`: a `string` feature. #### 18828_comp.os.ms-windows.misc - `text`: a `string` feature. #### 18828_comp.sys.ibm.pc.hardware - `text`: a `string` feature. #### 18828_comp.sys.mac.hardware - `text`: a `string` feature. ### Data Splits | name |train| |------------------------------|----:| |18828_alt.atheism | 799| |18828_comp.graphics | 973| |18828_comp.os.ms-windows.misc | 985| |18828_comp.sys.ibm.pc.hardware| 982| |18828_comp.sys.mac.hardware | 961| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @incollection{LANG1995331, title = {NewsWeeder: Learning to Filter Netnews}, editor = {Armand Prieditis and Stuart Russell}, booktitle = {Machine Learning Proceedings 1995}, publisher = {Morgan Kaufmann}, address = {San Francisco (CA)}, pages = {331-339}, year = {1995}, isbn = {978-1-55860-377-6}, doi = {https://doi.org/10.1016/B978-1-55860-377-6.50048-7}, url = {https://www.sciencedirect.com/science/article/pii/B9781558603776500487}, author = {Ken Lang}, } ``` ### Contributions Thanks to [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq) for adding this dataset.

indic_glue

--- annotations_creators: - other language_creators: - found language: - as - bn - en - gu - hi - kn - ml - mr - or - pa - ta - te license: - other multilinguality: - multilingual size_categories: - 100K<n<1M source_datasets: - extended|other task_categories: - text-classification - token-classification - multiple-choice task_ids: - topic-classification - natural-language-inference - sentiment-analysis - semantic-similarity-scoring - named-entity-recognition - multiple-choice-qa pretty_name: IndicGLUE tags: - discourse-mode-classification - paraphrase-identification - cross-lingual-similarity - headline-classification dataset_info: - config_name: wnli.en features: - name: hypothesis dtype: string - name: premise dtype: string - name: label dtype: class_label: names: '0': not_entailment '1': entailment '2': None splits: - name: train num_bytes: 104577 num_examples: 635 - name: validation num_bytes: 11886 num_examples: 71 - name: test num_bytes: 37305 num_examples: 146 download_size: 591249 dataset_size: 153768 - config_name: wnli.hi features: - name: hypothesis dtype: string - name: premise dtype: string - name: label dtype: class_label: names: '0': not_entailment '1': entailment '2': None splits: - name: train num_bytes: 253342 num_examples: 635 - name: validation num_bytes: 28684 num_examples: 71 - name: test num_bytes: 90831 num_examples: 146 download_size: 591249 dataset_size: 372857 - config_name: wnli.gu features: - name: hypothesis dtype: string - name: premise dtype: string - name: label dtype: class_label: names: '0': not_entailment '1': entailment '2': None splits: - name: train num_bytes: 251562 num_examples: 635 - name: validation num_bytes: 28183 num_examples: 71 - name: test num_bytes: 94586 num_examples: 146 download_size: 591249 dataset_size: 374331 - config_name: wnli.mr features: - name: hypothesis dtype: string - name: premise dtype: string - name: label dtype: class_label: names: '0': not_entailment '1': entailment '2': None splits: - name: train num_bytes: 256657 num_examples: 635 - name: validation num_bytes: 29226 num_examples: 71 - name: test num_bytes: 97136 num_examples: 146 download_size: 591249 dataset_size: 383019 - config_name: copa.en features: - name: premise dtype: string - name: choice1 dtype: string - name: choice2 dtype: string - name: question dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 46049 num_examples: 400 - name: validation num_bytes: 11695 num_examples: 100 - name: test num_bytes: 55862 num_examples: 500 download_size: 757679 dataset_size: 113606 - config_name: copa.hi features: - name: premise dtype: string - name: choice1 dtype: string - name: choice2 dtype: string - name: question dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 93392 num_examples: 362 - name: validation num_bytes: 23575 num_examples: 88 - name: test num_bytes: 112846 num_examples: 449 download_size: 757679 dataset_size: 229813 - config_name: copa.gu features: - name: premise dtype: string - name: choice1 dtype: string - name: choice2 dtype: string - name: question dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 92113 num_examples: 362 - name: validation num_bytes: 23466 num_examples: 88 - name: test num_bytes: 110013 num_examples: 448 download_size: 757679 dataset_size: 225592 - config_name: copa.mr features: - name: premise dtype: string - name: choice1 dtype: string - name: choice2 dtype: string - name: question dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 93457 num_examples: 362 - name: validation num_bytes: 23890 num_examples: 88 - name: test num_bytes: 112071 num_examples: 449 download_size: 757679 dataset_size: 229418 - config_name: sna.bn features: - name: text dtype: string - name: label dtype: class_label: names: '0': kolkata '1': state '2': national '3': sports '4': entertainment '5': international splits: - name: train num_bytes: 46070054 num_examples: 11284 - name: validation num_bytes: 5648130 num_examples: 1411 - name: test num_bytes: 5799983 num_examples: 1411 download_size: 11803096 dataset_size: 57518167 - 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config_name: csqa.hi features: - name: question dtype: string - name: answer dtype: string - name: category dtype: string - name: title dtype: string - name: options sequence: string - name: out_of_context_options sequence: string splits: - name: test num_bytes: 40409475 num_examples: 35140 download_size: 65099316 dataset_size: 40409475 - config_name: csqa.kn features: - name: question dtype: string - name: answer dtype: string - name: category dtype: string - name: title dtype: string - name: options sequence: string - name: out_of_context_options sequence: string splits: - name: test num_bytes: 21199880 num_examples: 13666 download_size: 65099316 dataset_size: 21199880 - config_name: csqa.ml features: - name: question dtype: string - name: answer dtype: string - name: category dtype: string - name: title dtype: string - name: options sequence: string - name: out_of_context_options sequence: string splits: - name: test num_bytes: 47220932 num_examples: 26537 download_size: 65099316 dataset_size: 47220932 - config_name: csqa.mr features: - name: question dtype: string - name: answer dtype: string - name: category dtype: string - name: title dtype: string - name: options sequence: string - name: out_of_context_options sequence: string splits: - name: test num_bytes: 13667238 num_examples: 11370 download_size: 65099316 dataset_size: 13667238 - config_name: csqa.or features: - name: question dtype: string - name: answer dtype: string - name: category dtype: string - name: title dtype: string - name: options sequence: string - name: out_of_context_options sequence: string splits: - name: test num_bytes: 2562397 num_examples: 1975 download_size: 65099316 dataset_size: 2562397 - config_name: csqa.pa features: - name: question dtype: string - name: answer dtype: string - name: category dtype: string - name: title dtype: string - name: options sequence: string - name: out_of_context_options sequence: string splits: - name: test num_bytes: 5806129 num_examples: 5667 download_size: 65099316 dataset_size: 5806129 - config_name: csqa.ta features: - name: question dtype: string - name: answer dtype: string - name: category dtype: string - name: title dtype: string - name: options sequence: string - name: out_of_context_options sequence: string splits: - name: test num_bytes: 61868609 num_examples: 38590 download_size: 65099316 dataset_size: 61868609 - config_name: csqa.te features: - name: question dtype: string - name: answer dtype: string - name: category dtype: string - name: title dtype: string - name: options sequence: string - name: out_of_context_options sequence: string splits: - name: test num_bytes: 58785157 num_examples: 41338 download_size: 65099316 dataset_size: 58785157 - config_name: wstp.as features: - name: sectionText dtype: string - name: correctTitle dtype: string - name: titleA dtype: string - name: titleB dtype: string - name: titleC dtype: string - name: titleD dtype: string - name: url dtype: string splits: - name: train num_bytes: 13581364 num_examples: 5000 - name: validation num_bytes: 1698996 num_examples: 625 - 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name: test num_bytes: 702377 num_examples: 310 download_size: 1742048 dataset_size: 8229508 - config_name: iitp-pr.hi features: - name: text dtype: string - name: label dtype: class_label: names: '0': negative '1': neutral '2': positive splits: - name: train num_bytes: 945593 num_examples: 4182 - name: validation num_bytes: 120104 num_examples: 523 - name: test num_bytes: 121914 num_examples: 523 download_size: 266545 dataset_size: 1187611 - config_name: actsa-sc.te features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 1370911 num_examples: 4328 - name: validation num_bytes: 166093 num_examples: 541 - name: test num_bytes: 168295 num_examples: 541 download_size: 378882 dataset_size: 1705299 - config_name: md.hi features: - name: sentence dtype: string - name: discourse_mode dtype: string - name: story_number dtype: int32 - name: id dtype: int32 splits: - name: train num_bytes: 1672117 num_examples: 7974 - name: validation num_bytes: 211195 num_examples: 997 - name: test num_bytes: 210183 num_examples: 997 download_size: 1048441 dataset_size: 2093495 - config_name: wiki-ner.as features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-LOC '1': B-ORG '2': B-PER '3': I-LOC '4': I-ORG '5': I-PER '6': O - name: additional_info sequence: sequence: string splits: - name: train num_bytes: 375007 num_examples: 1021 - name: validation num_bytes: 49336 num_examples: 157 - name: test num_bytes: 50480 num_examples: 160 download_size: 5980272 dataset_size: 474823 - config_name: wiki-ner.bn features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-LOC '1': B-ORG '2': B-PER '3': I-LOC '4': I-ORG '5': I-PER '6': O - name: additional_info sequence: sequence: string splits: - name: train num_bytes: 7502896 num_examples: 20223 - name: validation num_bytes: 988707 num_examples: 2985 - name: test num_bytes: 985965 num_examples: 2690 download_size: 5980272 dataset_size: 9477568 - config_name: wiki-ner.gu features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-LOC '1': B-ORG '2': B-PER '3': I-LOC '4': I-ORG '5': I-PER '6': O - name: additional_info sequence: sequence: string splits: - name: train num_bytes: 1571612 num_examples: 2343 - name: validation num_bytes: 192828 num_examples: 297 - name: test num_bytes: 197901 num_examples: 255 download_size: 5980272 dataset_size: 1962341 - config_name: wiki-ner.hi features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-LOC '1': B-ORG '2': B-PER '3': I-LOC '4': I-ORG '5': I-PER '6': O - name: additional_info sequence: sequence: string splits: - name: train num_bytes: 3762529 num_examples: 9463 - name: validation num_bytes: 468702 num_examples: 1114 - name: test num_bytes: 475277 num_examples: 1256 download_size: 5980272 dataset_size: 4706508 - config_name: wiki-ner.kn features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-LOC '1': B-ORG '2': B-PER '3': I-LOC '4': I-ORG '5': I-PER '6': O - name: additional_info sequence: sequence: string splits: - name: train num_bytes: 1352051 num_examples: 2679 - name: validation num_bytes: 179562 num_examples: 412 - name: test num_bytes: 180815 num_examples: 476 download_size: 5980272 dataset_size: 1712428 - config_name: wiki-ner.ml features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-LOC '1': B-ORG '2': B-PER '3': I-LOC '4': I-ORG '5': I-PER '6': O - name: additional_info sequence: sequence: string splits: - name: train num_bytes: 7678935 num_examples: 15620 - name: validation num_bytes: 969971 num_examples: 2067 - name: test num_bytes: 991126 num_examples: 2042 download_size: 5980272 dataset_size: 9640032 - config_name: wiki-ner.mr features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-LOC '1': B-ORG '2': B-PER '3': I-LOC '4': I-ORG '5': I-PER '6': O - name: additional_info sequence: sequence: string splits: - name: train num_bytes: 5431537 num_examples: 12151 - name: validation num_bytes: 701661 num_examples: 1498 - name: test num_bytes: 655706 num_examples: 1329 download_size: 5980272 dataset_size: 6788904 - config_name: wiki-ner.or features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-LOC '1': B-ORG '2': B-PER '3': I-LOC '4': I-ORG '5': I-PER '6': O - name: additional_info sequence: sequence: string splits: - name: train num_bytes: 493782 num_examples: 1077 - name: validation num_bytes: 58592 num_examples: 132 - name: test num_bytes: 62235 num_examples: 153 download_size: 5980272 dataset_size: 614609 - config_name: wiki-ner.pa features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-LOC '1': B-ORG '2': B-PER '3': I-LOC '4': I-ORG '5': I-PER '6': O - name: additional_info sequence: sequence: string splits: - name: train num_bytes: 520268 num_examples: 1408 - name: validation num_bytes: 61194 num_examples: 186 - name: test num_bytes: 61812 num_examples: 179 download_size: 5980272 dataset_size: 643274 - config_name: wiki-ner.ta features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-LOC '1': B-ORG '2': B-PER '3': I-LOC '4': I-ORG '5': I-PER '6': O - name: additional_info sequence: sequence: string splits: - name: train num_bytes: 10117152 num_examples: 20466 - name: validation num_bytes: 1267212 num_examples: 2586 - name: test num_bytes: 1321650 num_examples: 2611 download_size: 5980272 dataset_size: 12706014 - config_name: wiki-ner.te features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-LOC '1': B-ORG '2': B-PER '3': I-LOC '4': I-ORG '5': I-PER '6': O - name: additional_info sequence: sequence: string splits: - name: train num_bytes: 3881235 num_examples: 7978 - name: validation num_bytes: 458533 num_examples: 841 - name: test num_bytes: 507830 num_examples: 1110 download_size: 5980272 dataset_size: 4847598 --- # Dataset Card for "indic_glue" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://ai4bharat.iitm.ac.in/indic-glue - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages](https://aclanthology.org/2020.findings-emnlp.445/) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 3.51 GB - **Size of the generated dataset:** 1.65 GB - **Total amount of disk used:** 5.16 GB ### Dataset Summary IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te. The Winograd Schema Challenge (Levesque et al., 2011) is a reading comprehension task in which a system must read a sentence with a pronoun and select the referent of that pronoun from a list of choices. The examples are manually constructed to foil simple statistical methods: Each one is contingent on contextual information provided by a single word or phrase in the sentence. To convert the problem into sentence pair classification, we construct sentence pairs by replacing the ambiguous pronoun with each possible referent. The task is to predict if the sentence with the pronoun substituted is entailed by the original sentence. We use a small evaluation set consisting of new examples derived from fiction books that was shared privately by the authors of the original corpus. While the included training set is balanced between two classes, the test set is imbalanced between them (65% not entailment). Also, due to a data quirk, the development set is adversarial: hypotheses are sometimes shared between training and development examples, so if a model memorizes the training examples, they will predict the wrong label on corresponding development set example. As with QNLI, each example is evaluated separately, so there is not a systematic correspondence between a model's score on this task and its score on the unconverted original task. We call converted dataset WNLI (Winograd NLI). This dataset is translated and publicly released for 3 Indian languages by AI4Bharat. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### actsa-sc.te - **Size of downloaded dataset files:** 0.38 MB - **Size of the generated dataset:** 1.71 MB - **Total amount of disk used:** 2.09 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "label": 0, "text": "\"ప్రయాణాల్లో ఉన్నవారికోసం బస్ స్టేషన్లు, రైల్వే స్టేషన్లలో పల్స్పోలియో బూతులను ఏర్పాటు చేసి చిన్నారులకు పోలియో చుక్కలు వేసేలా ఏర..." } ``` #### bbca.hi - **Size of downloaded dataset files:** 5.77 MB - **Size of the generated dataset:** 27.63 MB - **Total amount of disk used:** 33.40 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "label": "pakistan", "text": "\"नेटिजन यानि इंटरनेट पर सक्रिय नागरिक अब ट्विटर पर सरकार द्वारा लगाए प्रतिबंधों के समर्थन या विरोध में अपने विचार व्यक्त करते है..." } ``` #### copa.en - **Size of downloaded dataset files:** 0.75 MB - **Size of the generated dataset:** 0.12 MB - **Total amount of disk used:** 0.87 MB An example of 'validation' looks as follows. ``` { "choice1": "I swept the floor in the unoccupied room.", "choice2": "I shut off the light in the unoccupied room.", "label": 1, "premise": "I wanted to conserve energy.", "question": "effect" } ``` #### copa.gu - **Size of downloaded dataset files:** 0.75 MB - **Size of the generated dataset:** 0.23 MB - **Total amount of disk used:** 0.99 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "choice1": "\"સ્ત્રી જાણતી હતી કે તેનો મિત્ર મુશ્કેલ સમયમાંથી પસાર થઈ રહ્યો છે.\"...", "choice2": "\"મહિલાને લાગ્યું કે તેના મિત્રએ તેની દયાળુ લાભ લીધો છે.\"...", "label": 0, "premise": "મહિલાએ તેના મિત્રની મુશ્કેલ વર્તન સહન કરી.", "question": "cause" } ``` #### copa.hi - **Size of downloaded dataset files:** 0.75 MB - **Size of the generated dataset:** 0.23 MB - **Total amount of disk used:** 0.99 MB An example of 'validation' looks as follows. ``` { "choice1": "मैंने उसका प्रस्ताव ठुकरा दिया।", "choice2": "उन्होंने मुझे उत्पाद खरीदने के लिए राजी किया।", "label": 0, "premise": "मैंने सेल्समैन की पिच पर शक किया।", "question": "effect" } ``` ### Data Fields The data fields are the same among all splits. #### actsa-sc.te - `text`: a `string` feature. - `label`: a classification label, with possible values including `positive` (0), `negative` (1). #### bbca.hi - `label`: a `string` feature. - `text`: a `string` feature. #### copa.en - `premise`: a `string` feature. - `choice1`: a `string` feature. - `choice2`: a `string` feature. - `question`: a `string` feature. - `label`: a `int32` feature. #### copa.gu - `premise`: a `string` feature. - `choice1`: a `string` feature. - `choice2`: a `string` feature. - `question`: a `string` feature. - `label`: a `int32` feature. #### copa.hi - `premise`: a `string` feature. - `choice1`: a `string` feature. - `choice2`: a `string` feature. - `question`: a `string` feature. - `label`: a `int32` feature. ### Data Splits #### actsa-sc.te | |train|validation|test| |-----------|----:|---------:|---:| |actsa-sc.te| 4328| 541| 541| #### bbca.hi | |train|test| |-------|----:|---:| |bbca.hi| 3467| 866| #### copa.en | |train|validation|test| |-------|----:|---------:|---:| |copa.en| 400| 100| 500| #### copa.gu | |train|validation|test| |-------|----:|---------:|---:| |copa.gu| 362| 88| 448| #### copa.hi | |train|validation|test| |-------|----:|---------:|---:| |copa.hi| 362| 88| 449| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{kakwani-etal-2020-indicnlpsuite, title = "{I}ndic{NLPS}uite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for {I}ndian Languages", author = "Kakwani, Divyanshu and Kunchukuttan, Anoop and Golla, Satish and N.C., Gokul and Bhattacharyya, Avik and Khapra, Mitesh M. and Kumar, Pratyush", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.findings-emnlp.445", doi = "10.18653/v1/2020.findings-emnlp.445", pages = "4948--4961", } @inproceedings{Levesque2011TheWS, title={The Winograd Schema Challenge}, author={H. Levesque and E. Davis and L. Morgenstern}, booktitle={KR}, year={2011} } ``` ### Contributions Thanks to [@sumanthd17](https://github.com/sumanthd17) for adding this dataset.

tau/scrolls

tau

--- language: - en task_categories: - question-answering - summarization - text-generation task_ids: - multiple-choice-qa - natural-language-inference paperswithcode_id: scrolls configs: - gov_report - summ_screen_fd - qmsum - qasper - narrative_qa - quality - contract_nli tags: - query-based-summarization - long-texts --- ## Dataset Description - **Homepage:** [SCROLLS](https://www.scrolls-benchmark.com/) - **Repository:** [SCROLLS Github repository](https://github.com/tau-nlp/scrolls) - **Paper:** [SCROLLS: Standardized CompaRison Over Long Language Sequences ](https://arxiv.org/pdf/2201.03533.pdf) - **Leaderboard:** [Leaderboard](https://www.scrolls-benchmark.com/leaderboard) - **Point of Contact:** [scrolls-benchmark-contact@googlegroups.com](scrolls-benchmark-contact@googlegroups.com) # Dataset Card for SCROLLS ## Overview SCROLLS is a suite of datasets that require synthesizing information over long texts. The benchmark includes seven natural language tasks across multiple domains, including summarization, question answering, and natural language inference. ## Leaderboard The SCROLLS benchmark leaderboard can be found [here](https://www.scrolls-benchmark.com/leaderboard). ## Tasks SCROLLS comprises the following tasks: #### GovReport ([Huang et al., 2021](https://arxiv.org/pdf/2104.02112.pdf)) GovReport is a summarization dataset of reports addressing various national policy issues published by the Congressional Research Service and the U.S. Government Accountability Office, where each document is paired with a hand-written executive summary. The reports and their summaries are longer than their equivalents in other popular long-document summarization datasets; for example, GovReport's documents are approximately 1.5 and 2.5 times longer than the documents in Arxiv and PubMed, respectively. #### SummScreenFD ([Chen et al., 2021](https://arxiv.org/pdf/2104.07091.pdf)) SummScreenFD is a summarization dataset in the domain of TV shows (e.g. Friends, Game of Thrones). Given a transcript of a specific episode, the goal is to produce the episode's recap. The original dataset is divided into two complementary subsets, based on the source of its community contributed transcripts. For SCROLLS, we use the ForeverDreaming (FD) subset, as it incorporates 88 different shows, making it a more diverse alternative to the TV MegaSite (TMS) subset, which has only 10 shows. Community-authored recaps for the ForeverDreaming transcripts were collected from English Wikipedia and TVMaze. #### QMSum ([Zhong et al., 2021](https://arxiv.org/pdf/2104.05938.pdf)) QMSum is a query-based summarization dataset, consisting of 232 meetings transcripts from multiple domains. The corpus covers academic group meetings at the International Computer Science Institute and their summaries, industrial product meetings for designing a remote control, and committee meetings of the Welsh and Canadian Parliaments, dealing with a variety of public policy issues. Annotators were tasked with writing queries about the broad contents of the meetings, as well as specific questions about certain topics or decisions, while ensuring that the relevant text for answering each query spans at least 200 words or 10 turns. #### NarrativeQA ([Kočiský et al., 2018](https://arxiv.org/pdf/1712.07040.pdf)) NarrativeQA (Kočiský et al., 2021) is an established question answering dataset over entire books from Project Gutenberg and movie scripts from different websites. Annotators were given summaries of the books and scripts obtained from Wikipedia, and asked to generate question-answer pairs, resulting in about 30 questions and answers for each of the 1,567 books and scripts. They were encouraged to use their own words rather then copying, and avoid asking yes/no questions or ones about the cast. Each question was then answered by an additional annotator, providing each question with two reference answers (unless both answers are identical). #### Qasper ([Dasigi et al., 2021](https://arxiv.org/pdf/2105.03011.pdf)) Qasper is a question answering dataset over NLP papers filtered from the Semantic Scholar Open Research Corpus (S2ORC). Questions were written by NLP practitioners after reading only the title and abstract of the papers, while another set of NLP practitioners annotated the answers given the entire document. Qasper contains abstractive, extractive, and yes/no questions, as well as unanswerable ones. #### QuALITY ([Pang et al., 2021](https://arxiv.org/pdf/2112.08608.pdf)) QuALITY is a multiple-choice question answering dataset over articles and stories sourced from Project Gutenberg, the Open American National Corpus, and more. Experienced writers wrote questions and distractors, and were incentivized to write answerable, unambiguous questions such that in order to correctly answer them, human annotators must read large portions of the given document. Reference answers were then calculated using the majority vote between of the annotators and writer's answers. To measure the difficulty of their questions, Pang et al. conducted a speed validation process, where another set of annotators were asked to answer questions given only a short period of time to skim through the document. As a result, 50% of the questions in QuALITY are labeled as hard, i.e. the majority of the annotators in the speed validation setting chose the wrong answer. #### ContractNLI ([Koreeda and Manning, 2021](https://arxiv.org/pdf/2110.01799.pdf)) Contract NLI is a natural language inference dataset in the legal domain. Given a non-disclosure agreement (the premise), the task is to predict whether a particular legal statement (the hypothesis) is entailed, not entailed (neutral), or cannot be entailed (contradiction) from the contract. The NDAs were manually picked after simple filtering from the Electronic Data Gathering, Analysis, and Retrieval system (EDGAR) and Google. The dataset contains a total of 607 contracts and 17 unique hypotheses, which were combined to produce the dataset's 10,319 examples. ## Data Fields All the datasets in the benchmark are in the same input-output format - `input`: a `string` feature. The input document. - `output`: a `string` feature. The target. - `id`: a `string` feature. Unique per input. - `pid`: a `string` feature. Unique per input-output pair (can differ from 'id' in NarrativeQA and Qasper, where there is more then one valid target). ## Citation If you use the SCROLLS data, **please make sure to cite all of the original dataset papers.** [[bibtex](https://scrolls-tau.s3.us-east-2.amazonaws.com/scrolls_datasets.bib)] ``` @inproceedings{shaham-etal-2022-scrolls, title = "{SCROLLS}: Standardized {C}ompa{R}ison Over Long Language Sequences", author = "Shaham, Uri and Segal, Elad and Ivgi, Maor and Efrat, Avia and Yoran, Ori and Haviv, Adi and Gupta, Ankit and Xiong, Wenhan and Geva, Mor and Berant, Jonathan and Levy, Omer", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, United Arab Emirates", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.emnlp-main.823", pages = "12007--12021", } ```

mteb/sickr-sts

mteb

--- language: - en ---

dbpedia_14

--- annotations_creators: - machine-generated language_creators: - crowdsourced language: - en license: - cc-by-sa-3.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - topic-classification paperswithcode_id: dbpedia pretty_name: DBpedia dataset_info: features: - name: label dtype: class_label: names: '0': Company '1': EducationalInstitution '2': Artist '3': Athlete '4': OfficeHolder '5': MeanOfTransportation '6': Building '7': NaturalPlace '8': Village '9': Animal '10': Plant '11': Album '12': Film '13': WrittenWork - name: title dtype: string - name: content dtype: string config_name: dbpedia_14 splits: - name: train num_bytes: 178428970 num_examples: 560000 - name: test num_bytes: 22310285 num_examples: 70000 download_size: 68341743 dataset_size: 200739255 --- # Dataset Card for DBpedia14 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [DBpedia14 homepage](https://wiki.dbpedia.org/develop/datasets) - **Repository:** [DBpedia14 repository](https://github.com/dbpedia/extraction-framework) - **Paper:** [DBpedia--a large-scale, multilingual knowledge base extracted from Wikipedia](https://content.iospress.com/articles/semantic-web/sw134) - **Point of Contact:** [Xiang Zhang](mailto:xiang.zhang@nyu.edu) ### Dataset Summary The DBpedia ontology classification dataset is constructed by picking 14 non-overlapping classes from DBpedia 2014. They are listed in classes.txt. From each of thse 14 ontology classes, we randomly choose 40,000 training samples and 5,000 testing samples. Therefore, the total size of the training dataset is 560,000 and testing dataset 70,000. There are 3 columns in the dataset (same for train and test splits), corresponding to class index (1 to 14), title and content. The title and content are escaped using double quotes ("), and any internal double quote is escaped by 2 double quotes (""). There are no new lines in title or content. ### Supported Tasks and Leaderboards - `text-classification`, `topic-classification`: The dataset is mainly used for text classification: given the content and the title, predict the correct topic. ### Languages Although DBpedia is a multilingual knowledge base, the DBpedia14 extract contains English data mainly, other languages may appear (e.g. a film whose title is origanlly not English). ## Dataset Structure ### Data Instances A typical data point, comprises of a title, a content and the corresponding label. An example from the DBpedia test set looks as follows: ``` { 'title':'', 'content':" TY KU /taɪkuː/ is an American alcoholic beverage company that specializes in sake and other spirits. The privately-held company was founded in 2004 and is headquartered in New York City New York. While based in New York TY KU's beverages are made in Japan through a joint venture with two sake breweries. Since 2011 TY KU's growth has extended its products into all 50 states.", 'label':0 } ``` ### Data Fields - 'title': a string containing the title of the document - escaped using double quotes (") and any internal double quote is escaped by 2 double quotes (""). - 'content': a string containing the body of the document - escaped using double quotes (") and any internal double quote is escaped by 2 double quotes (""). - 'label': one of the 14 possible topics. ### Data Splits The data is split into a training and test set. For each of the 14 classes we have 40,000 training samples and 5,000 testing samples. Therefore, the total size of the training dataset is 560,000 and testing dataset 70,000. ## Dataset Creation ### Curation Rationale The DBPedia ontology classification dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu), licensed under the terms of the Creative Commons Attribution-ShareAlike License and the GNU Free Documentation License. It is used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015). ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The DBPedia ontology classification dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu), licensed under the terms of the Creative Commons Attribution-ShareAlike License and the GNU Free Documentation License. It is used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015). ### Licensing Information The DBPedia ontology classification dataset is licensed under the terms of the Creative Commons Attribution-ShareAlike License and the GNU Free Documentation License. ### Citation Information Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015). Lehmann, Jens, Robert Isele, Max Jakob, Anja Jentzsch, Dimitris Kontokostas, Pablo N. Mendes, Sebastian Hellmann et al. "DBpedia–a large-scale, multilingual knowledge base extracted from Wikipedia." Semantic web 6, no. 2 (2015): 167-195. ### Contributions Thanks to [@hfawaz](https://github.com/hfawaz) for adding this dataset.

wino_bias

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - mit multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - token-classification task_ids: - coreference-resolution paperswithcode_id: winobias pretty_name: WinoBias dataset_info: - config_name: wino_bias features: - name: document_id dtype: string - name: part_number dtype: string - name: word_number sequence: int32 - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': '"' '1': '''''' '2': '#' '3': $ '4': ( '5': ) '6': ',' '7': . '8': ':' '9': '``' '10': CC '11': CD '12': DT '13': EX '14': FW '15': IN '16': JJ '17': JJR '18': JJS '19': LS '20': MD '21': NN '22': NNP '23': NNPS '24': NNS '25': NN|SYM '26': PDT '27': POS '28': PRP '29': PRP$ '30': RB '31': RBR '32': RBS '33': RP '34': SYM '35': TO '36': UH '37': VB '38': VBD '39': VBG '40': VBN '41': VBP '42': VBZ '43': WDT '44': WP '45': WP$ '46': WRB '47': HYPH '48': XX '49': NFP '50': AFX '51': ADD '52': -LRB- '53': -RRB- - name: parse_bit sequence: string - name: predicate_lemma sequence: string - name: predicate_framenet_id sequence: string - name: word_sense sequence: string - name: speaker sequence: string - name: ner_tags sequence: class_label: names: '0': B-PERSON '1': I-PERSON '2': B-NORP '3': I-NORP '4': B-FAC '5': I-FAC '6': B-ORG '7': I-ORG '8': B-GPE '9': I-GPE '10': B-LOC '11': I-LOC '12': B-PRODUCT '13': I-PRODUCT '14': B-EVENT '15': I-EVENT '16': B-WORK_OF_ART '17': I-WORK_OF_ART '18': B-LAW '19': I-LAW '20': B-LANGUAGE '21': I-LANGUAGE '22': B-DATE '23': I-DATE '24': B-TIME '25': I-TIME '26': B-PERCENT '27': I-PERCENT '28': B-MONEY '29': I-MONEY '30': B-QUANTITY '31': I-QUANTITY '32': B-ORDINAL '33': I-ORDINAL '34': B-CARDINAL '35': I-CARDINAL '36': '*' '37': '0' - name: verbal_predicates sequence: string splits: - name: train num_bytes: 173899234 num_examples: 150335 download_size: 268725744 dataset_size: 173899234 - config_name: type1_pro features: - name: document_id dtype: string - name: part_number dtype: string - name: word_number sequence: int32 - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': '"' '1': '''''' '2': '#' '3': $ '4': ( '5': ) '6': ',' '7': . '8': ':' '9': '``' '10': CC '11': CD '12': DT '13': EX '14': FW '15': IN '16': JJ '17': JJR '18': JJS '19': LS '20': MD '21': NN '22': NNP '23': NNPS '24': NNS '25': NN|SYM '26': PDT '27': POS '28': PRP '29': PRP$ '30': RB '31': RBR '32': RBS '33': RP '34': SYM '35': TO '36': UH '37': VB '38': VBD '39': VBG '40': VBN '41': VBP '42': VBZ '43': WDT '44': WP '45': WP$ '46': WRB '47': HYPH '48': XX '49': NFP '50': AFX '51': ADD '52': -LRB- '53': -RRB- '54': '-' - name: parse_bit sequence: string - name: predicate_lemma sequence: string - name: predicate_framenet_id sequence: string - name: word_sense sequence: string - name: speaker sequence: string - name: ner_tags sequence: class_label: names: '0': B-PERSON '1': I-PERSON '2': B-NORP '3': I-NORP '4': B-FAC '5': I-FAC '6': B-ORG '7': I-ORG '8': B-GPE '9': I-GPE '10': B-LOC '11': I-LOC '12': B-PRODUCT '13': I-PRODUCT '14': B-EVENT '15': I-EVENT '16': B-WORK_OF_ART '17': I-WORK_OF_ART '18': B-LAW '19': I-LAW '20': B-LANGUAGE '21': I-LANGUAGE '22': B-DATE '23': I-DATE '24': B-TIME '25': I-TIME '26': B-PERCENT '27': I-PERCENT '28': B-MONEY '29': I-MONEY '30': B-QUANTITY '31': I-QUANTITY '32': B-ORDINAL '33': I-ORDINAL '34': B-CARDINAL '35': I-CARDINAL '36': '*' '37': '0' '38': '-' - name: verbal_predicates sequence: string - name: coreference_clusters sequence: string splits: - name: validation num_bytes: 379380 num_examples: 396 - name: test num_bytes: 402041 num_examples: 396 download_size: 846198 dataset_size: 781421 - config_name: type1_anti features: - name: document_id dtype: string - name: part_number dtype: string - name: word_number sequence: int32 - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': '"' '1': '''''' '2': '#' '3': $ '4': ( '5': ) '6': ',' '7': . '8': ':' '9': '``' '10': CC '11': CD '12': DT '13': EX '14': FW '15': IN '16': JJ '17': JJR '18': JJS '19': LS '20': MD '21': NN '22': NNP '23': NNPS '24': NNS '25': NN|SYM '26': PDT '27': POS '28': PRP '29': PRP$ '30': RB '31': RBR '32': RBS '33': RP '34': SYM '35': TO '36': UH '37': VB '38': VBD '39': VBG '40': VBN '41': VBP '42': VBZ '43': WDT '44': WP '45': WP$ '46': WRB '47': HYPH '48': XX '49': NFP '50': AFX '51': ADD '52': -LRB- '53': -RRB- '54': '-' - name: parse_bit sequence: string - name: predicate_lemma sequence: string - name: predicate_framenet_id sequence: string - name: word_sense sequence: string - name: speaker sequence: string - name: ner_tags sequence: class_label: names: '0': B-PERSON '1': I-PERSON '2': B-NORP '3': I-NORP '4': B-FAC '5': I-FAC '6': B-ORG '7': I-ORG '8': B-GPE '9': I-GPE '10': B-LOC '11': I-LOC '12': B-PRODUCT '13': I-PRODUCT '14': B-EVENT '15': I-EVENT '16': B-WORK_OF_ART '17': I-WORK_OF_ART '18': B-LAW '19': I-LAW '20': B-LANGUAGE '21': I-LANGUAGE '22': B-DATE '23': I-DATE '24': B-TIME '25': I-TIME '26': B-PERCENT '27': I-PERCENT '28': B-MONEY '29': I-MONEY '30': B-QUANTITY '31': I-QUANTITY '32': B-ORDINAL '33': I-ORDINAL '34': B-CARDINAL '35': I-CARDINAL '36': '*' '37': '0' '38': '-' - name: verbal_predicates sequence: string - name: coreference_clusters sequence: string splits: - name: validation num_bytes: 380846 num_examples: 396 - name: test num_bytes: 403229 num_examples: 396 download_size: 894311 dataset_size: 784075 - config_name: type2_pro features: - name: document_id dtype: string - name: part_number dtype: string - name: word_number sequence: int32 - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': '"' '1': '''''' '2': '#' '3': $ '4': ( '5': ) '6': ',' '7': . '8': ':' '9': '``' '10': CC '11': CD '12': DT '13': EX '14': FW '15': IN '16': JJ '17': JJR '18': JJS '19': LS '20': MD '21': NN '22': NNP '23': NNPS '24': NNS '25': NN|SYM '26': PDT '27': POS '28': PRP '29': PRP$ '30': RB '31': RBR '32': RBS '33': RP '34': SYM '35': TO '36': UH '37': VB '38': VBD '39': VBG '40': VBN '41': VBP '42': VBZ '43': WDT '44': WP '45': WP$ '46': WRB '47': HYPH '48': XX '49': NFP '50': AFX '51': ADD '52': -LRB- '53': -RRB- '54': '-' - name: parse_bit sequence: string - name: predicate_lemma sequence: string - name: predicate_framenet_id sequence: string - name: word_sense sequence: string - name: speaker sequence: string - name: ner_tags sequence: class_label: names: '0': B-PERSON '1': I-PERSON '2': B-NORP '3': I-NORP '4': B-FAC '5': I-FAC '6': B-ORG '7': I-ORG '8': B-GPE '9': I-GPE '10': B-LOC '11': I-LOC '12': B-PRODUCT '13': I-PRODUCT '14': B-EVENT '15': I-EVENT '16': B-WORK_OF_ART '17': I-WORK_OF_ART '18': B-LAW '19': I-LAW '20': B-LANGUAGE '21': I-LANGUAGE '22': B-DATE '23': I-DATE '24': B-TIME '25': I-TIME '26': B-PERCENT '27': I-PERCENT '28': B-MONEY '29': I-MONEY '30': B-QUANTITY '31': I-QUANTITY '32': B-ORDINAL '33': I-ORDINAL '34': B-CARDINAL '35': I-CARDINAL '36': '*' '37': '0' '38': '-' - name: verbal_predicates sequence: string - name: coreference_clusters sequence: string splits: - name: validation num_bytes: 367293 num_examples: 396 - name: test num_bytes: 375480 num_examples: 396 download_size: 802425 dataset_size: 742773 - config_name: type2_anti features: - name: document_id dtype: string - name: part_number dtype: string - name: word_number sequence: int32 - name: tokens sequence: string - name: pos_tags sequence: class_label: names: '0': '"' '1': '''''' '2': '#' '3': $ '4': ( '5': ) '6': ',' '7': . '8': ':' '9': '``' '10': CC '11': CD '12': DT '13': EX '14': FW '15': IN '16': JJ '17': JJR '18': JJS '19': LS '20': MD '21': NN '22': NNP '23': NNPS '24': NNS '25': NN|SYM '26': PDT '27': POS '28': PRP '29': PRP$ '30': RB '31': RBR '32': RBS '33': RP '34': SYM '35': TO '36': UH '37': VB '38': VBD '39': VBG '40': VBN '41': VBP '42': VBZ '43': WDT '44': WP '45': WP$ '46': WRB '47': HYPH '48': XX '49': NFP '50': AFX '51': ADD '52': -LRB- '53': -RRB- '54': '-' - name: parse_bit sequence: string - name: predicate_lemma sequence: string - name: predicate_framenet_id sequence: string - name: word_sense sequence: string - name: speaker sequence: string - name: ner_tags sequence: class_label: names: '0': B-PERSON '1': I-PERSON '2': B-NORP '3': I-NORP '4': B-FAC '5': I-FAC '6': B-ORG '7': I-ORG '8': B-GPE '9': I-GPE '10': B-LOC '11': I-LOC '12': B-PRODUCT '13': I-PRODUCT '14': B-EVENT '15': I-EVENT '16': B-WORK_OF_ART '17': I-WORK_OF_ART '18': B-LAW '19': I-LAW '20': B-LANGUAGE '21': I-LANGUAGE '22': B-DATE '23': I-DATE '24': B-TIME '25': I-TIME '26': B-PERCENT '27': I-PERCENT '28': B-MONEY '29': I-MONEY '30': B-QUANTITY '31': I-QUANTITY '32': B-ORDINAL '33': I-ORDINAL '34': B-CARDINAL '35': I-CARDINAL '36': '*' '37': '0' '38': '-' - name: verbal_predicates sequence: string - name: coreference_clusters sequence: string splits: - name: validation num_bytes: 368757 num_examples: 396 - name: test num_bytes: 377262 num_examples: 396 download_size: 848804 dataset_size: 746019 --- # Dataset Card for Wino_Bias dataset ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [WinoBias](https://uclanlp.github.io/corefBias/overview) - **Repository:** - **Paper:** [Arxiv](https://arxiv.org/abs/1804.06876) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary WinoBias, a Winograd-schema dataset for coreference resolution focused on gender bias. The corpus contains Winograd-schema style sentences with entities corresponding to people referred by their occupation (e.g. the nurse, the doctor, the carpenter). ### Supported Tasks and Leaderboards The underlying task is coreference resolution. ### Languages English ## Dataset Structure ### Data Instances The dataset has 4 subsets: `type1_pro`, `type1_anti`, `type2_pro` and `type2_anti`. The `*_pro` subsets contain sentences that reinforce gender stereotypes (e.g. mechanics are male, nurses are female), whereas the `*_anti` datasets contain "anti-stereotypical" sentences (e.g. mechanics are female, nurses are male). The `type1` (*WB-Knowledge*) subsets contain sentences for which world knowledge is necessary to resolve the co-references, and `type2` (*WB-Syntax*) subsets require only the syntactic information present in the sentence to resolve them. ### Data Fields - document_id = This is a variation on the document filename - part_number = Some files are divided into multiple parts numbered as 000, 001, 002, ... etc. - word_num = This is the word index of the word in that sentence. - tokens = This is the token as segmented/tokenized in the Treebank. - pos_tags = This is the Penn Treebank style part of speech. When parse information is missing, all part of speeches except the one for which there is some sense or proposition annotation are marked with a XX tag. The verb is marked with just a VERB tag. - parse_bit = This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the "([pos] [word])" string (or leaf) and concatenating the items in the rows of that column. When the parse information is missing, the first word of a sentence is tagged as "(TOP*" and the last word is tagged as "*)" and all intermediate words are tagged with a "*". - predicate_lemma = The predicate lemma is mentioned for the rows for which we have semantic role information or word sense information. All other rows are marked with a "-". - predicate_framenet_id = This is the PropBank frameset ID of the predicate in predicate_lemma. - word_sense = This is the word sense of the word in Column tokens. - speaker = This is the speaker or author name where available. - ner_tags = These columns identifies the spans representing various named entities. For documents which do not have named entity annotation, each line is represented with an "*". - verbal_predicates = There is one column each of predicate argument structure information for the predicate mentioned in predicate_lemma. If there are no predicates tagged in a sentence this is a single column with all rows marked with an "*". ### Data Splits Dev and Test Split available ## Dataset Creation ### Curation Rationale The WinoBias dataset was introduced in 2018 (see [paper](https://arxiv.org/abs/1804.06876)), with its original task being *coreference resolution*, which is a task that aims to identify mentions that refer to the same entity or person. ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? The dataset was created by researchers familiar with the WinoBias project, based on two prototypical templates provided by the authors, in which entities interact in plausible ways. ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? "Researchers familiar with the [WinoBias] project" ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [Recent work](https://www.microsoft.com/en-us/research/uploads/prod/2021/06/The_Salmon_paper.pdf) has shown that this dataset contains grammatical issues, incorrect or ambiguous labels, and stereotype conflation, among other limitations. ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez and Kai-Wei Chan ### Licensing Information MIT Licence ### Citation Information @article{DBLP:journals/corr/abs-1804-06876, author = {Jieyu Zhao and Tianlu Wang and Mark Yatskar and Vicente Ordonez and Kai{-}Wei Chang}, title = {Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods}, journal = {CoRR}, volume = {abs/1804.06876}, year = {2018}, url = {http://arxiv.org/abs/1804.06876}, archivePrefix = {arXiv}, eprint = {1804.06876}, timestamp = {Mon, 13 Aug 2018 16:47:01 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1804-06876.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ### Contributions Thanks to [@akshayb7](https://github.com/akshayb7) for adding this dataset. Updated by [@JieyuZhao](https://github.com/JieyuZhao).

gsarti/wmt_vat

gsarti

--- annotations_creators: - found language_creators: - expert-generated language: - cs - de - en - et - fi - fr - gu - iu - ja - kk - km - lt - lv - pl - ps - ro - ru - ta - tr - zh license: - unknown multilinguality: - multilingual - translation size_categories: - unknown source_datasets: - extended|wmt16 - extended|wmt17 - extended|wmt18 - extended|wmt19 - extended|wmt20 task_categories: - text-generation - translation task_ids: [] pretty_name: wmt_vat tags: - conditional-text-generation --- # Dataset Card for Variance-Aware MT Test Sets ## Table of Contents - [Dataset Card for Variance-Aware MT Test Sets](#dataset-card-for-variance-aware-mt-test-sets) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Machine Translation](#machine-translation) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - **Repository:** [Github](https://github.com/NLP2CT/Variance-Aware-MT-Test-Sets) - **Paper:** [NeurIPS](https://openreview.net/forum?id=hhKA5k0oVy5) - **Point of Contact:** [Runzhe Zhan](mailto:nlp2ct.runzhe@gmail.com) ### Dataset Summary This dataset comprises 70 small and discriminative test sets for machine translation (MT) evaluation called variance-aware test sets (VAT), covering 35 translation directions from WMT16 to WMT20 competitions. VAT is automatically created by a novel variance-aware filtering method that filters the indiscriminative test instances of the current MT benchmark without any human labor. Experimental results show that VAT outperforms the original WMT benchmark in terms of the correlation with human judgment across mainstream language pairs and test sets. Further analysis on the properties of VAT reveals the challenging linguistic features (e.g., translation of low-frequency words and proper nouns) for the competitive MT systems, providing guidance for constructing future MT test sets. **Disclaimer**: *The VAT test sets are hosted through Github by the [Natural Language Processing & Portuguese-Chinese Machine Translation Laboratory (NLP2CT Lab)](http://nlp2ct.cis.um.edu.mo/) of the University of Macau. They were introduced by the paper [Variance-Aware Machine Translation Test Sets](https://openreview.net/forum?id=hhKA5k0oVy5) by [Runzhe Zhan](https://runzhe.me/), [Xuebo Liu](https://sunbowliu.github.io/), [Derek F. Wong](https://www.fst.um.edu.mo/personal/derek-wong/), [Lidia S. Chao](https://aclanthology.org/people/l/lidia-s-chao/) and follow the original licensing for WMT test sets. ### Supported Tasks and Leaderboards #### Machine Translation Refer to the [original paper](https://openreview.net/forum?id=hhKA5k0oVy5) for additional details on model evaluation on VAT. ### Languages The following table taken from the original paper lists the languages supported by the VAT test sets, for a total of 70 language pairs: | ↔️ | `wmt16` | `wmt17` | `wmt18` | `wmt19` | `wmt20` | |----------:|:--------|:--------|:--------|--------:|--------:| | `xx_en` | `cs`,`de`,`fi`, <br /> `ro`,`ru`,`tr` | `cs`,`de`,`fi`,`lv`, <br /> `ru`,`tr`,`zh` | `cs`,`de`,`et`,`fi`, <br /> `ru`,`tr`,`zh` | `de`,`fi`,`gu`, <br /> `kk`,`lt`,`ru`,`zh` | `cs`,`de`,`iu`,`ja`,`km`, <br /> `pl`,`ps`,`ru`,`ta`,`zh`| | `en_xx` | `ru` | `cs`,`de`,`fi`, <br /> `lv`,`ru`,`tr`,`zh` | `cs`,`de`,`et`,`fi`, <br /> `ru`,`tr`,`zh` | `cs`,`de`,`fi`,`gu`, <br /> `kk`,`lt`,`ru`,`zh` | `cs`,`de`,`ja`,`pl`, <br /> `ru`,`ta`,`zh`| | `xx_yy` | / | / | / | `de_cs`,`de_fr`, <br /> `fr_de` | / | To use any one of the test set, pass `wmtXX_src_tgt` as configuration name to the `load_dataset` command. E.g. to load the English-Russian test set from `wmt16`, use `load_dataset('gsarti/wmt_vat', 'wmt16_en_ru')`. ## Dataset Structure ### Data Instances A sample from the `test` split (the only available split) for the WMT16 English-Russian language (`wmt16_en_ru` config) is provided below. All configurations have the same structure. ```python { 'orig_id': 0, 'source': 'The social card of residents of Ivanovo region is to be recognised as an electronic payment instrument.', 'reference': 'Социальная карта жителя Ивановской области признается электронным средством платежа.' } ``` The text is provided as-in the original dataset, without further preprocessing or tokenization. ### Data Fields - `orig_id`: Id corresponding to the row id in the original dataset, before variance-aware filtering. - `source`: The source sentence. - `reference`: The reference sentence in the target language. ### Data Splits Taken from the original repository: | Configuration | # Sentences | # Words | # Vocabulary | | :-----------: | :--------: | :-----: | :--------------: | | `wmt20_km_en` | 928 | 17170 | 3645 | | `wmt20_cs_en` | 266 | 12568 | 3502 | | `wmt20_en_de` | 567 | 21336 | 5945 | | `wmt20_ja_en` | 397 | 10526 | 3063 | | `wmt20_ps_en` | 1088 | 20296 | 4303 | | `wmt20_en_zh` | 567 | 18224 | 5019 | | `wmt20_en_ta` | 400 | 7809 | 4028 | | `wmt20_de_en` | 314 | 16083 | 4046 | | `wmt20_zh_en` | 800 | 35132 | 6457 | | `wmt20_en_ja` | 400 | 12718 | 2969 | | `wmt20_en_cs` | 567 | 16579 | 6391 | | `wmt20_en_pl` | 400 | 8423 | 3834 | | `wmt20_en_ru` | 801 | 17446 | 6877 | | `wmt20_pl_en` | 400 | 7394 | 2399 | | `wmt20_iu_en` | 1188 | 23494 | 3876 | | `wmt20_ru_en` | 396 | 6966 | 2330 | | `wmt20_ta_en` | 399 | 7427 | 2148 | | `wmt19_zh_en` | 800 | 36739 | 6168 | | `wmt19_en_cs` | 799 | 15433 | 6111 | | `wmt19_de_en` | 800 | 15219 | 4222 | | `wmt19_en_gu` | 399 | 8494 | 3548 | | `wmt19_fr_de` | 680 | 12616 | 3698 | | `wmt19_en_zh` | 799 | 20230 | 5547 | | `wmt19_fi_en` | 798 | 13759 | 3555 | | `wmt19_en_fi` | 799 | 13303 | 6149 | | `wmt19_kk_en` | 400 | 9283 | 2584 | | `wmt19_de_cs` | 799 | 15080 | 6166 | | `wmt19_lt_en` | 400 | 10474 | 2874 | | `wmt19_en_lt` | 399 | 7251 | 3364 | | `wmt19_ru_en` | 800 | 14693 | 3817 | | `wmt19_en_kk` | 399 | 6411 | 3252 | | `wmt19_en_ru` | 799 | 16393 | 6125 | | `wmt19_gu_en` | 406 | 8061 | 2434 | | `wmt19_de_fr` | 680 | 16181 | 3517 | | `wmt19_en_de` | 799 | 18946 | 5340 | | `wmt18_en_cs` | 1193 | 19552 | 7926 | | `wmt18_cs_en` | 1193 | 23439 | 5453 | | `wmt18_en_fi` | 1200 | 16239 | 7696 | | `wmt18_en_tr` | 1200 | 19621 | 8613 | | `wmt18_en_et` | 800 | 13034 | 6001 | | `wmt18_ru_en` | 1200 | 26747 | 6045 | | `wmt18_et_en` | 800 | 20045 | 5045 | | `wmt18_tr_en` | 1200 | 25689 | 5955 | | `wmt18_fi_en` | 1200 | 24912 | 5834 | | `wmt18_zh_en` | 1592 | 42983 | 7985 | | `wmt18_en_zh` | 1592 | 34796 | 8579 | | `wmt18_en_ru` | 1200 | 22830 | 8679 | | `wmt18_de_en` | 1199 | 28275 | 6487 | | `wmt18_en_de` | 1199 | 25473 | 7130 | | `wmt17_en_lv` | 800 | 14453 | 6161 | | `wmt17_zh_en` | 800 | 20590 | 5149 | | `wmt17_en_tr` | 1203 | 17612 | 7714 | | `wmt17_lv_en` | 800 | 18653 | 4747 | | `wmt17_en_de` | 1202 | 22055 | 6463 | | `wmt17_ru_en` | 1200 | 24807 | 5790 | | `wmt17_en_fi` | 1201 | 17284 | 7763 | | `wmt17_tr_en` | 1203 | 23037 | 5387 | | `wmt17_en_zh` | 800 | 18001 | 5629 | | `wmt17_en_ru` | 1200 | 22251 | 8761 | | `wmt17_fi_en` | 1201 | 23791 | 5300 | | `wmt17_en_cs` | 1202 | 21278 | 8256 | | `wmt17_de_en` | 1202 | 23838 | 5487 | | `wmt17_cs_en` | 1202 | 22707 | 5310 | | `wmt16_tr_en` | 1200 | 19225 | 4823 | | `wmt16_ru_en` | 1199 | 23010 | 5442 | | `wmt16_ro_en` | 800 | 16200 | 3968 | | `wmt16_de_en` | 1200 | 22612 | 5511 | | `wmt16_en_ru` | 1199 | 20233 | 7872 | | `wmt16_fi_en` | 1200 | 20744 | 5176 | | `wmt16_cs_en` | 1200 | 23235 | 5324 | ### Dataset Creation The dataset was created by retaining a subset of the top 40% instances from various WMT test sets for which the variance between automatic scores (BLEU, BLEURT, COMET, BERTScore) was the highest. Please refer to the original article [Variance-Aware Machine Translation Test Sets](https://openreview.net/forum?id=hhKA5k0oVy5) for additional information on dataset creation. ## Additional Information ### Dataset Curators The original authors of VAT are the curators of the original dataset. For problems or updates on this 🤗 Datasets version, please contact [gabriele.sarti996@gmail.com](mailto:gabriele.sarti996@gmail.com). ### Licensing Information The variance-aware test set were created based on the original WMT test set. Thus, the the [original data licensing plan](http://www.statmt.org/wmt20/translation-task.html) already stated by WMT organizers is still applicable: > The data released for the WMT news translation task can be freely used for research purposes, we just ask that you cite the WMT shared task overview paper, and respect any additional citation requirements on the individual data sets. For other uses of the data, you should consult with original owners of the data sets. ### Citation Information Please cite the authors if you use these corpora in your work. It is also advised to cite the original WMT shared task paper for the specific test sets that were used. ```bibtex @inproceedings{ zhan2021varianceaware, title={Variance-Aware Machine Translation Test Sets}, author={Runzhe Zhan and Xuebo Liu and Derek F. Wong and Lidia S. Chao}, booktitle={Thirty-fifth Conference on Neural Information Processing Systems, Datasets and Benchmarks Track}, year={2021}, url={https://openreview.net/forum?id=hhKA5k0oVy5} } ```

bcui19/chat-v2-anthropic-helpfulness

bcui19

--- license: apache-2.0 dataset_info: features: - name: prompt dtype: string - name: response dtype: string - name: source dtype: string splits: - name: train num_bytes: 162490682.0 num_examples: 155270 - name: test num_bytes: 8773391.0 num_examples: 8336 download_size: 82339171 dataset_size: 171264073.0 ---

PKU-Alignment/PKU-SafeRLHF

PKU-Alignment

--- license: cc-by-nc-4.0 task_categories: - text-generation language: - en tags: - safe - safety - ai-safety - llm - lm - human-feedback - rlhf - safe-rlhf size_categories: - 100K<n<1M --- # Dataset Card for PKU-SafeRLHF <span style="color: red;">Warning: this dataset contains data that may be offensive or harmful. The data are intended for research purposes, especially research that can make models less harmful. The views expressed in the data do not reflect the views of PKU-Alignment Team or any of its members. </span> ## Dataset Summary The preference dataset consists of 30k+ expert comparison data. Each entry in this dataset includes two responses to a question, along with safety meta-labels and preferences for both responses, taking into consideration their helpfulness and harmlessness. The annotation pipeline for this dataset is depicted in the following image:  ## Human-Preference on Harmlessness and Helpfulness ### Harmlessness of a QA Pair The harmlessness of a QA pair is evaluated based on ethical considerations and safety implications inherent in the interaction. A QA pair is deemed harmless when it is classified as risk-neutral across all 14 harm categories outlined in our paper. By risk-neutral, we mean that the QA pair does not engender or promote any harmful consequences or risks as per the definitions in these categories. Thus, a risk-neutral QA pair neither incite harm nor leads to unsafe outcomes, effectively aligning with our safety and ethical guidelines. ### Helpfulness of a Response The helpfulness of a response pertains to how effectively it addresses a given prompt. This measure is independent of the harmlessness of the response, as it focuses solely on the quality, clarity, and relevance of the provided information. Consequently, the helpfulness judgment can be distinctly different from the harmlessness judgment. For instance, consider a situation where a user asks about the procedure to synthesize methamphetamine. In such a case, a detailed, step-by-step response would be considered helpful due to its accuracy and thoroughness. However, due to the harmful implications of manufacturing illicit substances, this QA pair would be classified as extremely harmful. ### Ranking of Responses Once the helpfulness and harmlessness of responses are evaluated, they are ranked accordingly. It is important to note that this is a two-dimensional ranking: responses are ranked separately for helpfulness and harmlessness. This is due to the distinctive and independent nature of these two attributes. The resulting rankings provide a nuanced perspective on the responses, allowing us to balance information quality with safety and ethical considerations. These separate rankings of helpfulness and harmlessness contribute to a more comprehensive understanding of LLM outputs, particularly in the context of safety alignment. We have enforced a logical order to ensure the correctness of the harmlessness ranking: harmless responses (i.e. all 14 harm categories risk-neutral) are always ranked higher than harmful ones (i.e., at least 1 category risky). ## Usage To load our dataset, use the `load_dataset()` function as follows: ```python from datasets import load_dataset dataset = load_dataset("PKU-Alignment/PKU-SafeRLHF") ``` ## Paper You can find more information in our paper - **Dataset Paper:** <https://arxiv.org/abs/2307.04657> ## Contact The original authors host this dataset on GitHub here: https://github.com/PKU-Alignment/beavertails.

NeelNanda/pile-10k

NeelNanda

--- license: bigscience-bloom-rail-1.0 --- The first 10K elements of [The Pile](https://pile.eleuther.ai/), useful for debugging models trained on it. See the [HuggingFace page for the full Pile](https://huggingface.co/datasets/the_pile) for more info. Inspired by [stas' great resource](https://huggingface.co/datasets/stas/openwebtext-10k) doing the same for OpenWebText

argilla/gutenberg_spacy-ner

argilla

--- dataset_info: features: - name: text dtype: string - name: tokens sequence: string - name: prediction list: - name: end dtype: int64 - name: label dtype: string - name: score dtype: float64 - name: start dtype: int64 - name: prediction_agent dtype: string - name: annotation dtype: 'null' - name: annotation_agent dtype: 'null' - name: id dtype: string - name: metadata dtype: 'null' - name: status dtype: string - name: event_timestamp dtype: 'null' - name: metrics struct: - name: annotated struct: - name: mentions sequence: 'null' - name: predicted struct: - name: mentions list: - name: capitalness dtype: string - name: chars_length dtype: int64 - name: density dtype: float64 - name: label dtype: string - name: score dtype: float64 - name: tokens_length dtype: int64 - name: value dtype: string - name: tokens list: - name: capitalness dtype: string - name: char_end dtype: int64 - name: char_start dtype: int64 - name: custom dtype: 'null' - name: idx dtype: int64 - name: length dtype: int64 - name: score dtype: 'null' - name: tag dtype: string - name: value dtype: string - name: tokens_length dtype: int64 - name: vectors struct: - name: mini-lm-sentence-transformers sequence: float64 splits: - name: train num_bytes: 1426424 num_examples: 100 download_size: 389794 dataset_size: 1426424 language: - en --- # Dataset Card for "gutenberg_spacy-ner" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)