Datasets:

ftopal

/

huggingface-datasets-embeddings

like 0

14aa5773afa135ba835cc5179bbc4a63657a42ae

# Dataset Card for wisesight_sentiment ## 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/PyThaiNLP/wisesight-sentiment - **Repository:** https://github.com/PyThaiNLP/wisesight-sentiment - **Paper:** - **Leaderboard:** https://www.kaggle.com/c/wisesight-sentiment/ - **Point of Contact:** https://github.com/PyThaiNLP/ ### Dataset Summary Wisesight Sentiment Corpus: Social media messages in Thai language with sentiment label (positive, neutral, negative, question) - Released to public domain under Creative Commons Zero v1.0 Universal license. - Labels: {"pos": 0, "neu": 1, "neg": 2, "q": 3} - Size: 26,737 messages - Language: Central Thai - Style: Informal and conversational. With some news headlines and advertisement. - Time period: Around 2016 to early 2019. With small amount from other period. - Domains: Mixed. Majority are consumer products and services (restaurants, cosmetics, drinks, car, hotels), with some current affairs. - Privacy: - Only messages that made available to the public on the internet (websites, blogs, social network sites). - For Facebook, this means the public comments (everyone can see) that made on a public page. - Private/protected messages and messages in groups, chat, and inbox are not included. - Alternations and modifications: - Keep in mind that this corpus does not statistically represent anything in the language register. - Large amount of messages are not in their original form. Personal data are removed or masked. - Duplicated, leading, and trailing whitespaces are removed. Other punctuations, symbols, and emojis are kept intact. (Mis)spellings are kept intact. - Messages longer than 2,000 characters are removed. - Long non-Thai messages are removed. Duplicated message (exact match) are removed. - More characteristics of the data can be explore [this notebook](https://github.com/PyThaiNLP/wisesight-sentiment/blob/master/exploration.ipynb) ### Supported Tasks and Leaderboards Sentiment analysis / [Kaggle Leaderboard](https://www.kaggle.com/c/wisesight-sentiment/) ### Languages Thai ## Dataset Structure ### Data Instances ``` {'category': 'pos', 'texts': 'น่าสนนน'} {'category': 'neu', 'texts': 'ครับ #phithanbkk'} {'category': 'neg', 'texts': 'ซื้อแต่ผ้าอนามัยแบบเย็นมาค่ะ แบบว่าอีห่ากูนอนไม่ได้'} {'category': 'q', 'texts': 'มีแอลกอฮอลมั้ยคะ'} ``` ### Data Fields - `texts`: texts - `category`: sentiment of texts ranging from `pos` (positive; 0), `neu` (neutral; 1), `neg` (negative; 2) and `q` (question; 3) ### Data Splits | | train | valid | test | |-----------|-------|-------|-------| | # samples | 21628 | 2404 | 2671 | | # neu | 11795 | 1291 | 1453 | | # neg | 5491 | 637 | 683 | | # pos | 3866 | 434 | 478 | | # q | 476 | 42 | 57 | | avg words | 27.21 | 27.18 | 27.12 | | avg chars | 89.82 | 89.50 | 90.36 | ## Dataset Creation ### Curation Rationale Originally, the dataset was conceived for the [In-class Kaggle Competition](https://www.kaggle.com/c/wisesight-sentiment/) at Chulalongkorn university by [Ekapol Chuangsuwanich](https://www.cp.eng.chula.ac.th/en/about/faculty/ekapolc/) (Faculty of Engineering, Chulalongkorn University). It has since become one of the benchmarks for sentiment analysis in Thai. ### Source Data #### Initial Data Collection and Normalization - Style: Informal and conversational. With some news headlines and advertisement. - Time period: Around 2016 to early 2019. With small amount from other period. - Domains: Mixed. Majority are consumer products and services (restaurants, cosmetics, drinks, car, hotels), with some current affairs. - Privacy: - Only messages that made available to the public on the internet (websites, blogs, social network sites). - For Facebook, this means the public comments (everyone can see) that made on a public page. - Private/protected messages and messages in groups, chat, and inbox are not included. - Usernames and non-public figure names are removed - Phone numbers are masked (e.g. 088-888-8888, 09-9999-9999, 0-2222-2222) - If you see any personal data still remain in the set, please tell us - so we can remove them. - Alternations and modifications: - Keep in mind that this corpus does not statistically represent anything in the language register. - Large amount of messages are not in their original form. Personal data are removed or masked. - Duplicated, leading, and trailing whitespaces are removed. Other punctuations, symbols, and emojis are kept intact. - (Mis)spellings are kept intact. - Messages longer than 2,000 characters are removed. - Long non-Thai messages are removed. Duplicated message (exact match) are removed. #### Who are the source language producers? Social media users in Thailand ### Annotations #### Annotation process - Sentiment values are assigned by human annotators. - A human annotator put his/her best effort to assign just one label, out of four, to a message. - Agreement, enjoyment, and satisfaction are positive. Disagreement, sadness, and disappointment are negative. - Showing interest in a topic or in a product is counted as positive. In this sense, a question about a particular product could has a positive sentiment value, if it shows the interest in the product. - Saying that other product or service is better is counted as negative. - General information or news title tend to be counted as neutral. #### Who are the annotators? Outsourced annotators hired by [Wisesight (Thailand) Co., Ltd.](https://github.com/wisesight/) ### Personal and Sensitive Information - The authors tried to exclude any known personally identifiable information from this data set. - Usernames and non-public figure names are removed - Phone numbers are masked (e.g. 088-888-8888, 09-9999-9999, 0-2222-2222) - If you see any personal data still remain in the set, please tell us - so we can remove them. ## Considerations for Using the Data ### Social Impact of Dataset - `wisesight_sentiment` is the first and one of the few open datasets for sentiment analysis of social media data in Thai - There are risks of personal information that escape the anonymization process ### Discussion of Biases - A message can be ambiguous. When possible, the judgement will be based solely on the text itself. - In some situation, like when the context is missing, the annotator may have to rely on his/her own world knowledge and just guess. - In some cases, the human annotator may have an access to the message's context, like an image. These additional information are not included as part of this corpus. ### Other Known Limitations - The labels are imbalanced; over half of the texts are `neu` (neutral) whereas there are very few `q` (question). - Misspellings in social media texts make word tokenization process for Thai difficult, thus impacting the model performance ## Additional Information ### Dataset Curators Thanks [PyThaiNLP](https://github.com/PyThaiNLP/pythainlp) community, [Kitsuchart Pasupa](http://www.it.kmitl.ac.th/~kitsuchart/) (Faculty of Information Technology, King Mongkut's Institute of Technology Ladkrabang), and [Ekapol Chuangsuwanich](https://www.cp.eng.chula.ac.th/en/about/faculty/ekapolc/) (Faculty of Engineering, Chulalongkorn University) for advice. The original Kaggle competition, using the first version of this corpus, can be found at https://www.kaggle.com/c/wisesight-sentiment/ ### Licensing Information - If applicable, copyright of each message content belongs to the original poster. - **Annotation data (labels) are released to public domain.** - [Wisesight (Thailand) Co., Ltd.](https://github.com/wisesight/) helps facilitate the annotation, but does not necessarily agree upon the labels made by the human annotators. This annotation is for research purpose and does not reflect the professional work that Wisesight has been done for its customers. - The human annotator does not necessarily agree or disagree with the message. Likewise, the label he/she made to the message does not necessarily reflect his/her personal view towards the message. ### Citation Information Please cite the following if you make use of the dataset: Arthit Suriyawongkul, Ekapol Chuangsuwanich, Pattarawat Chormai, and Charin Polpanumas. 2019. **PyThaiNLP/wisesight-sentiment: First release.** September. BibTeX: ``` @software{bact_2019_3457447, author = {Suriyawongkul, Arthit and Chuangsuwanich, Ekapol and Chormai, Pattarawat and Polpanumas, Charin}, title = {PyThaiNLP/wisesight-sentiment: First release}, month = sep, year = 2019, publisher = {Zenodo}, version = {v1.0}, doi = {10.5281/zenodo.3457447}, url = {https://doi.org/10.5281/zenodo.3457447} } ``` ### Contributions Thanks to [@cstorm125](https://github.com/cstorm125) for adding this dataset.

wisesight_sentiment

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["th"], "license": ["cc0-1.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "pretty_name": "WisesightSentiment", "dataset_info": {"features": [{"name": "texts", "dtype": "string"}, {"name": "category", "dtype": {"class_label": {"names": {"0": "pos", "1": "neu", "2": "neg", "3": "q"}}}}], "config_name": "wisesight_sentiment", "splits": [{"name": "train", "num_bytes": 5328819, "num_examples": 21628}, {"name": "validation", "num_bytes": 593570, "num_examples": 2404}, {"name": "test", "num_bytes": 662137, "num_examples": 2671}], "download_size": 2102326, "dataset_size": 6584526}, "train-eval-index": [{"config": "wisesight_sentiment", "task": "text-classification", "task_id": "multi_class_classification", "splits": {"train_split": "train", "eval_split": "test"}, "col_mapping": {"texts": "text", "category": "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"}}]}]}

2024-01-18T11:18:25+00:00

TAGS #task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Thai #license-cc0-1.0 #region-us

Dataset Card for wisesight\_sentiment ===================================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: URL * Paper: * Leaderboard: URL * Point of Contact: URL ### Dataset Summary Wisesight Sentiment Corpus: Social media messages in Thai language with sentiment label (positive, neutral, negative, question) * Released to public domain under Creative Commons Zero v1.0 Universal license. * Labels: {"pos": 0, "neu": 1, "neg": 2, "q": 3} * Size: 26,737 messages * Language: Central Thai * Style: Informal and conversational. With some news headlines and advertisement. * Time period: Around 2016 to early 2019. With small amount from other period. * Domains: Mixed. Majority are consumer products and services (restaurants, cosmetics, drinks, car, hotels), with some current affairs. * Privacy: + Only messages that made available to the public on the internet (websites, blogs, social network sites). + For Facebook, this means the public comments (everyone can see) that made on a public page. + Private/protected messages and messages in groups, chat, and inbox are not included. * Alternations and modifications: + Keep in mind that this corpus does not statistically represent anything in the language register. + Large amount of messages are not in their original form. Personal data are removed or masked. + Duplicated, leading, and trailing whitespaces are removed. Other punctuations, symbols, and emojis are kept intact. (Mis)spellings are kept intact. + Messages longer than 2,000 characters are removed. + Long non-Thai messages are removed. Duplicated message (exact match) are removed. * More characteristics of the data can be explore this notebook ### Supported Tasks and Leaderboards Sentiment analysis / Kaggle Leaderboard ### Languages Thai Dataset Structure ----------------- ### Data Instances ### Data Fields * 'texts': texts * 'category': sentiment of texts ranging from 'pos' (positive; 0), 'neu' (neutral; 1), 'neg' (negative; 2) and 'q' (question; 3) ### Data Splits Dataset Creation ---------------- ### Curation Rationale Originally, the dataset was conceived for the In-class Kaggle Competition at Chulalongkorn university by Ekapol Chuangsuwanich (Faculty of Engineering, Chulalongkorn University). It has since become one of the benchmarks for sentiment analysis in Thai. ### Source Data #### Initial Data Collection and Normalization * Style: Informal and conversational. With some news headlines and advertisement. * Time period: Around 2016 to early 2019. With small amount from other period. * Domains: Mixed. Majority are consumer products and services (restaurants, cosmetics, drinks, car, hotels), with some current affairs. * Privacy: + Only messages that made available to the public on the internet (websites, blogs, social network sites). + For Facebook, this means the public comments (everyone can see) that made on a public page. + Private/protected messages and messages in groups, chat, and inbox are not included. + Usernames and non-public figure names are removed + Phone numbers are masked (e.g. 088-888-8888, 09-9999-9999, 0-2222-2222) + If you see any personal data still remain in the set, please tell us - so we can remove them. * Alternations and modifications: + Keep in mind that this corpus does not statistically represent anything in the language register. + Large amount of messages are not in their original form. Personal data are removed or masked. + Duplicated, leading, and trailing whitespaces are removed. Other punctuations, symbols, and emojis are kept intact. + (Mis)spellings are kept intact. + Messages longer than 2,000 characters are removed. + Long non-Thai messages are removed. Duplicated message (exact match) are removed. #### Who are the source language producers? Social media users in Thailand ### Annotations #### Annotation process * Sentiment values are assigned by human annotators. * A human annotator put his/her best effort to assign just one label, out of four, to a message. * Agreement, enjoyment, and satisfaction are positive. Disagreement, sadness, and disappointment are negative. * Showing interest in a topic or in a product is counted as positive. In this sense, a question about a particular product could has a positive sentiment value, if it shows the interest in the product. * Saying that other product or service is better is counted as negative. * General information or news title tend to be counted as neutral. #### Who are the annotators? Outsourced annotators hired by Wisesight (Thailand) Co., Ltd. ### Personal and Sensitive Information * The authors tried to exclude any known personally identifiable information from this data set. * Usernames and non-public figure names are removed * Phone numbers are masked (e.g. 088-888-8888, 09-9999-9999, 0-2222-2222) * If you see any personal data still remain in the set, please tell us - so we can remove them. Considerations for Using the Data --------------------------------- ### Social Impact of Dataset * 'wisesight\_sentiment' is the first and one of the few open datasets for sentiment analysis of social media data in Thai * There are risks of personal information that escape the anonymization process ### Discussion of Biases * A message can be ambiguous. When possible, the judgement will be based solely on the text itself. + In some situation, like when the context is missing, the annotator may have to rely on his/her own world knowledge and just guess. + In some cases, the human annotator may have an access to the message's context, like an image. These additional information are not included as part of this corpus. ### Other Known Limitations * The labels are imbalanced; over half of the texts are 'neu' (neutral) whereas there are very few 'q' (question). * Misspellings in social media texts make word tokenization process for Thai difficult, thus impacting the model performance Additional Information ---------------------- ### Dataset Curators Thanks PyThaiNLP community, Kitsuchart Pasupa (Faculty of Information Technology, King Mongkut's Institute of Technology Ladkrabang), and Ekapol Chuangsuwanich (Faculty of Engineering, Chulalongkorn University) for advice. The original Kaggle competition, using the first version of this corpus, can be found at URL ### Licensing Information * If applicable, copyright of each message content belongs to the original poster. * Annotation data (labels) are released to public domain. * Wisesight (Thailand) Co., Ltd. helps facilitate the annotation, but does not necessarily agree upon the labels made by the human annotators. This annotation is for research purpose and does not reflect the professional work that Wisesight has been done for its customers. * The human annotator does not necessarily agree or disagree with the message. Likewise, the label he/she made to the message does not necessarily reflect his/her personal view towards the message. Please cite the following if you make use of the dataset: Arthit Suriyawongkul, Ekapol Chuangsuwanich, Pattarawat Chormai, and Charin Polpanumas. 2019. PyThaiNLP/wisesight-sentiment: First release. September. BibTeX: ### Contributions Thanks to @cstorm125 for adding this dataset.

76f6eba7d6b488ed8fe1f7ab80a87636898b2891

# 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, and in response, they have indicated that they do not have plans to update the Common Crawl corpus data. Their rationale pertains to the expectation that such data has been superseded, primarily by CCMatrix, and to some extent, by ParaCrawl datasets.</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.

wmt14

2022-03-02T23:29:22+00:00

{"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": [], "paperswithcode_id": "wmt-2014", "pretty_name": "WMT14", "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}]}

2024-02-05T11:44:36+00:00

TAGS #task_categories-translation #annotations_creators-no-annotation #language_creators-found #multilinguality-translation #size_categories-10M<n<100M #source_datasets-extended|europarl_bilingual #source_datasets-extended|giga_fren #source_datasets-extended|news_commentary #source_datasets-extended|un_multi #source_datasets-extended|hind_encorp #language-Czech #language-German #language-English #language-French #language-Hindi #language-Russian #license-unknown #region-us

Dataset Card for "wmt14" ======================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: * Point of Contact: * 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 **Warning:** There are issues with the Common Crawl corpus data ([We have contacted the WMT organizers, and in response, they have indicated that they do not have plans to update the Common Crawl corpus data. Their rationale pertains to the expectation that such data has been superseded, primarily by CCMatrix, and to some extent, by ParaCrawl datasets.](URL <ul> <li>Non-English files contain many English sentences.</li> <li>Their ) Translation dataset based on the data from URL. 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: ### Supported Tasks and Leaderboards ### Languages 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 Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @thomwolf, @patrickvonplaten for adding this dataset.

355dc137508cca435c3f7386cda18439af8ab7e9

# Dataset Card for "wmt15" ## 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/wmt15/translation-task.html](http://www.statmt.org/wmt15/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:** 1.74 GB - **Size of the generated dataset:** 284.34 MB - **Total amount of disk used:** 2.02 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, and in response, they have indicated that they do not have plans to update the Common Crawl corpus data. Their rationale pertains to the expectation that such data has been superseded, primarily by CCMatrix, and to some extent, by ParaCrawl datasets.</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("wmt15", "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:** 1.74 GB - **Size of the generated dataset:** 284.34 MB - **Total amount of disk used:** 2.02 GB An example of 'validation' 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|959768| 3003|2656| ## 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:2015:WMT, author = {Bojar, Ond {r}ej and Chatterjee, Rajen and Federmann, Christian and Haddow, Barry and Huck, Matthias and Hokamp, Chris and Koehn, Philipp and Logacheva, Varvara and Monz, Christof and Negri, Matteo and Post, Matt and Scarton, Carolina and Specia, Lucia and Turchi, Marco}, title = {Findings of the 2015 Workshop on Statistical Machine Translation}, booktitle = {Proceedings of the Tenth Workshop on Statistical Machine Translation}, month = {September}, year = {2015}, address = {Lisbon, Portugal}, publisher = {Association for Computational Linguistics}, pages = {1--46}, url = {http://aclweb.org/anthology/W15-3001} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

wmt15

2022-03-02T23:29:22+00:00

{"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["cs", "de", "en", "fi", "fr", "ru"], "license": ["unknown"], "multilinguality": ["translation"], "size_categories": ["10M<n<100M"], "source_datasets": ["extended|europarl_bilingual", "extended|giga_fren", "extended|news_commentary", "extended|un_multi"], "task_categories": ["translation"], "task_ids": [], "paperswithcode_id": "wmt-2015", "pretty_name": "WMT15", "dataset_info": [{"config_name": "cs-en", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["cs", "en"]}}}], "splits": [{"name": "train", "num_bytes": 282996942, "num_examples": 959768}, {"name": "validation", "num_bytes": 757817, "num_examples": 3003}, {"name": "test", "num_bytes": 572203, "num_examples": 2656}], "download_size": 1740666258, "dataset_size": 284326962}, {"config_name": "de-en", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["de", "en"]}}}], "splits": [{"name": "train", "num_bytes": 1364002869, "num_examples": 4522998}, {"name": "validation", "num_bytes": 777334, "num_examples": 3003}, {"name": "test", "num_bytes": 522989, "num_examples": 2169}], "download_size": 1740666258, "dataset_size": 1365303192}, {"config_name": "fi-en", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["fi", "en"]}}}], "splits": [{"name": "train", "num_bytes": 605146817, "num_examples": 2073394}, {"name": "validation", "num_bytes": 363941, "num_examples": 1500}, {"name": "test", "num_bytes": 306335, "num_examples": 1370}], "download_size": 273390220, "dataset_size": 605817093}, {"config_name": "fr-en", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["fr", "en"]}}}], "splits": [{"name": "train", "num_bytes": 14758986622, "num_examples": 40853137}, {"name": "validation", "num_bytes": 1138737, "num_examples": 4503}, {"name": "test", "num_bytes": 298771, "num_examples": 1500}], "download_size": 6702781608, "dataset_size": 14760424130}, {"config_name": "ru-en", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["ru", "en"]}}}], "splits": [{"name": "train", "num_bytes": 437752256, "num_examples": 1495081}, {"name": "validation", "num_bytes": 1087746, "num_examples": 3003}, {"name": "test", "num_bytes": 955972, "num_examples": 2818}], "download_size": 1092059435, "dataset_size": 439795974}]}

2024-02-05T11:45:25+00:00

TAGS #task_categories-translation #annotations_creators-no-annotation #language_creators-found #multilinguality-translation #size_categories-10M<n<100M #source_datasets-extended|europarl_bilingual #source_datasets-extended|giga_fren #source_datasets-extended|news_commentary #source_datasets-extended|un_multi #language-Czech #language-German #language-English #language-Finnish #language-French #language-Russian #license-unknown #region-us

Dataset Card for "wmt15" ======================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: * Point of Contact: * Size of downloaded dataset files: 1.74 GB * Size of the generated dataset: 284.34 MB * Total amount of disk used: 2.02 GB ### Dataset Summary **Warning:** There are issues with the Common Crawl corpus data ([We have contacted the WMT organizers, and in response, they have indicated that they do not have plans to update the Common Crawl corpus data. Their rationale pertains to the expectation that such data has been superseded, primarily by CCMatrix, and to some extent, by ParaCrawl datasets.](URL <ul> <li>Non-English files contain many English sentences.</li> <li>Their ) Translation dataset based on the data from URL. 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: ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### cs-en * Size of downloaded dataset files: 1.74 GB * Size of the generated dataset: 284.34 MB * Total amount of disk used: 2.02 GB An example of 'validation' 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 Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @thomwolf, @patrickvonplaten for adding this dataset.

27ea1f6483dca29955adc6a9e7d8a3556fbb1aea

# Dataset Card for "wmt16" ## 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/wmt16/translation-task.html](http://www.statmt.org/wmt16/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:** 1.69 GB - **Size of the generated dataset:** 297.28 MB - **Total amount of disk used:** 1.99 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, and in response, they have indicated that they do not have plans to update the Common Crawl corpus data. Their rationale pertains to the expectation that such data has been superseded, primarily by CCMatrix, and to some extent, by ParaCrawl datasets.</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("wmt16", "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:** 1.69 GB - **Size of the generated dataset:** 297.28 MB - **Total amount of disk used:** 1.99 GB An example of 'validation' 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|997240| 2656|2999| ## 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:2016:WMT1, author = {Bojar, Ond {r}ej and Chatterjee, Rajen and Federmann, Christian and Graham, Yvette and Haddow, Barry and Huck, Matthias and Jimeno Yepes, Antonio and Koehn, Philipp and Logacheva, Varvara and Monz, Christof and Negri, Matteo and Neveol, Aurelie and Neves, Mariana and Popel, Martin and Post, Matt and Rubino, Raphael and Scarton, Carolina and Specia, Lucia and Turchi, Marco and Verspoor, Karin and Zampieri, Marcos}, title = {Findings of the 2016 Conference on Machine Translation}, booktitle = {Proceedings of the First Conference on Machine Translation}, month = {August}, year = {2016}, address = {Berlin, Germany}, publisher = {Association for Computational Linguistics}, pages = {131--198}, url = {http://www.aclweb.org/anthology/W/W16/W16-2301} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

wmt16

2022-03-02T23:29:22+00:00

{"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["cs", "de", "en", "fi", "ro", "ru", "tr"], "license": ["unknown"], "multilinguality": ["translation"], "size_categories": ["10M<n<100M"], "source_datasets": ["extended|europarl_bilingual", "extended|news_commentary", "extended|setimes", "extended|un_multi"], "task_categories": ["translation"], "task_ids": [], "paperswithcode_id": "wmt-2016", "pretty_name": "WMT16", "dataset_info": [{"config_name": "cs-en", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["cs", "en"]}}}], "splits": [{"name": "train", "num_bytes": 296006386, "num_examples": 997240}, {"name": "validation", "num_bytes": 572203, "num_examples": 2656}, {"name": "test", "num_bytes": 707870, "num_examples": 2999}], "download_size": 1690726387, "dataset_size": 297286459}, {"config_name": "de-en", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["de", "en"]}}}], "splits": [{"name": "train", "num_bytes": 1373123263, "num_examples": 4548885}, {"name": "validation", "num_bytes": 522989, "num_examples": 2169}, {"name": "test", "num_bytes": 735516, "num_examples": 2999}], "download_size": 1690726387, "dataset_size": 1374381768}, {"config_name": "fi-en", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["fi", "en"]}}}], "splits": [{"name": "train", "num_bytes": 605146827, "num_examples": 2073394}, {"name": "validation", "num_bytes": 306335, "num_examples": 1370}, {"name": "test", "num_bytes": 1410515, "num_examples": 6000}], "download_size": 273390220, "dataset_size": 606863677}, {"config_name": "ro-en", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["ro", "en"]}}}], "splits": [{"name": "train", "num_bytes": 188288211, "num_examples": 610320}, {"name": "validation", "num_bytes": 561799, "num_examples": 1999}, {"name": "test", "num_bytes": 539216, "num_examples": 1999}], "download_size": 287363574, "dataset_size": 189389226}, {"config_name": "ru-en", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["ru", "en"]}}}], "splits": [{"name": "train", "num_bytes": 448338585, "num_examples": 1516162}, {"name": "validation", "num_bytes": 955972, "num_examples": 2818}, {"name": "test", "num_bytes": 1050677, "num_examples": 2998}], "download_size": 1042119564, "dataset_size": 450345234}, {"config_name": "tr-en", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["tr", "en"]}}}], "splits": [{"name": "train", "num_bytes": 60416617, "num_examples": 205756}, {"name": "validation", "num_bytes": 240650, "num_examples": 1001}, {"name": "test", "num_bytes": 732436, "num_examples": 3000}], "download_size": 62263061, "dataset_size": 61389703}]}

2024-02-05T11:42:08+00:00

TAGS #task_categories-translation #annotations_creators-no-annotation #language_creators-found #multilinguality-translation #size_categories-10M<n<100M #source_datasets-extended|europarl_bilingual #source_datasets-extended|news_commentary #source_datasets-extended|setimes #source_datasets-extended|un_multi #language-Czech #language-German #language-English #language-Finnish #language-Romanian #language-Russian #language-Turkish #license-unknown #region-us

Dataset Card for "wmt16" ======================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: * Point of Contact: * Size of downloaded dataset files: 1.69 GB * Size of the generated dataset: 297.28 MB * Total amount of disk used: 1.99 GB ### Dataset Summary **Warning:** There are issues with the Common Crawl corpus data ([We have contacted the WMT organizers, and in response, they have indicated that they do not have plans to update the Common Crawl corpus data. Their rationale pertains to the expectation that such data has been superseded, primarily by CCMatrix, and to some extent, by ParaCrawl datasets.](URL <ul> <li>Non-English files contain many English sentences.</li> <li>Their ) Translation dataset based on the data from URL. 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: ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### cs-en * Size of downloaded dataset files: 1.69 GB * Size of the generated dataset: 297.28 MB * Total amount of disk used: 1.99 GB An example of 'validation' 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 Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @thomwolf, @patrickvonplaten for adding this dataset.

5dc16aaa4afcd5e52ec433c19acbf19cdd388bf1

# Dataset Card for "wmt17" ## 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/wmt17/translation-task.html](http://www.statmt.org/wmt17/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:** 1.78 GB - **Size of the generated dataset:** 302.09 MB - **Total amount of disk used:** 2.09 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, and in response, they have indicated that they do not have plans to update the Common Crawl corpus data. Their rationale pertains to the expectation that such data has been superseded, primarily by CCMatrix, and to some extent, by ParaCrawl datasets.</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("wmt17", "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:** 1.78 GB - **Size of the generated dataset:** 302.09 MB - **Total amount of disk used:** 2.09 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|1018291| 2999|3005| ## 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:2017:WMT1, author = {Bojar, Ond {r}ej and Chatterjee, Rajen and Federmann, Christian and Graham, Yvette and Haddow, Barry and Huang, Shujian and Huck, Matthias and Koehn, Philipp and Liu, Qun and Logacheva, Varvara and Monz, Christof and Negri, Matteo and Post, Matt and Rubino, Raphael and Specia, Lucia and Turchi, Marco}, title = {Findings of the 2017 Conference on Machine Translation (WMT17)}, booktitle = {Proceedings of the Second Conference on Machine Translation, Volume 2: Shared Task Papers}, month = {September}, year = {2017}, address = {Copenhagen, Denmark}, publisher = {Association for Computational Linguistics}, pages = {169--214}, url = {http://www.aclweb.org/anthology/W17-4717} } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

wmt17

2022-03-02T23:29:22+00:00

{"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["cs", "de", "en", "fi", "lv", "ru", "tr", "zh"], "license": ["unknown"], "multilinguality": ["translation"], "size_categories": ["10M<n<100M"], "source_datasets": ["extended|europarl_bilingual", "extended|news_commentary", "extended|setimes", "extended|un_multi"], "task_categories": ["translation"], "task_ids": [], "pretty_name": "WMT17", "dataset_info": [{"config_name": "cs-en", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["cs", "en"]}}}], "splits": [{"name": "train", "num_bytes": 300698431, "num_examples": 1018291}, {"name": "validation", "num_bytes": 707870, "num_examples": 2999}, {"name": "test", "num_bytes": 674430, "num_examples": 3005}], "download_size": 1784240523, "dataset_size": 302080731}, {"config_name": "de-en", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["de", "en"]}}}], "splits": [{"name": "train", "num_bytes": 1715537443, "num_examples": 5906184}, {"name": "validation", "num_bytes": 735516, "num_examples": 2999}, {"name": "test", "num_bytes": 729519, "num_examples": 3004}], "download_size": 1945382236, "dataset_size": 1717002478}, {"config_name": "fi-en", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["fi", "en"]}}}], "splits": [{"name": "train", "num_bytes": 743856525, "num_examples": 2656542}, {"name": "validation", "num_bytes": 1410515, "num_examples": 6000}, {"name": "test", "num_bytes": 1388828, "num_examples": 6004}], "download_size": 434531933, "dataset_size": 746655868}, {"config_name": "lv-en", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["lv", "en"]}}}], "splits": [{"name": "train", "num_bytes": 517419100, "num_examples": 3567528}, {"name": "validation", "num_bytes": 544604, "num_examples": 2003}, {"name": "test", "num_bytes": 530474, "num_examples": 2001}], "download_size": 169634544, "dataset_size": 518494178}, {"config_name": "ru-en", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["ru", "en"]}}}], "splits": [{"name": "train", "num_bytes": 11000075522, "num_examples": 24782720}, {"name": "validation", "num_bytes": 1050677, "num_examples": 2998}, {"name": "test", "num_bytes": 1040195, "num_examples": 3001}], "download_size": 3582640660, "dataset_size": 11002166394}, {"config_name": "tr-en", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["tr", "en"]}}}], "splits": [{"name": "train", "num_bytes": 60416617, "num_examples": 205756}, {"name": "validation", "num_bytes": 732436, "num_examples": 3000}, {"name": "test", "num_bytes": 752773, "num_examples": 3007}], "download_size": 62263061, "dataset_size": 61901826}, {"config_name": "zh-en", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["zh", "en"]}}}], "splits": [{"name": "train", "num_bytes": 5529286149, "num_examples": 25134743}, {"name": "validation", "num_bytes": 589591, "num_examples": 2002}, {"name": "test", "num_bytes": 540347, "num_examples": 2001}], "download_size": 2314906945, "dataset_size": 5530416087}]}

2024-02-05T11:42:39+00:00

TAGS #task_categories-translation #annotations_creators-no-annotation #language_creators-found #multilinguality-translation #size_categories-10M<n<100M #source_datasets-extended|europarl_bilingual #source_datasets-extended|news_commentary #source_datasets-extended|setimes #source_datasets-extended|un_multi #language-Czech #language-German #language-English #language-Finnish #language-Latvian #language-Russian #language-Turkish #language-Chinese #license-unknown #region-us

Dataset Card for "wmt17" ======================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: * Point of Contact: * Size of downloaded dataset files: 1.78 GB * Size of the generated dataset: 302.09 MB * Total amount of disk used: 2.09 GB ### Dataset Summary **Warning:** There are issues with the Common Crawl corpus data ([We have contacted the WMT organizers, and in response, they have indicated that they do not have plans to update the Common Crawl corpus data. Their rationale pertains to the expectation that such data has been superseded, primarily by CCMatrix, and to some extent, by ParaCrawl datasets.](URL <ul> <li>Non-English files contain many English sentences.</li> <li>Their ) Translation dataset based on the data from URL. 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: ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### cs-en * Size of downloaded dataset files: 1.78 GB * Size of the generated dataset: 302.09 MB * Total amount of disk used: 2.09 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 Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @patrickvonplaten, @thomwolf for adding this dataset.

1e08da43f060af9c7fda51ea26c83c9602f38313

# Dataset Card for "wmt18" ## 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/wmt18/translation-task.html](http://www.statmt.org/wmt18/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:** 2.03 GB - **Size of the generated dataset:** 1.46 GB - **Total amount of disk used:** 3.49 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, and in response, they have indicated that they do not have plans to update the Common Crawl corpus data. Their rationale pertains to the expectation that such data has been superseded, primarily by CCMatrix, and to some extent, by ParaCrawl datasets.</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("wmt18", "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:** 2.03 GB - **Size of the generated dataset:** 1.46 GB - **Total amount of disk used:** 3.49 GB An example of 'validation' 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|11046024| 3005|2983| ## 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:2018:WMT1, author = {Bojar, Ond {r}ej and Federmann, Christian and Fishel, Mark and Graham, Yvette and Haddow, Barry and Huck, Matthias and Koehn, Philipp and Monz, Christof}, title = {Findings of the 2018 Conference on Machine Translation (WMT18)}, booktitle = {Proceedings of the Third Conference on Machine Translation, Volume 2: Shared Task Papers}, month = {October}, year = {2018}, address = {Belgium, Brussels}, publisher = {Association for Computational Linguistics}, pages = {272--307}, url = {http://www.aclweb.org/anthology/W18-6401} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

wmt18

2022-03-02T23:29:22+00:00

{"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["cs", "de", "en", "et", "fi", "kk", "ru", "tr", "zh"], "license": ["unknown"], "multilinguality": ["translation"], "size_categories": ["10M<n<100M"], "source_datasets": ["extended|europarl_bilingual", "extended|news_commentary", "extended|opus_paracrawl", "extended|setimes", "extended|un_multi"], "task_categories": ["translation"], "task_ids": [], "paperswithcode_id": "wmt-2018", "pretty_name": "WMT18", "dataset_info": [{"config_name": "cs-en", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["cs", "en"]}}}], "splits": [{"name": "train", "num_bytes": 1461016186, "num_examples": 11046024}, {"name": "validation", "num_bytes": 674430, "num_examples": 3005}, {"name": "test", "num_bytes": 696229, "num_examples": 2983}], "download_size": 2030359086, "dataset_size": 1462386845}, {"config_name": "de-en", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["de", "en"]}}}], "splits": [{"name": "train", "num_bytes": 8187552108, "num_examples": 42271874}, {"name": "validation", "num_bytes": 729519, "num_examples": 3004}, {"name": "test", "num_bytes": 757649, "num_examples": 2998}], "download_size": 3808612335, "dataset_size": 8189039276}, {"config_name": "et-en", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["et", "en"]}}}], "splits": [{"name": "train", "num_bytes": 647992667, "num_examples": 2175873}, {"name": "validation", "num_bytes": 459398, "num_examples": 2000}, {"name": "test", "num_bytes": 489394, "num_examples": 2000}], "download_size": 524534404, "dataset_size": 648941459}, {"config_name": "fi-en", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["fi", "en"]}}}], "splits": [{"name": "train", "num_bytes": 857171881, "num_examples": 3280600}, {"name": "validation", "num_bytes": 1388828, "num_examples": 6004}, {"name": "test", "num_bytes": 691841, "num_examples": 3000}], "download_size": 491874780, "dataset_size": 859252550}, {"config_name": "kk-en", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["kk", "en"]}}}], "splits": [{"name": "train"}, {"name": "validation"}, {"name": "test"}], "download_size": 0, "dataset_size": 0}, {"config_name": "ru-en", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["ru", "en"]}}}], "splits": [{"name": "train", "num_bytes": 13665367647, "num_examples": 36858512}, {"name": "validation", "num_bytes": 1040195, "num_examples": 3001}, {"name": "test", "num_bytes": 1085596, "num_examples": 3000}], "download_size": 4195144356, "dataset_size": 13667493438}, {"config_name": "tr-en", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["tr", "en"]}}}], "splits": [{"name": "train", "num_bytes": 60416617, "num_examples": 205756}, {"name": "validation", "num_bytes": 752773, "num_examples": 3007}, {"name": "test", "num_bytes": 770313, "num_examples": 3000}], "download_size": 62263061, "dataset_size": 61939703}, {"config_name": "zh-en", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["zh", "en"]}}}], "splits": [{"name": "train", "num_bytes": 5536169801, "num_examples": 25160346}, {"name": "validation", "num_bytes": 540347, "num_examples": 2001}, {"name": "test", "num_bytes": 1107522, "num_examples": 3981}], "download_size": 2259428767, "dataset_size": 5537817670}]}

2024-02-05T11:42:55+00:00

TAGS #task_categories-translation #annotations_creators-no-annotation #language_creators-found #multilinguality-translation #size_categories-10M<n<100M #source_datasets-extended|europarl_bilingual #source_datasets-extended|news_commentary #source_datasets-extended|opus_paracrawl #source_datasets-extended|setimes #source_datasets-extended|un_multi #language-Czech #language-German #language-English #language-Estonian #language-Finnish #language-Kazakh #language-Russian #language-Turkish #language-Chinese #license-unknown #region-us

Dataset Card for "wmt18" ======================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: * Point of Contact: * Size of downloaded dataset files: 2.03 GB * Size of the generated dataset: 1.46 GB * Total amount of disk used: 3.49 GB ### Dataset Summary **Warning:** There are issues with the Common Crawl corpus data ([We have contacted the WMT organizers, and in response, they have indicated that they do not have plans to update the Common Crawl corpus data. Their rationale pertains to the expectation that such data has been superseded, primarily by CCMatrix, and to some extent, by ParaCrawl datasets.](URL <ul> <li>Non-English files contain many English sentences.</li> <li>Their ) Translation dataset based on the data from URL. 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: ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### cs-en * Size of downloaded dataset files: 2.03 GB * Size of the generated dataset: 1.46 GB * Total amount of disk used: 3.49 GB An example of 'validation' 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 Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @thomwolf, @patrickvonplaten for adding this dataset.

7df8dc7e50241035742bfbb2bb9e62a35984e5d1

# Dataset Card for "wmt19" ## 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/wmt19/translation-task.html](http://www.statmt.org/wmt19/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:** 2.02 GB - **Size of the generated dataset:** 1.32 GB - **Total amount of disk used:** 3.33 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, and in response, they have indicated that they do not have plans to update the Common Crawl corpus data. Their rationale pertains to the expectation that such data has been superseded, primarily by CCMatrix, and to some extent, by ParaCrawl datasets.</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("wmt19", "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:** 2.02 GB - **Size of the generated dataset:** 1.32 GB - **Total amount of disk used:** 3.33 GB An example of 'validation' 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| |-----|------:|---------:| |cs-en|7270695| 2983| ## 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 ``` @ONLINE {wmt19translate, author = "Wikimedia Foundation", title = "ACL 2019 Fourth Conference on Machine Translation (WMT19), Shared Task: Machine Translation of News", url = "http://www.statmt.org/wmt19/translation-task.html" } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

wmt19

2022-03-02T23:29:22+00:00

{"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["cs", "de", "en", "fi", "fr", "gu", "kk", "lt", "ru", "zh"], "license": ["unknown"], "multilinguality": ["translation"], "size_categories": ["10M<n<100M"], "source_datasets": ["extended|europarl_bilingual", "extended|news_commentary", "extended|opus_paracrawl", "extended|un_multi"], "task_categories": ["translation"], "task_ids": [], "pretty_name": "WMT19", "dataset_info": [{"config_name": "cs-en", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["cs", "en"]}}}], "splits": [{"name": "train", "num_bytes": 1314871994, "num_examples": 7270695}, {"name": "validation", "num_bytes": 696229, "num_examples": 2983}], "download_size": 2018537046, "dataset_size": 1315568223}, {"config_name": "de-en", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["de", "en"]}}}], "splits": [{"name": "train", "num_bytes": 8420967590, "num_examples": 38690334}, {"name": "validation", "num_bytes": 757649, "num_examples": 2998}], "download_size": 10422475109, "dataset_size": 8421725239}, {"config_name": "fi-en", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["fi", "en"]}}}], "splits": [{"name": "train", "num_bytes": 1422922267, "num_examples": 6587448}, {"name": "validation", "num_bytes": 691841, "num_examples": 3000}], "download_size": 1006124909, "dataset_size": 1423614108}, {"config_name": "gu-en", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["gu", "en"]}}}], "splits": [{"name": "train", "num_bytes": 590763, "num_examples": 11670}, {"name": "validation", "num_bytes": 774621, "num_examples": 1998}], "download_size": 38891457, "dataset_size": 1365384}, {"config_name": "kk-en", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["kk", "en"]}}}], "splits": [{"name": "train", "num_bytes": 9157438, "num_examples": 126583}, {"name": "validation", "num_bytes": 846857, "num_examples": 2066}], "download_size": 41558315, "dataset_size": 10004295}, {"config_name": "lt-en", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["lt", "en"]}}}], "splits": [{"name": "train", "num_bytes": 513084361, "num_examples": 2344893}, {"name": "validation", "num_bytes": 541953, "num_examples": 2000}], "download_size": 411309952, "dataset_size": 513626314}, {"config_name": "ru-en", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["ru", "en"]}}}], "splits": [{"name": "train", "num_bytes": 13721377178, "num_examples": 37492126}, {"name": "validation", "num_bytes": 1085596, "num_examples": 3000}], "download_size": 4134147853, "dataset_size": 13722462774}, {"config_name": "zh-en", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["zh", "en"]}}}], "splits": [{"name": "train", "num_bytes": 5584359748, "num_examples": 25984574}, {"name": "validation", "num_bytes": 1107522, "num_examples": 3981}], "download_size": 2195879129, "dataset_size": 5585467270}, {"config_name": "fr-de", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["fr", "de"]}}}], "splits": [{"name": "train", "num_bytes": 2358413485, "num_examples": 9824476}, {"name": "validation", "num_bytes": 441426, "num_examples": 1512}], "download_size": 757345846, "dataset_size": 2358854911}]}

2024-02-05T11:43:11+00:00

TAGS #task_categories-translation #annotations_creators-no-annotation #language_creators-found #multilinguality-translation #size_categories-10M<n<100M #source_datasets-extended|europarl_bilingual #source_datasets-extended|news_commentary #source_datasets-extended|opus_paracrawl #source_datasets-extended|un_multi #language-Czech #language-German #language-English #language-Finnish #language-French #language-Gujarati #language-Kazakh #language-Lithuanian #language-Russian #language-Chinese #license-unknown #region-us

Dataset Card for "wmt19" ======================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: * Point of Contact: * Size of downloaded dataset files: 2.02 GB * Size of the generated dataset: 1.32 GB * Total amount of disk used: 3.33 GB ### Dataset Summary **Warning:** There are issues with the Common Crawl corpus data ([We have contacted the WMT organizers, and in response, they have indicated that they do not have plans to update the Common Crawl corpus data. Their rationale pertains to the expectation that such data has been superseded, primarily by CCMatrix, and to some extent, by ParaCrawl datasets.](URL <ul> <li>Non-English files contain many English sentences.</li> <li>Their ) Translation dataset based on the data from URL. 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: ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### cs-en * Size of downloaded dataset files: 2.02 GB * Size of the generated dataset: 1.32 GB * Total amount of disk used: 3.33 GB An example of 'validation' 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 Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @patrickvonplaten, @mariamabarham, @thomwolf for adding this dataset.

947003d8b96062f1c954e2398e717e99fa4c7264

# Dataset Card for WMT20 - MultiLingual Quality Estimation (MLQE) Task1 ## 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:** [WMT20 Quality Estimation Shared Task](http://www.statmt.org/wmt20/quality-estimation-task.html) - **Repository:** [Github repository](https://github.com/facebookresearch/mlqe/) - **Paper:** *Not available* ### Dataset Summary From the homepage: *This shared task (part of WMT20) will build on its previous editions to further examine automatic methods for estimating the quality of neural machine translation output at run-time, without relying on reference translations. As in previous years, we cover estimation at various levels. Important elements introduced this year include: a new task where sentences are annotated with Direct Assessment (DA) scores instead of labels based on post-editing; a new multilingual sentence-level dataset mainly from Wikipedia articles, where the source articles can be retrieved for document-wide context; the availability of NMT models to explore system-internal information for the task.* *Task 1 uses Wikipedia data for 6 language pairs that includes high-resource English--German (En-De) and English--Chinese (En-Zh), medium-resource Romanian--English (Ro-En) and Estonian--English (Et-En), and low-resource Sinhalese--English (Si-En) and Nepalese--English (Ne-En), as well as a dataset with a combination of Wikipedia articles and Reddit articles for Russian-English (En-Ru). The datasets were collected by translating sentences sampled from source language articles using state-of-the-art NMT models built using the fairseq toolkit and annotated with Direct Assessment (DA) scores by professional translators. Each sentence was annotated following the FLORES setup, which presents a form of DA, where at least three professional translators rate each sentence from 0-100 according to the perceived translation quality. DA scores are standardised using the z-score by rater. Participating systems are required to score sentences according to z-standardised DA scores.* ### Supported Tasks and Leaderboards From the homepage: *Sentence-level submissions will be evaluated in terms of the Pearson's correlation metric for the DA prediction agains human DA (z-standardised mean DA score, i.e. z_mean). These are the [official evaluation scripts](https://github.com/sheffieldnlp/qe-eval-scripts). The evaluation will focus on multilingual systems, i.e. systems that are able to provide predictions for all languages in the Wikipedia domain. Therefore, average Pearson correlation across all these languages will be used to rank QE systems. We will also evaluate QE systems on a per-language basis for those interested in particular languages.* ### Languages Eight languages are represented in this dataset: - English (`en`) - German (`de`) - Romanian (`ro`) - Estonian (`et`) - Nepalese (`ne`) - Sinhala (`si`) - Russian (`ru`) ## Dataset Structure ### Data Instances An example looks like this: ``` { 'segid': 123, 'translation': { 'en': 'José Ortega y Gasset visited Husserl at Freiburg in 1934.', 'de': '1934 besuchte José Ortega y Gasset Husserl in Freiburg.', }, 'scores': [100.0, 100.0, 100.0], 'mean': 100.0, 'z_scores': [0.9553316831588745, 1.552362322807312, 0.850531816482544], 'z_mean': 1.1194086074829102, 'model_score': -0.10244649648666382, 'doc_id': 'Edmund Husserl', 'nmt_output': '1934 besuchte José Ort@@ ega y G@@ asset Hus@@ ser@@ l in Freiburg .', 'word_probas': [-0.4458000063896179, -0.2745000123977661, -0.07199999690055847, -0.002300000051036477, -0.005900000222027302, -0.14579999446868896, -0.07500000298023224, -0.012400000356137753, -0.026900000870227814, -0.036400001496076584, -0.05299999937415123, -0.14990000426769257, -0.012400000356137753, -0.1145000010728836, -0.10999999940395355], } ``` ### Data Fields - `segid`: segment id. - `original`: original sentence. - `translation`: Dictionary with pairs (source,target). - src_lg: sequence of text in source language. - tgt_lg: sequence of text in target language. - `scores`: list of DA scores by all annotators - the number of annotators may vary. [] if N/A (only for `ru-en/test`). - `mean`: average of DA scores. -10_000 if N/A (only for `ru-en/test`). - `z_scores`: list of z-standardized DA scores. [] if N/A (only for `ru-en/test`). - `z_mean`: average of z-standardized DA scores. -10_000 if N/A (only for `ru-en/test`). - `model_score`: NMT model score for sentence. -10_000 if N/A (only for `ru-en/test`). - `doc_id`: the name of the article where each original segment came from. - `nmt_output`: the actual output of the NMT model before any post-processing, corresponding to the log-probas in `word_probas` (the token is not printed, so the number of log-probabilities equals the number of tokens plus 1). - `word_probas`: log-probabilities from the NMT model for each decoded token including the token. ### Data Splits There are 7 configurations in this dataset (one for each available language pair). Each configuration is composed of 7K examples for training, 1K for validation and 1K for test. ## Dataset Creation ### Curation Rationale The original text is extracted from Wikipedia, Russian Reddit and Russian WikiQuotes. Translations are obtained using state-of-the-art NMT models built using the [fairseq toolkit](https://github.com/pytorch/fairseq) and annotated with Direct Assesment scores by professional translators. ### 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 Unknown ### Citation Information ``` Not available. ``` ### Contributions Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset.

wmt20_mlqe_task1

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated", "machine-generated"], "language_creators": ["found"], "language": ["de", "en", "et", "ne", "ro", "ru", "si", "zh"], "license": ["unknown"], "multilinguality": ["translation"], "size_categories": ["1K<n<10K"], "source_datasets": ["extended|reddit", "extended|wikipedia"], "task_categories": ["translation"], "task_ids": [], "pretty_name": "WMT20 - MultiLingual Quality Estimation (MLQE) Task1", "config_names": ["en-de", "en-zh", "et-en", "ne-en", "ro-en", "ru-en", "si-en"], "dataset_info": [{"config_name": "en-de", "features": [{"name": "segid", "dtype": "int32"}, {"name": "translation", "dtype": {"translation": {"languages": ["en", "de"]}}}, {"name": "scores", "sequence": "float32"}, {"name": "mean", "dtype": "float32"}, {"name": "z_scores", "sequence": "float32"}, {"name": "z_mean", "dtype": "float32"}, {"name": "model_score", "dtype": "float32"}, {"name": "doc_id", "dtype": "string"}, {"name": "nmt_output", "dtype": "string"}, {"name": "word_probas", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 3492361, "num_examples": 7000}, {"name": "test", "num_bytes": 524159, "num_examples": 1000}, {"name": "validation", "num_bytes": 522576, "num_examples": 1000}], "download_size": 2343124, "dataset_size": 4539096}, {"config_name": "en-zh", "features": [{"name": "segid", "dtype": "int32"}, {"name": "translation", "dtype": {"translation": {"languages": ["en", "zh"]}}}, {"name": "scores", "sequence": "float32"}, {"name": "mean", "dtype": "float32"}, {"name": "z_scores", "sequence": "float32"}, {"name": "z_mean", "dtype": "float32"}, {"name": "model_score", "dtype": "float32"}, {"name": "doc_id", "dtype": "string"}, {"name": "nmt_output", "dtype": "string"}, {"name": "word_probas", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 3286433, "num_examples": 7000}, {"name": "test", "num_bytes": 494994, "num_examples": 1000}, {"name": "validation", "num_bytes": 488477, "num_examples": 1000}], "download_size": 2557445, "dataset_size": 4269904}, {"config_name": "et-en", "features": [{"name": "segid", "dtype": "int32"}, {"name": "translation", "dtype": {"translation": {"languages": ["et", "en"]}}}, {"name": "scores", "sequence": "float32"}, {"name": "mean", "dtype": "float32"}, {"name": "z_scores", "sequence": "float32"}, {"name": "z_mean", "dtype": "float32"}, {"name": "model_score", "dtype": "float32"}, {"name": "doc_id", "dtype": "string"}, {"name": "nmt_output", "dtype": "string"}, {"name": "word_probas", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 3483669, "num_examples": 7000}, {"name": "test", "num_bytes": 527372, "num_examples": 1000}, {"name": "validation", "num_bytes": 531499, "num_examples": 1000}], "download_size": 2320067, "dataset_size": 4542540}, {"config_name": "ne-en", "features": [{"name": "segid", "dtype": "int32"}, {"name": "translation", "dtype": {"translation": {"languages": ["ne", "en"]}}}, {"name": "scores", "sequence": "float32"}, {"name": "mean", "dtype": "float32"}, {"name": "z_scores", "sequence": "float32"}, {"name": "z_mean", "dtype": "float32"}, {"name": "model_score", "dtype": "float32"}, {"name": "doc_id", "dtype": "string"}, {"name": "nmt_output", "dtype": "string"}, {"name": "word_probas", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 5313197, "num_examples": 7000}, {"name": "test", "num_bytes": 767637, "num_examples": 1000}, {"name": "validation", "num_bytes": 784784, "num_examples": 1000}], "download_size": 2913901, "dataset_size": 6865618}, {"config_name": "ro-en", "features": [{"name": "segid", "dtype": "int32"}, {"name": "translation", "dtype": {"translation": {"languages": ["ro", "en"]}}}, {"name": "scores", "sequence": "float32"}, {"name": "mean", "dtype": "float32"}, {"name": "z_scores", "sequence": "float32"}, {"name": "z_mean", "dtype": "float32"}, {"name": "model_score", "dtype": "float32"}, {"name": "doc_id", "dtype": "string"}, {"name": "nmt_output", "dtype": "string"}, {"name": "word_probas", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 3360498, "num_examples": 7000}, {"name": "test", "num_bytes": 500700, "num_examples": 1000}, {"name": "validation", "num_bytes": 507646, "num_examples": 1000}], "download_size": 2222103, "dataset_size": 4368844}, {"config_name": "si-en", "features": [{"name": "segid", "dtype": "int32"}, {"name": "translation", "dtype": {"translation": {"languages": ["si", "en"]}}}, {"name": "scores", "sequence": "float32"}, {"name": "mean", "dtype": "float32"}, {"name": "z_scores", "sequence": "float32"}, {"name": "z_mean", "dtype": "float32"}, {"name": "model_score", "dtype": "float32"}, {"name": "doc_id", "dtype": "string"}, {"name": "nmt_output", "dtype": "string"}, {"name": "word_probas", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 5135077, "num_examples": 7000}, {"name": "test", "num_bytes": 763730, "num_examples": 1000}, {"name": "validation", "num_bytes": 758271, "num_examples": 1000}], "download_size": 2841894, "dataset_size": 6657078}, {"config_name": "ru-en", "features": [{"name": "segid", "dtype": "int32"}, {"name": "translation", "dtype": {"translation": {"languages": ["ru", "en"]}}}, {"name": "scores", "sequence": "float32"}, {"name": "mean", "dtype": "float32"}, {"name": "z_scores", "sequence": "float32"}, {"name": "z_mean", "dtype": "float32"}, {"name": "model_score", "dtype": "float32"}, {"name": "doc_id", "dtype": "string"}, {"name": "nmt_output", "dtype": "string"}, {"name": "word_probas", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 3520776, "num_examples": 7000}, {"name": "test", "num_bytes": 478531, "num_examples": 1000}, {"name": "validation", "num_bytes": 499601, "num_examples": 1000}], "download_size": 2123684, "dataset_size": 4498908}]}

2024-01-18T11:18:32+00:00

TAGS #task_categories-translation #annotations_creators-expert-generated #annotations_creators-machine-generated #language_creators-found #multilinguality-translation #size_categories-1K<n<10K #source_datasets-extended|reddit #source_datasets-extended|wikipedia #language-German #language-English #language-Estonian #language-Nepali (macrolanguage) #language-Romanian #language-Russian #language-Sinhala #language-Chinese #license-unknown #region-us

# Dataset Card for WMT20 - MultiLingual Quality Estimation (MLQE) Task1 ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: WMT20 Quality Estimation Shared Task - Repository: Github repository - Paper: *Not available* ### Dataset Summary From the homepage: *This shared task (part of WMT20) will build on its previous editions to further examine automatic methods for estimating the quality of neural machine translation output at run-time, without relying on reference translations. As in previous years, we cover estimation at various levels. Important elements introduced this year include: a new task where sentences are annotated with Direct Assessment (DA) scores instead of labels based on post-editing; a new multilingual sentence-level dataset mainly from Wikipedia articles, where the source articles can be retrieved for document-wide context; the availability of NMT models to explore system-internal information for the task.* *Task 1 uses Wikipedia data for 6 language pairs that includes high-resource English--German (En-De) and English--Chinese (En-Zh), medium-resource Romanian--English (Ro-En) and Estonian--English (Et-En), and low-resource Sinhalese--English (Si-En) and Nepalese--English (Ne-En), as well as a dataset with a combination of Wikipedia articles and Reddit articles for Russian-English (En-Ru). The datasets were collected by translating sentences sampled from source language articles using state-of-the-art NMT models built using the fairseq toolkit and annotated with Direct Assessment (DA) scores by professional translators. Each sentence was annotated following the FLORES setup, which presents a form of DA, where at least three professional translators rate each sentence from 0-100 according to the perceived translation quality. DA scores are standardised using the z-score by rater. Participating systems are required to score sentences according to z-standardised DA scores.* ### Supported Tasks and Leaderboards From the homepage: *Sentence-level submissions will be evaluated in terms of the Pearson's correlation metric for the DA prediction agains human DA (z-standardised mean DA score, i.e. z_mean). These are the official evaluation scripts. The evaluation will focus on multilingual systems, i.e. systems that are able to provide predictions for all languages in the Wikipedia domain. Therefore, average Pearson correlation across all these languages will be used to rank QE systems. We will also evaluate QE systems on a per-language basis for those interested in particular languages.* ### Languages Eight languages are represented in this dataset: - English ('en') - German ('de') - Romanian ('ro') - Estonian ('et') - Nepalese ('ne') - Sinhala ('si') - Russian ('ru') ## Dataset Structure ### Data Instances An example looks like this: ### Data Fields - 'segid': segment id. - 'original': original sentence. - 'translation': Dictionary with pairs (source,target). - src_lg: sequence of text in source language. - tgt_lg: sequence of text in target language. - 'scores': list of DA scores by all annotators - the number of annotators may vary. [] if N/A (only for 'ru-en/test'). - 'mean': average of DA scores. -10_000 if N/A (only for 'ru-en/test'). - 'z_scores': list of z-standardized DA scores. [] if N/A (only for 'ru-en/test'). - 'z_mean': average of z-standardized DA scores. -10_000 if N/A (only for 'ru-en/test'). - 'model_score': NMT model score for sentence. -10_000 if N/A (only for 'ru-en/test'). - 'doc_id': the name of the article where each original segment came from. - 'nmt_output': the actual output of the NMT model before any post-processing, corresponding to the log-probas in 'word_probas' (the token is not printed, so the number of log-probabilities equals the number of tokens plus 1). - 'word_probas': log-probabilities from the NMT model for each decoded token including the token. ### Data Splits There are 7 configurations in this dataset (one for each available language pair). Each configuration is composed of 7K examples for training, 1K for validation and 1K for test. ## Dataset Creation ### Curation Rationale The original text is extracted from Wikipedia, Russian Reddit and Russian WikiQuotes. Translations are obtained using state-of-the-art NMT models built using the fairseq toolkit and annotated with Direct Assesment scores by professional translators. ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information Unknown ### Contributions Thanks to @VictorSanh for adding this dataset.

ae5cd66417e7b6c936e9eb0ce47cfef430629a15

# Dataset Card for WMT20 - MultiLingual Quality Estimation (MLQE) Task2 ## 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:** [WMT20 Quality Estimation Shared Task](http://www.statmt.org/wmt20/quality-estimation-task.html) - **Repository**: [Github repository](https://github.com/deep-spin/deep-spin.github.io/tree/master/docs/data/wmt2020_qe) - **Paper:** *Not available* ### Dataset Summary From the homepage: *This shared task (part of WMT20) will build on its previous editions to further examine automatic methods for estimating the quality of neural machine translation output at run-time, without relying on reference translations. As in previous years, we cover estimation at various levels. Important elements introduced this year include: a new task where sentences are annotated with Direct Assessment (DA) scores instead of labels based on post-editing; a new multilingual sentence-level dataset mainly from Wikipedia articles, where the source articles can be retrieved for document-wide context; the availability of NMT models to explore system-internal information for the task.* *Task 1 evaluates the application of QE for post-editing purposes. It consists of predicting:* - ***Word-level tags.*** *This is done both on source side (to detect which words caused errors) and target side (to detect mistranslated or missing words).* - ***Target.*** *Each token is tagged as either `OK` or `BAD`. Additionally, each gap between two words is tagged as `BAD` if one or more missing words should have been there, and `OK` otherwise. Note that number of tags for each target sentence is 2*N+1, where N is the number of tokens in the sentence.* - ***Source.*** *Tokens are tagged as `OK` if they were correctly translated, and `BAD` otherwise. Gaps are not tagged.* - ***Sentence-level HTER scores.*** *HTER (Human Translation Error Rate) is the ratio between the number of edits (insertions/deletions/replacements) needed and the reference translation length.* ### Supported Tasks and Leaderboards From the homepage: *For sentence-level QE, submissions are evaluated in terms of the Pearson's correlation metric for the sentence-level HTER prediction. For word-level QE, they will be evaluated in terms of MCC ([Matthews correlation coefficient](https://en.wikipedia.org/wiki/Matthews_correlation_coefficient)). These are the [official evaluation scripts](https://github.com/sheffieldnlp/qe-eval-scripts).* ### Languages There are two language pairs in this dataset: - English - German (`en` - `de`) - German - Chinese (`en` - `zh`) ## Dataset Structure ### Data Instances An example looks like this: ``` { 'translation': { 'en': 'favorite fish include cod , salmon , winter flounder , haddock , striped bass , pollock , hake , bluefish , and , in southern New England , Tautog .', 'de': 'zu den Lieblingsfischen gehören Kabeljau , Lachs , Winterflounder , Schellfisch , gestreifter Bass , Pollock , Seehecht , Rotbarsch und in Südengland Tautog .', } 'src_tags': [1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1], 'mt_tags': [1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1], 'pe': 'zu den Lieblingsfischen zählen Kabeljau , Lachs , Winterflunder , Schellfisch , Wolfsbarsch , Pollock , Seehecht , Bluefish und im Süden Neuenglands Tautog .', 'hter': 0.3199999928474426, 'alignments': [[2, 0], [2, 1], [2, 3], [3, 2], [3, 4], [4, 5], [5, 6], [6, 5], [7, 6], [8, 6], [9, 7], [10, 8], [10, 10], [11, 9], [12, 12], [13, 13], [14, 11], [15, 12], [15, 15], [16, 14], [17, 17], [19, 16], [20, 16], [21, 20], [22, 18], [23, 19], [23, 21], [24, 22], [25, 21], [26, 22], [27, 22], [28, 23], [29, 24]], } ``` ### Data Fields - `translation`: Dictionary with pairs (source,target). - src_lg: sequence of text in source language. - tgt_lg: sequence of text in target language. - `src_tags`: source word-level tags. `0`=`BAD`, `1`=`OK`. `[]` if N/A (only for test). - `mt_tags`: target word-level tags. `0`=`BAD`, `1`=`OK`. `[]` if N/A (only for test). - `pe`: post-edited version of NMT output. `""` if N/A (only for test). - `hter`: human translation error rate. `-10_000` if N/A (only for test). - `alignments`: Word aligments. List of pairs of integers. ### Data Splits There are 2 configurations in this dataset (one for each available language pair). Each configuration is composed of 7K examples for training, 1K for validation and 1K for (blind) test. ## Dataset Creation ### Curation Rationale The original text is extracted from Wikipedia. From the homepage: *Word-level labels have been obtained by using the alignments provided by the [TER](http://www.cs.umd.edu/~snover/tercom/) tool (settings: tokenised, case insensitive, exact matching only, disabling shifts by using the `-d 0` option) between machine translations and their post-edited versions. Shifts (word order errors) were not annotated as such (but rather as deletions + insertions) to avoid introducing noise in the annotation.* *HTER values are obtained deterministically from word-level tags. However, when computing HTER, we allow shifts in TER.* *The baseline system is a neural predictor-estimator approach implemented in [OpenKiwi](https://github.com/Unbabel/OpenKiwi) ([Kepler at al., 2019](https://arxiv.org/abs/1902.08646)), where the predictor model will be trained on the parallel data used to train the NMT model.* ### 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 Unknown ### Citation Information ``` Not available. ``` ### Contributions Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset.

wmt20_mlqe_task2

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated", "machine-generated"], "language_creators": ["found"], "language": ["de", "en", "zh"], "license": ["unknown"], "multilinguality": ["translation"], "size_categories": ["1K<n<10K"], "source_datasets": ["extended|wikipedia"], "task_categories": ["translation", "text-classification"], "task_ids": [], "pretty_name": "WMT20 - MultiLingual Quality Estimation (MLQE) Task2", "config_names": ["en-de", "en-zh"], "tags": ["translation-quality-estimation"], "dataset_info": [{"config_name": "en-de", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["en", "de"]}}}, {"name": "src_tags", "sequence": {"class_label": {"names": {"0": "BAD", "1": "OK"}}}}, {"name": "mt_tags", "sequence": {"class_label": {"names": {"0": "BAD", "1": "OK"}}}}, {"name": "pe", "dtype": "string"}, {"name": "hter", "dtype": "float32"}, {"name": "alignments", "sequence": {"sequence": "int32"}}], "splits": [{"name": "train", "num_bytes": 6463930, "num_examples": 7000}, {"name": "test", "num_bytes": 425582, "num_examples": 1000}, {"name": "validation", "num_bytes": 927616, "num_examples": 1000}], "download_size": 1377020, "dataset_size": 7817128}, {"config_name": "en-zh", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["en", "zh"]}}}, {"name": "src_tags", "sequence": {"class_label": {"names": {"0": "BAD", "1": "OK"}}}}, {"name": "mt_tags", "sequence": {"class_label": {"names": {"0": "BAD", "1": "OK"}}}}, {"name": "pe", "dtype": "string"}, {"name": "hter", "dtype": "float32"}, {"name": "alignments", "sequence": {"sequence": "int32"}}], "splits": [{"name": "train", "num_bytes": 6786898, "num_examples": 7000}, {"name": "test", "num_bytes": 443740, "num_examples": 1000}, {"name": "validation", "num_bytes": 954710, "num_examples": 1000}], "download_size": 1564953, "dataset_size": 8185348}]}

2024-01-18T11:18:34+00:00

TAGS #task_categories-translation #task_categories-text-classification #annotations_creators-expert-generated #annotations_creators-machine-generated #language_creators-found #multilinguality-translation #size_categories-1K<n<10K #source_datasets-extended|wikipedia #language-German #language-English #language-Chinese #license-unknown #translation-quality-estimation #arxiv-1902.08646 #region-us

# Dataset Card for WMT20 - MultiLingual Quality Estimation (MLQE) Task2 ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: WMT20 Quality Estimation Shared Task - Repository: Github repository - Paper: *Not available* ### Dataset Summary From the homepage: *This shared task (part of WMT20) will build on its previous editions to further examine automatic methods for estimating the quality of neural machine translation output at run-time, without relying on reference translations. As in previous years, we cover estimation at various levels. Important elements introduced this year include: a new task where sentences are annotated with Direct Assessment (DA) scores instead of labels based on post-editing; a new multilingual sentence-level dataset mainly from Wikipedia articles, where the source articles can be retrieved for document-wide context; the availability of NMT models to explore system-internal information for the task.* *Task 1 evaluates the application of QE for post-editing purposes. It consists of predicting:* - *Word-level tags.* *This is done both on source side (to detect which words caused errors) and target side (to detect mistranslated or missing words).* - *Target.* *Each token is tagged as either 'OK' or 'BAD'. Additionally, each gap between two words is tagged as 'BAD' if one or more missing words should have been there, and 'OK' otherwise. Note that number of tags for each target sentence is 2*N+1, where N is the number of tokens in the sentence.* - *Source.* *Tokens are tagged as 'OK' if they were correctly translated, and 'BAD' otherwise. Gaps are not tagged.* - *Sentence-level HTER scores.* *HTER (Human Translation Error Rate) is the ratio between the number of edits (insertions/deletions/replacements) needed and the reference translation length.* ### Supported Tasks and Leaderboards From the homepage: *For sentence-level QE, submissions are evaluated in terms of the Pearson's correlation metric for the sentence-level HTER prediction. For word-level QE, they will be evaluated in terms of MCC (Matthews correlation coefficient). These are the official evaluation scripts.* ### Languages There are two language pairs in this dataset: - English - German ('en' - 'de') - German - Chinese ('en' - 'zh') ## Dataset Structure ### Data Instances An example looks like this: ### Data Fields - 'translation': Dictionary with pairs (source,target). - src_lg: sequence of text in source language. - tgt_lg: sequence of text in target language. - 'src_tags': source word-level tags. '0'='BAD', '1'='OK'. '[]' if N/A (only for test). - 'mt_tags': target word-level tags. '0'='BAD', '1'='OK'. '[]' if N/A (only for test). - 'pe': post-edited version of NMT output. '""' if N/A (only for test). - 'hter': human translation error rate. '-10_000' if N/A (only for test). - 'alignments': Word aligments. List of pairs of integers. ### Data Splits There are 2 configurations in this dataset (one for each available language pair). Each configuration is composed of 7K examples for training, 1K for validation and 1K for (blind) test. ## Dataset Creation ### Curation Rationale The original text is extracted from Wikipedia. From the homepage: *Word-level labels have been obtained by using the alignments provided by the TER tool (settings: tokenised, case insensitive, exact matching only, disabling shifts by using the '-d 0' option) between machine translations and their post-edited versions. Shifts (word order errors) were not annotated as such (but rather as deletions + insertions) to avoid introducing noise in the annotation.* *HTER values are obtained deterministically from word-level tags. However, when computing HTER, we allow shifts in TER.* *The baseline system is a neural predictor-estimator approach implemented in OpenKiwi (Kepler at al., 2019), where the predictor model will be trained on the parallel data used to train the NMT model.* ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information Unknown ### Contributions Thanks to @VictorSanh for adding this dataset.

a382331a13d2abc25f281f907ba6748c9ff2d853

# Dataset Card for WMT20 - MultiLingual Quality Estimation (MLQE) Task3 ## 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:** [WMT20 Quality Estimation Shared Task](http://www.statmt.org/wmt20/quality-estimation-task.html) - **Repository**: [Github repository](https://github.com/deep-spin/deep-spin.github.io/tree/master/docs/data/wmt2020_qe) - **Paper:** *Not available* ### Dataset Summary From the homepage: *This shared task (part of WMT20) will build on its previous editions to further examine automatic methods for estimating the quality of neural machine translation output at run-time, without relying on reference translations. As in previous years, we cover estimation at various levels. Important elements introduced this year include: a new task where sentences are annotated with Direct Assessment (DA) scores instead of labels based on post-editing; a new multilingual sentence-level dataset mainly from Wikipedia articles, where the source articles can be retrieved for document-wide context; the availability of NMT models to explore system-internal information for the task.* *The goal of this task 3 is to predict document-level quality scores as well as fine-grained annotations.* *Each document has a product title and its description, and is annotated for translation errors according to the MQM framework. Each error annotation has:* - ***Word span(s).*** *Errors may consist of one or more words, not necessarily contiguous.* - ***Severity.*** *An error can be minor (if it doesn't lead to a loss of meaning and it doesn't confuse or mislead the user), major (if it changes the meaning) or critical (if it changes the meaning and carry any type of implication, or could be seen as offensive).* - ***Type.*** *A label specifying the error type, such as wrong word order, missing words, agreement, etc. They may provide additional information, but systems don't need to predict them.* ### Supported Tasks and Leaderboards From the homepage: *Submissions will be evaluated as in Task 1, in terms of Pearson's correlation between the true and predicted MQM document-level scores. Additionally, the predicted annotations will be evaluated in terms of their F1 scores with respect to the gold annotations. The [official evaluation scripts](https://github.com/sheffieldnlp/qe-eval-scripts) are available.* ### Languages There is a single language pair in the dataset: English (`en`) - French (`fr`). ## Dataset Structure ### Data Instances An example looks like this: ``` { 'document_id': 'B0000568SY', 'source_segments': ['Razor Scooter Replacement Wheels Set with Bearings', 'Scooter Wheels w/Bearings-Blue'], 'source_tokenized': ['Razor Scooter Replacement Wheels Set with Bearings', 'Scooter Wheels w / Bearings-Blue'], 'mt_segments': ['Roues de rechange Razor Scooter sertie de roulements', 'Roues de scooter w/roulements-bleu'], 'mt_tokenized': ['Roues de rechange Razor Scooter sertie de roulements', 'Roues de scooter w / roulements-bleu'], 'annotations': { 'segment_id': [[0], [1], [1], [0, 0], [0], [1], [1]], 'annotation_start': [[42], [19], [9], [0, 32], [9], [17], [30]], 'annotation_length': [[10], [10], [7], [5, 6], [8], [1], [4]], 'severity': [0, 0, 0, 0, 0, 1, 0], 'severity_weight': [1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0] 'category': [3, 3, 3, 1, 3, 36, 3], }, 'token_annotations': { 'category': [3, 3, 3, 1, 3, 36, 3], 'first_token': [[7], [5], [2], [0, 5], [2], [3], [5]], 'last_token': [[7], [5], [2], [0, 5], [2], [3], [5]], 'segment_id': [[0], [1], [1], [0, 0], [0], [1], [1]], 'severity': [0, 0, 0, 0, 0, 1, 0], 'token_after_gap': [[-1], [-1], [-1], [-1, -1], [-1], [-1], [-1]] }, 'token_index': [[[0, 5], [6, 2], [9, 8], [18, 5], [24, 7], [32, 6], [39, 2], [42, 10]], [[0, 5], [6, 2], [9, 7], [17, 1], [18, 1], [19, 15]]], 'total_words': 16 } ``` ### Data Fields - `document_id`: the document id (name of the folder). - `source_segments`: the original source text, one sentence per line (i.e. per element of the list). - `source_tokenized`: a tokenized version of `source_segments`. - `mt_segments`: the original machine-translated text, one sentence per line (i.e. per element of the list). - `mt_tokenized`: a tokenized version of `mt_segments`. Default value is `[]` when this information is not available (it happens 3 times in the train set: `B0001BW0PQ`, `B0001GS19U` and `B000A6SMJ0`). - `annotations`: error annotations for the document. Each item of the list corresponds to an error annotation, which in turn may contain one or more error spans. Error fields are encoded in a dictionary. In the case of a multi-span error, multiple starting positions and lengths are encoded in the list. Note that these positions points to `mt.segments`, not `mt_tokenized`. - `segment_id`: List of list of integers. Id of each error. - `annotation_start`: List of list of integers. Start of each error. - `annotation_length`: List of list of intergers. Length of each error. - `severity`: List of one hot. Severity category of each error. - `severity_weight`: List of floats. Severity weight of each error. - `category`: List of one hot. Category of each error. See the 45 categories in `_ANNOTATION_CATEGORIES_MAPPING`. - `token_annotations`: tokenized version of `annotations`. Each error span that contains one or more tokens has a "first token" and "last token". Again, multi-span errors have their first and last tokens encoded in a list. When a span is over a gap between two tokens, the "first" and "last" positions are `-1` (encoded as `-` in the original data), and instead the `token_after_gap` column points to the token immediately after the gap. In case of a gap occurring at the end of the sentence, this value will be equal to the number of tokens. - `segment_id`: List of list of integers. Id of each error. - `first_token`: List of list of integers. Start of each error. - `last_token`: List of list of intergers. End of each error. - `token_after_gap`: List of list of integers. Token after gap of each error. - `severity`: List of one hot. Severity category of each error. - `category`: List of one hot. Category of each error. See the 45 categories in `_ANNOTATION_CATEGORIES_MAPPING`. - `token_index`: a mapping of tokens to their start and ending positions in `mt_segments`. For each token, a start and end value are encoded in a list of length 2, and all tokens represent one item in the list. - `total_words`: total number of words in the document ``` _ANNOTATION_CATEGORIES_MAPPING = { 0: 'Addition', 1: 'Agreement', 2: 'Ambiguous Translation', 3: 'Capitalization', 4: 'Character Encoding', 5: 'Company Terminology', 6: 'Date/Time', 7: 'Diacritics', 8: 'Duplication', 9: 'False Friend', 10: 'Grammatical Register', 11: 'Hyphenation', 12: 'Inconsistency', 13: 'Lexical Register', 14: 'Lexical Selection', 15: 'Named Entity', 16: 'Number', 17: 'Omitted Auxiliary Verb', 18: 'Omitted Conjunction', 19: 'Omitted Determiner', 20: 'Omitted Preposition', 21: 'Omitted Pronoun', 22: 'Orthography', 23: 'Other POS Omitted', 24: 'Over-translation', 25: 'Overly Literal', 26: 'POS', 27: 'Punctuation', 28: "Shouldn't Have Been Translated", 29: "Shouldn't have been translated", 30: 'Spelling', 31: 'Tense/Mood/Aspect', 32: 'Under-translation', 33: 'Unidiomatic', 34: 'Unintelligible', 35: 'Unit Conversion', 36: 'Untranslated', 37: 'Whitespace', 38: 'Word Order', 39: 'Wrong Auxiliary Verb', 40: 'Wrong Conjunction', 41: 'Wrong Determiner', 42: 'Wrong Language Variety', 43: 'Wrong Preposition', 44: 'Wrong Pronoun' } ``` ### Data Splits The dataset contains 1,448 documents for training, 200 documents for validation and 180 for (blind) test (all English-French). ## Dataset Creation ### Curation Rationale The data is dervied from the [Amazon Product Reviews dataset](http://jmcauley.ucsd.edu/data/amazon/). ### 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 Unknown ### Citation Information ``` Not available. ``` ### Contributions Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset.

wmt20_mlqe_task3

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated", "machine-generated"], "language_creators": ["found"], "language": ["en", "fr"], "license": ["unknown"], "multilinguality": ["translation"], "size_categories": ["1K<n<10K"], "source_datasets": ["extended|amazon_us_reviews"], "task_categories": ["translation"], "task_ids": [], "pretty_name": "WMT20 - MultiLingual Quality Estimation (MLQE) Task3", "dataset_info": {"features": [{"name": "document_id", "dtype": "string"}, {"name": "source_segments", "sequence": "string"}, {"name": "source_tokenized", "sequence": "string"}, {"name": "mt_segments", "sequence": "string"}, {"name": "mt_tokenized", "sequence": "string"}, {"name": "annotations", "sequence": [{"name": "segment_id", "sequence": "int32"}, {"name": "annotation_start", "sequence": "int32"}, {"name": "annotation_length", "sequence": "int32"}, {"name": "severity", "dtype": {"class_label": {"names": {"0": "minor", "1": "major", "2": "critical"}}}}, {"name": "severity_weight", "dtype": "float32"}, {"name": "category", "dtype": {"class_label": {"names": {"0": "Addition", "1": "Agreement", "2": "Ambiguous Translation", "3": "Capitalization", "4": "Character Encoding", "5": "Company Terminology", "6": "Date/Time", "7": "Diacritics", "8": "Duplication", "9": "False Friend", "10": "Grammatical Register", "11": "Hyphenation", "12": "Inconsistency", "13": "Lexical Register", "14": "Lexical Selection", "15": "Named Entity", "16": "Number", "17": "Omitted Auxiliary Verb", "18": "Omitted Conjunction", "19": "Omitted Determiner", "20": "Omitted Preposition", "21": "Omitted Pronoun", "22": "Orthography", "23": "Other POS Omitted", "24": "Over-translation", "25": "Overly Literal", "26": "POS", "27": "Punctuation", "28": "Shouldn't Have Been Translated", "29": "Shouldn't have been translated", "30": "Spelling", "31": "Tense/Mood/Aspect", "32": "Under-translation", "33": "Unidiomatic", "34": "Unintelligible", "35": "Unit Conversion", "36": "Untranslated", "37": "Whitespace", "38": "Word Order", "39": "Wrong Auxiliary Verb", "40": "Wrong Conjunction", "41": "Wrong Determiner", "42": "Wrong Language Variety", "43": "Wrong Preposition", "44": "Wrong Pronoun"}}}}]}, {"name": "token_annotations", "sequence": [{"name": "segment_id", "sequence": "int32"}, {"name": "first_token", "sequence": "int32"}, {"name": "last_token", "sequence": "int32"}, {"name": "token_after_gap", "sequence": "int32"}, {"name": "severity", "dtype": {"class_label": {"names": {"0": "minor", "1": "major", "2": "critical"}}}}, {"name": "category", "dtype": {"class_label": {"names": {"0": "Addition", "1": "Agreement", "2": "Ambiguous Translation", "3": "Capitalization", "4": "Character Encoding", "5": "Company Terminology", "6": "Date/Time", "7": "Diacritics", "8": "Duplication", "9": "False Friend", "10": "Grammatical Register", "11": "Hyphenation", "12": "Inconsistency", "13": "Lexical Register", "14": "Lexical Selection", "15": "Named Entity", "16": "Number", "17": "Omitted Auxiliary Verb", "18": "Omitted Conjunction", "19": "Omitted Determiner", "20": "Omitted Preposition", "21": "Omitted Pronoun", "22": "Orthography", "23": "Other POS Omitted", "24": "Over-translation", "25": "Overly Literal", "26": "POS", "27": "Punctuation", "28": "Shouldn't Have Been Translated", "29": "Shouldn't have been translated", "30": "Spelling", "31": "Tense/Mood/Aspect", "32": "Under-translation", "33": "Unidiomatic", "34": "Unintelligible", "35": "Unit Conversion", "36": "Untranslated", "37": "Whitespace", "38": "Word Order", "39": "Wrong Auxiliary Verb", "40": "Wrong Conjunction", "41": "Wrong Determiner", "42": "Wrong Language Variety", "43": "Wrong Preposition", "44": "Wrong Pronoun"}}}}]}, {"name": "token_index", "sequence": {"sequence": {"sequence": "int32"}}}, {"name": "total_words", "dtype": "int32"}], "config_name": "plain_text", "splits": [{"name": "train", "num_bytes": 10762355, "num_examples": 1448}, {"name": "test", "num_bytes": 745260, "num_examples": 180}, {"name": "validation", "num_bytes": 1646596, "num_examples": 200}], "download_size": 3534634, "dataset_size": 13154211}}

2024-01-18T11:18:35+00:00

TAGS #task_categories-translation #annotations_creators-expert-generated #annotations_creators-machine-generated #language_creators-found #multilinguality-translation #size_categories-1K<n<10K #source_datasets-extended|amazon_us_reviews #language-English #language-French #license-unknown #region-us

# Dataset Card for WMT20 - MultiLingual Quality Estimation (MLQE) Task3 ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: WMT20 Quality Estimation Shared Task - Repository: Github repository - Paper: *Not available* ### Dataset Summary From the homepage: *This shared task (part of WMT20) will build on its previous editions to further examine automatic methods for estimating the quality of neural machine translation output at run-time, without relying on reference translations. As in previous years, we cover estimation at various levels. Important elements introduced this year include: a new task where sentences are annotated with Direct Assessment (DA) scores instead of labels based on post-editing; a new multilingual sentence-level dataset mainly from Wikipedia articles, where the source articles can be retrieved for document-wide context; the availability of NMT models to explore system-internal information for the task.* *The goal of this task 3 is to predict document-level quality scores as well as fine-grained annotations.* *Each document has a product title and its description, and is annotated for translation errors according to the MQM framework. Each error annotation has:* - *Word span(s).* *Errors may consist of one or more words, not necessarily contiguous.* - *Severity.* *An error can be minor (if it doesn't lead to a loss of meaning and it doesn't confuse or mislead the user), major (if it changes the meaning) or critical (if it changes the meaning and carry any type of implication, or could be seen as offensive).* - *Type.* *A label specifying the error type, such as wrong word order, missing words, agreement, etc. They may provide additional information, but systems don't need to predict them.* ### Supported Tasks and Leaderboards From the homepage: *Submissions will be evaluated as in Task 1, in terms of Pearson's correlation between the true and predicted MQM document-level scores. Additionally, the predicted annotations will be evaluated in terms of their F1 scores with respect to the gold annotations. The official evaluation scripts are available.* ### Languages There is a single language pair in the dataset: English ('en') - French ('fr'). ## Dataset Structure ### Data Instances An example looks like this: ### Data Fields - 'document_id': the document id (name of the folder). - 'source_segments': the original source text, one sentence per line (i.e. per element of the list). - 'source_tokenized': a tokenized version of 'source_segments'. - 'mt_segments': the original machine-translated text, one sentence per line (i.e. per element of the list). - 'mt_tokenized': a tokenized version of 'mt_segments'. Default value is '[]' when this information is not available (it happens 3 times in the train set: 'B0001BW0PQ', 'B0001GS19U' and 'B000A6SMJ0'). - 'annotations': error annotations for the document. Each item of the list corresponds to an error annotation, which in turn may contain one or more error spans. Error fields are encoded in a dictionary. In the case of a multi-span error, multiple starting positions and lengths are encoded in the list. Note that these positions points to 'mt.segments', not 'mt_tokenized'. - 'segment_id': List of list of integers. Id of each error. - 'annotation_start': List of list of integers. Start of each error. - 'annotation_length': List of list of intergers. Length of each error. - 'severity': List of one hot. Severity category of each error. - 'severity_weight': List of floats. Severity weight of each error. - 'category': List of one hot. Category of each error. See the 45 categories in '_ANNOTATION_CATEGORIES_MAPPING'. - 'token_annotations': tokenized version of 'annotations'. Each error span that contains one or more tokens has a "first token" and "last token". Again, multi-span errors have their first and last tokens encoded in a list. When a span is over a gap between two tokens, the "first" and "last" positions are '-1' (encoded as '-' in the original data), and instead the 'token_after_gap' column points to the token immediately after the gap. In case of a gap occurring at the end of the sentence, this value will be equal to the number of tokens. - 'segment_id': List of list of integers. Id of each error. - 'first_token': List of list of integers. Start of each error. - 'last_token': List of list of intergers. End of each error. - 'token_after_gap': List of list of integers. Token after gap of each error. - 'severity': List of one hot. Severity category of each error. - 'category': List of one hot. Category of each error. See the 45 categories in '_ANNOTATION_CATEGORIES_MAPPING'. - 'token_index': a mapping of tokens to their start and ending positions in 'mt_segments'. For each token, a start and end value are encoded in a list of length 2, and all tokens represent one item in the list. - 'total_words': total number of words in the document ### Data Splits The dataset contains 1,448 documents for training, 200 documents for validation and 180 for (blind) test (all English-French). ## Dataset Creation ### Curation Rationale The data is dervied from the Amazon Product Reviews dataset. ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information Unknown ### Contributions Thanks to @VictorSanh for adding this dataset.

861aac88b2c6247dd93ade8b1c189ce714627750

# Dataset Card for "wmt_t2t" ## 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/tensorflow/tensor2tensor/blob/master/tensor2tensor/data_generators/translate_ende.py](https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/data_generators/translate_ende.py) - **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:** 1.73 GB - **Size of the generated dataset:** 1.39 GB - **Total amount of disk used:** 3.11 GB ### Dataset Summary The WMT EnDe Translate dataset used by the Tensor2Tensor library. 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("wmt_t2t", "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 #### de-en - **Size of downloaded dataset files:** 1.73 GB - **Size of the generated dataset:** 1.39 GB - **Total amount of disk used:** 3.11 GB An example of 'validation' looks as follows. ``` { "translation": { "de": "Just a test sentence.", "en": "Just a test sentence." } } ``` ### Data Fields The data fields are the same among all splits. #### de-en - `translation`: a multilingual `string` variable, with possible languages including `de`, `en`. ### Data Splits |name | train |validation|test| |-----|------:|---------:|---:| |de-en|4592289| 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.

wmt_t2t

2022-03-02T23:29:22+00:00

{"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["de", "en"], "license": ["unknown"], "multilinguality": ["translation"], "size_categories": ["10M<n<100M"], "source_datasets": ["extended|europarl_bilingual", "extended|news_commentary", "extended|opus_paracrawl", "extended|un_multi"], "task_categories": ["translation"], "task_ids": [], "pretty_name": "WMT T2T", "dataset_info": {"features": [{"name": "translation", "dtype": {"translation": {"languages": ["de", "en"]}}}], "config_name": "de-en", "splits": [{"name": "train", "num_bytes": 1385110179, "num_examples": 4592289}, {"name": "validation", "num_bytes": 736415, "num_examples": 3000}, {"name": "test", "num_bytes": 777334, "num_examples": 3003}], "download_size": 1728762345, "dataset_size": 1386623928}}

2024-02-05T11:41:05+00:00

TAGS #task_categories-translation #annotations_creators-no-annotation #language_creators-found #multilinguality-translation #size_categories-10M<n<100M #source_datasets-extended|europarl_bilingual #source_datasets-extended|news_commentary #source_datasets-extended|opus_paracrawl #source_datasets-extended|un_multi #language-German #language-English #license-unknown #region-us

Dataset Card for "wmt\_t2t" =========================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: * Point of Contact: * Size of downloaded dataset files: 1.73 GB * Size of the generated dataset: 1.39 GB * Total amount of disk used: 3.11 GB ### Dataset Summary The WMT EnDe Translate dataset used by the Tensor2Tensor library. Translation dataset based on the data from URL. 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: ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### de-en * Size of downloaded dataset files: 1.73 GB * Size of the generated dataset: 1.39 GB * Total amount of disk used: 3.11 GB An example of 'validation' looks as follows. ### Data Fields The data fields are the same among all splits. #### de-en * 'translation': a multilingual 'string' variable, with possible languages including 'de', 'en'. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @thomwolf, @patrickvonplaten for adding this dataset.

be8fbd57399293da6f9aad56a793bce5bf1fd30e

# Dataset Card for "wnut_17" ## 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://noisy-text.github.io/2017/emerging-rare-entities.html](http://noisy-text.github.io/2017/emerging-rare-entities.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:** 0.80 MB - **Size of the generated dataset:** 1.74 MB - **Total amount of disk used:** 2.55 MB ### Dataset Summary WNUT 17: Emerging and Rare entity recognition This shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions. Named entities form the basis of many modern approaches to other tasks (like event clustering and summarisation), but recall on them is a real problem in noisy text - even among annotators. This drop tends to be due to novel entities and surface forms. Take for example the tweet “so.. kktny in 30 mins?” - even human experts find entity kktny hard to detect and resolve. This task will evaluate the ability to detect and classify novel, emerging, singleton named entities in noisy text. The goal of this task is to provide a definition of emerging and of rare entities, and based on that, also datasets for detecting these entities. ### 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 - **Size of downloaded dataset files:** 0.80 MB - **Size of the generated dataset:** 1.74 MB - **Total amount of disk used:** 2.55 MB An example of 'train' looks as follows. ``` { "id": "0", "ner_tags": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0], "tokens": ["@paulwalk", "It", "'s", "the", "view", "from", "where", "I", "'m", "living", "for", "two", "weeks", ".", "Empire", "State", "Building", "=", "ESB", ".", "Pretty", "bad", "storm", "here", "last", "evening", "."] } ``` ### Data Fields The data fields are the same among all splits: - `id` (`string`): ID of the example. - `tokens` (`list` of `string`): Tokens of the example text. - `ner_tags` (`list` of class labels): NER tags of the tokens (using IOB2 format), with possible values: - 0: `O` - 1: `B-corporation` - 2: `I-corporation` - 3: `B-creative-work` - 4: `I-creative-work` - 5: `B-group` - 6: `I-group` - 7: `B-location` - 8: `I-location` - 9: `B-person` - 10: `I-person` - 11: `B-product` - 12: `I-product` ### Data Splits |train|validation|test| |----:|---------:|---:| | 3394| 1009|1287| ## 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{derczynski-etal-2017-results, title = "Results of the {WNUT}2017 Shared Task on Novel and Emerging Entity Recognition", author = "Derczynski, Leon and Nichols, Eric and van Erp, Marieke and Limsopatham, Nut", booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text", month = sep, year = "2017", address = "Copenhagen, Denmark", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/W17-4418", doi = "10.18653/v1/W17-4418", pages = "140--147", abstract = "This shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions. Named entities form the basis of many modern approaches to other tasks (like event clustering and summarization), but recall on them is a real problem in noisy text - even among annotators. This drop tends to be due to novel entities and surface forms. Take for example the tweet {``}so.. kktny in 30 mins?!{''} {--} even human experts find the entity {`}kktny{'} hard to detect and resolve. The goal of this task is to provide a definition of emerging and of rare entities, and based on that, also datasets for detecting these entities. The task as described in this paper evaluated the ability of participating entries to detect and classify novel and emerging named entities in noisy text.", } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@stefan-it](https://github.com/stefan-it), [@lewtun](https://github.com/lewtun), [@jplu](https://github.com/jplu) for adding this dataset.

wnut_17

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "paperswithcode_id": "wnut-2017-emerging-and-rare-entity", "pretty_name": "WNUT 17", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-corporation", "2": "I-corporation", "3": "B-creative-work", "4": "I-creative-work", "5": "B-group", "6": "I-group", "7": "B-location", "8": "I-location", "9": "B-person", "10": "I-person", "11": "B-product", "12": "I-product"}}}}], "config_name": "wnut_17", "splits": [{"name": "train", "num_bytes": 1078379, "num_examples": 3394}, {"name": "validation", "num_bytes": 259383, "num_examples": 1009}, {"name": "test", "num_bytes": 405536, "num_examples": 1287}], "download_size": 800955, "dataset_size": 1743298}}

2024-01-18T11:18:37+00:00

TAGS #task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc-by-4.0 #region-us

Dataset Card for "wnut\_17" =========================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: * Point of Contact: * Size of downloaded dataset files: 0.80 MB * Size of the generated dataset: 1.74 MB * Total amount of disk used: 2.55 MB ### Dataset Summary WNUT 17: Emerging and Rare entity recognition This shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions. Named entities form the basis of many modern approaches to other tasks (like event clustering and summarisation), but recall on them is a real problem in noisy text - even among annotators. This drop tends to be due to novel entities and surface forms. Take for example the tweet “so.. kktny in 30 mins?” - even human experts find entity kktny hard to detect and resolve. This task will evaluate the ability to detect and classify novel, emerging, singleton named entities in noisy text. The goal of this task is to provide a definition of emerging and of rare entities, and based on that, also datasets for detecting these entities. ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances * Size of downloaded dataset files: 0.80 MB * Size of the generated dataset: 1.74 MB * Total amount of disk used: 2.55 MB An example of 'train' looks as follows. ### Data Fields The data fields are the same among all splits: * 'id' ('string'): ID of the example. * 'tokens' ('list' of 'string'): Tokens of the example text. * 'ner\_tags' ('list' of class labels): NER tags of the tokens (using IOB2 format), with possible values: + 0: 'O' + 1: 'B-corporation' + 2: 'I-corporation' + 3: 'B-creative-work' + 4: 'I-creative-work' + 5: 'B-group' + 6: 'I-group' + 7: 'B-location' + 8: 'I-location' + 9: 'B-person' + 10: 'I-person' + 11: 'B-product' + 12: 'I-product' ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @thomwolf, @lhoestq, @stefan-it, @lewtun, @jplu for adding this dataset.

e708d4545d7ab10dd2c6b5b5b2a72ca28685dae2

# Dataset Card for Wongnai_Reviews ## Dataset Description - **Repository:** https://github.com/wongnai/wongnai-corpus ### Dataset Summary The Wongnai Review dataset contains restaurant reviews and ratings, almost entirely in Thai language. The reviews are in 5 classes ranging from 1 to 5 stars. This dataset was featured in a Kaggle challenge https://www.kaggle.com/c/wongnai-challenge-review-rating-prediction/overview ### Languages Thai ## Dataset Structure ### Data Fields - review_body - text of review - star_rating - an integer star rating (1-5) or -1 (for test) ### Data Splits Designated train (40,000 reviews) and test (6,204) sets. ### Source Data #### Initial Data Collection and Normalization Data was collected by Wongnai from business reviews on their website, and shared on GitHub and Kaggle. ### Annotations The reviews are users' own star ratings, so no additional annotation was needed. ## Additional Information ### Dataset Curators Contributors to original GitHub repo: - Ekkalak Thongthanomkul - Tanapol Nearunchorn - Yuwat Chuesathuchon ### Licensing Information LGPL-3.0 ### Citation Information See https://github.com/wongnai/wongnai-corpus ### Contributions Thanks to [@mapmeld](https://github.com/mapmeld), [@cstorm125](https://github.com/cstorm125) for adding this dataset.

wongnai_reviews

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["th"], "license": ["lgpl-3.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "pretty_name": "WongnaiReviews", "dataset_info": {"features": [{"name": "review_body", "dtype": "string"}, {"name": "star_rating", "dtype": {"class_label": {"names": {"0": "1", "1": "2", "2": "3", "3": "4", "4": "5"}}}}], "splits": [{"name": "train", "num_bytes": 60691428, "num_examples": 40000}, {"name": "test", "num_bytes": 9913686, "num_examples": 6203}], "download_size": 16556587, "dataset_size": 70605114}}

2024-01-18T11:18:39+00:00

TAGS #task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Thai #license-lgpl-3.0 #region-us

# Dataset Card for Wongnai_Reviews ## Dataset Description - Repository: URL ### Dataset Summary The Wongnai Review dataset contains restaurant reviews and ratings, almost entirely in Thai language. The reviews are in 5 classes ranging from 1 to 5 stars. This dataset was featured in a Kaggle challenge URL ### Languages Thai ## Dataset Structure ### Data Fields - review_body - text of review - star_rating - an integer star rating (1-5) or -1 (for test) ### Data Splits Designated train (40,000 reviews) and test (6,204) sets. ### Source Data #### Initial Data Collection and Normalization Data was collected by Wongnai from business reviews on their website, and shared on GitHub and Kaggle. ### Annotations The reviews are users' own star ratings, so no additional annotation was needed. ## Additional Information ### Dataset Curators Contributors to original GitHub repo: - Ekkalak Thongthanomkul - Tanapol Nearunchorn - Yuwat Chuesathuchon ### Licensing Information LGPL-3.0 See URL ### Contributions Thanks to @mapmeld, @cstorm125 for adding this dataset.

2858acc155d677e3955af0dcb3ececfec992df4b

# Dataset Card for Wizard-of-Oz ## 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:** [More info needed] - **Repository:** [GitHub](https://github.com/nmrksic/neural-belief-tracker/tree/master/data/woz) - **Paper:** [A Network-based End-to-End Trainable Task-oriented Dialogue System](https://arxiv.org/abs/1604.04562) - **Leaderboard:** [More info needed] - **Point of Contact:** [More info 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 [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.

woz_dialogue

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["de", "en", "it"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask", "token-classification", "text-classification"], "task_ids": ["dialogue-modeling", "multi-class-classification", "parsing"], "paperswithcode_id": "wizard-of-oz", "pretty_name": "Wizard-of-Oz", "config_names": ["de", "de_en", "en", "it", "it_en"], "dataset_info": [{"config_name": "en", "features": [{"name": "dialogue_idx", "dtype": "int32"}, {"name": "dialogue", "list": [{"name": "turn_label", "sequence": {"sequence": "string"}}, {"name": "asr", "sequence": {"sequence": "string"}}, {"name": "system_transcript", "dtype": "string"}, {"name": "turn_idx", "dtype": "int32"}, {"name": "belief_state", "list": [{"name": "slots", "sequence": {"sequence": "string"}}, {"name": "act", "dtype": "string"}]}, {"name": "transcript", "dtype": "string"}, {"name": "system_acts", "sequence": {"sequence": "string"}}]}], "splits": [{"name": "train", "num_bytes": 827189, "num_examples": 600}, {"name": "validation", "num_bytes": 265684, "num_examples": 200}, {"name": "test", "num_bytes": 537557, "num_examples": 400}], "download_size": 7529221, "dataset_size": 1630430}, {"config_name": "de", "features": [{"name": "dialogue_idx", "dtype": "int32"}, {"name": "dialogue", "list": [{"name": "turn_label", "sequence": {"sequence": "string"}}, {"name": "asr", "sequence": {"sequence": "string"}}, {"name": "system_transcript", "dtype": "string"}, {"name": "turn_idx", "dtype": "int32"}, {"name": "belief_state", "list": [{"name": "slots", "sequence": {"sequence": "string"}}, {"name": "act", "dtype": "string"}]}, {"name": "transcript", "dtype": "string"}, {"name": "system_acts", "sequence": {"sequence": "string"}}]}], "splits": [{"name": "train", "num_bytes": 881478, "num_examples": 600}, {"name": "validation", "num_bytes": 276758, "num_examples": 200}, {"name": "test", "num_bytes": 569703, "num_examples": 400}], "download_size": 7626734, "dataset_size": 1727939}, {"config_name": "de_en", "features": [{"name": "dialogue_idx", "dtype": "int32"}, {"name": "dialogue", "list": [{"name": "turn_label", "sequence": {"sequence": "string"}}, {"name": "asr", "sequence": {"sequence": "string"}}, {"name": "system_transcript", "dtype": "string"}, {"name": "turn_idx", "dtype": "int32"}, {"name": "belief_state", "list": [{"name": "slots", "sequence": {"sequence": "string"}}, {"name": "act", "dtype": "string"}]}, {"name": "transcript", "dtype": "string"}, {"name": "system_acts", "sequence": {"sequence": "string"}}]}], "splits": [{"name": "train", "num_bytes": 860151, "num_examples": 600}, {"name": "validation", "num_bytes": 269966, "num_examples": 200}, {"name": "test", "num_bytes": 555841, "num_examples": 400}], "download_size": 7584753, "dataset_size": 1685958}, {"config_name": "it", "features": [{"name": "dialogue_idx", "dtype": "int32"}, {"name": "dialogue", "list": [{"name": "turn_label", "sequence": {"sequence": "string"}}, {"name": "asr", "sequence": {"sequence": "string"}}, {"name": "system_transcript", "dtype": "string"}, {"name": "turn_idx", "dtype": "int32"}, {"name": "belief_state", "list": [{"name": "slots", "sequence": {"sequence": "string"}}, {"name": "act", "dtype": "string"}]}, {"name": "transcript", "dtype": "string"}, {"name": "system_acts", "sequence": {"sequence": "string"}}]}], "splits": [{"name": "train", "num_bytes": 842799, "num_examples": 600}, {"name": "validation", "num_bytes": 270258, "num_examples": 200}, {"name": "test", "num_bytes": 547759, "num_examples": 400}], "download_size": 7559615, "dataset_size": 1660816}, {"config_name": "it_en", "features": [{"name": "dialogue_idx", "dtype": "int32"}, {"name": "dialogue", "list": [{"name": "turn_label", "sequence": {"sequence": "string"}}, {"name": "asr", "sequence": {"sequence": "string"}}, {"name": "system_transcript", "dtype": "string"}, {"name": "turn_idx", "dtype": "int32"}, {"name": "belief_state", "list": [{"name": "slots", "sequence": {"sequence": "string"}}, {"name": "act", "dtype": "string"}]}, {"name": "transcript", "dtype": "string"}, {"name": "system_acts", "sequence": {"sequence": "string"}}]}], "splits": [{"name": "train", "num_bytes": 845095, "num_examples": 600}, {"name": "validation", "num_bytes": 270942, "num_examples": 200}, {"name": "test", "num_bytes": 548979, "num_examples": 400}], "download_size": 7563815, "dataset_size": 1665016}]}

2024-01-18T11:18:40+00:00

TAGS #task_categories-text-generation #task_categories-fill-mask #task_categories-token-classification #task_categories-text-classification #task_ids-dialogue-modeling #task_ids-multi-class-classification #task_ids-parsing #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-German #language-English #language-Italian #license-unknown #arxiv-1604.04562 #region-us

# Dataset Card for Wizard-of-Oz ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: [More info needed] - Repository: GitHub - Paper: A Network-based End-to-End Trainable Task-oriented Dialogue System - Leaderboard: [More info needed] - Point of Contact: [More info needed] ### Dataset Summary ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @patil-suraj for adding this dataset.

9edde8605d398ce002ea19868423d4962580d7db

# Dataset Card for wrbsc ## 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://clarin-pl.eu/dspace/handle/11321/305 - **Repository:** https://clarin-pl.eu/dspace/handle/11321/305 - **Paper:** [Needs More Information] - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary WUT Relations Between Sentences Corpus contains 2827 pairs of related sentences. Relationships are derived from Cross-document Structure Theory (CST), which enables multi-document summarization through identification of cross-document rhetorical relationships within a cluster of related documents. Every relation was marked by at least 3 annotators. ### Supported Tasks and Leaderboards [Needs More Information] ### Languages Polish ## Dataset Structure ### Data Instances An example contains two related sentences and a class representing the type of relationship between those sentences. ``` {'relationship': 0, 'sentence1': 'Znajdujące się w Biurze Bezpieczeństwa Narodowego akta Komisji Weryfikacyjnej WSI zostały przewiezione do siedziby Służby Kontrwywiadu Wojskowego.', 'sentence2': '2008-07-03: Wywiezienie akt dotyczących WSI – sprawa dla prokuratury?'} ``` ### Data Fields - `sentence1`: the first sentence being compared (`string`) - `sentence2`: the second sentence being compared (`string`) - `relationship`: the type of relationship between those sentences. Can be one of 16 classes listed below: - `Krzyżowanie_się`: crossing - `Tło_historyczne`: historical background - `Źródło`: source - `Dalsze_informacje`: additional information - `Zawieranie`: inclusion - `Opis`: description - `Uszczegółowienie`: further detail - `Parafraza`: paraphrase - `Spełnienie`: fulfillment - `Mowa_zależna`: passive voice - `Zmiana_poglądu`: change of opinion - `Streszczenie`: summarization - `Tożsamość`: identity - `Sprzeczność`: conflict - `Modalność`: modality - `Cytowanie`: quotation ### Data Splits Single train split ## 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 Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0) ### Citation Information ``` @misc{11321/305, title = {{WUT} Relations Between Sentences Corpus}, author = {Oleksy, Marcin and Fikus, Dominika and Wolski, Micha{\l} and Podbielska, Ma{\l}gorzata and Turek, Agnieszka and Kędzia, Pawe{\l}}, url = {http://hdl.handle.net/11321/305}, note = {{CLARIN}-{PL} digital repository}, copyright = {Attribution-{ShareAlike} 3.0 Unported ({CC} {BY}-{SA} 3.0)}, year = {2016} } ``` ### Contributions Thanks to [@kldarek](https://github.com/kldarek) for adding this dataset.

wrbsc

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["pl"], "license": ["cc-by-sa-3.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["semantic-similarity-classification"], "pretty_name": "wrbsc", "dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "relationship", "dtype": {"class_label": {"names": {"0": "Krzy\u017cowanie_si\u0119", "1": "T\u0142o_historyczne", "2": "\u0179r\u00f3d\u0142o", "3": "Dalsze_informacje", "4": "Zawieranie", "5": "Opis", "6": "Uszczeg\u00f3\u0142owienie", "7": "Parafraza", "8": "Spe\u0142nienie", "9": "Mowa_zale\u017cna", "10": "Zmiana_pogl\u0105du", "11": "Streszczenie", "12": "To\u017csamo\u015b\u0107", "13": "Sprzeczno\u015b\u0107", "14": "Modalno\u015b\u0107", "15": "Cytowanie"}}}}], "splits": [{"name": "train", "num_bytes": 779881, "num_examples": 2827}], "download_size": 1273815, "dataset_size": 779881}}

2024-01-18T11:18:41+00:00

TAGS #task_categories-text-classification #task_ids-semantic-similarity-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Polish #license-cc-by-sa-3.0 #region-us

# Dataset Card for wrbsc ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: URL - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary WUT Relations Between Sentences Corpus contains 2827 pairs of related sentences. Relationships are derived from Cross-document Structure Theory (CST), which enables multi-document summarization through identification of cross-document rhetorical relationships within a cluster of related documents. Every relation was marked by at least 3 annotators. ### Supported Tasks and Leaderboards ### Languages Polish ## Dataset Structure ### Data Instances An example contains two related sentences and a class representing the type of relationship between those sentences. ### Data Fields - 'sentence1': the first sentence being compared ('string') - 'sentence2': the second sentence being compared ('string') - 'relationship': the type of relationship between those sentences. Can be one of 16 classes listed below: - 'Krzyżowanie_się': crossing - 'Tło_historyczne': historical background - 'Źródło': source - 'Dalsze_informacje': additional information - 'Zawieranie': inclusion - 'Opis': description - 'Uszczegółowienie': further detail - 'Parafraza': paraphrase - 'Spełnienie': fulfillment - 'Mowa_zależna': passive voice - 'Zmiana_poglądu': change of opinion - 'Streszczenie': summarization - 'Tożsamość': identity - 'Sprzeczność': conflict - 'Modalność': modality - 'Cytowanie': quotation ### Data Splits Single train split ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0) ### Contributions Thanks to @kldarek for adding this dataset.

810604b9ad3aafdc6144597fdaa40f21a6f5f3de

# Dataset Card for "x_stance" ## 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:** https://github.com/ZurichNLP/xstance - **Paper:** [X-Stance: A Multilingual Multi-Target Dataset for Stance Detection](https://arxiv.org/abs/2003.08385) - **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:** 6.41 MB - **Size of the generated dataset:** 25.73 MB - **Total amount of disk used:** 32.14 MB ### Dataset Summary The x-stance dataset contains more than 150 political questions, and 67k comments written by candidates on those questions. It can be used to train and evaluate stance detection systems. ### 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 comments are partly German, partly French and Italian. The questions are available in all the three languages plus English. ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 6.41 MB - **Size of the generated dataset:** 25.73 MB - **Total amount of disk used:** 32.14 MB An example of 'train' looks as follows. ``` { "author": "f27b54a137b4", "comment": "Das Arbeitsgesetz regelt die Arbeitszeiten und schützt den Arbeitnehmer. Es macht doch Sinn, dass wenn eine Nachfrage besteht, die Läden öffnen dürfen und wenn es keine Nachfrage gibt, diese geschlossen bleiben.", "id": 10045, "label": "FAVOR", "language": "de", "numerical_label": 100, "question": "Sind Sie für eine vollständige Liberalisierung der Geschäftsöffnungszeiten (Geschäfte können die Öffnungszeiten nach freiem Ermessen festlegen)?", "question_id": 739, "topic": "Economy" } ``` ### Data Fields The data fields are the same among all splits. #### default - `question`: a `string` feature. - `id`: a `int32` feature. - `question_id`: a `int32` feature. - `language`: a `string` feature. - `comment`: a `string` feature. - `label`: a `string` feature. - `numerical_label`: a `int32` feature. - `author`: a `string` feature. - `topic`: a `string` feature. ### Data Splits | name |train|validation|test | |-------|----:|---------:|----:| |default|45640| 3926|17705| ## 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 data have been extracted from the Swiss voting advice platform Smartvote.ch. #### 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 is licensed under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). ### Citation Information ``` @inproceedings{vamvas2020xstance, author = "Vamvas, Jannis and Sennrich, Rico", title = "{X-Stance}: A Multilingual Multi-Target Dataset for Stance Detection", booktitle = "Proceedings of the 5th Swiss Text Analytics Conference (SwissText) \& 16th Conference on Natural Language Processing (KONVENS)", address = "Zurich, Switzerland", year = "2020", month = "jun", url = "http://ceur-ws.org/Vol-2624/paper9.pdf" } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@jvamvas](https://github.com/jvamvas) for adding this dataset.

x_stance

2022-03-02T23:29:22+00:00

{"annotations_creators": ["machine-generated"], "language_creators": ["found"], "language": ["de", "en", "fr", "it"], "license": ["cc-by-nc-4.0"], "multilinguality": ["multilingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": [], "paperswithcode_id": "x-stance", "pretty_name": "x-stance", "tags": ["stance-detection"], "dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "id", "dtype": "int32"}, {"name": "question_id", "dtype": "int32"}, {"name": "language", "dtype": "string"}, {"name": "comment", "dtype": "string"}, {"name": "label", "dtype": "string"}, {"name": "numerical_label", "dtype": "int32"}, {"name": "author", "dtype": "string"}, {"name": "topic", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 17619123, "num_examples": 45640}, {"name": "test", "num_bytes": 6607134, "num_examples": 17705}, {"name": "validation", "num_bytes": 1505979, "num_examples": 3926}], "download_size": 6410801, "dataset_size": 25732236}}

2024-01-18T11:18:42+00:00

TAGS #task_categories-text-classification #annotations_creators-machine-generated #language_creators-found #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-original #language-German #language-English #language-French #language-Italian #license-cc-by-nc-4.0 #stance-detection #arxiv-2003.08385 #region-us

Dataset Card for "x\_stance" ============================ Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: * Repository: URL * Paper: X-Stance: A Multilingual Multi-Target Dataset for Stance Detection * Point of Contact: * Size of downloaded dataset files: 6.41 MB * Size of the generated dataset: 25.73 MB * Total amount of disk used: 32.14 MB ### Dataset Summary The x-stance dataset contains more than 150 political questions, and 67k comments written by candidates on those questions. It can be used to train and evaluate stance detection systems. ### Supported Tasks and Leaderboards ### Languages The comments are partly German, partly French and Italian. The questions are available in all the three languages plus English. Dataset Structure ----------------- ### Data Instances #### default * Size of downloaded dataset files: 6.41 MB * Size of the generated dataset: 25.73 MB * Total amount of disk used: 32.14 MB An example of 'train' looks as follows. ### Data Fields The data fields are the same among all splits. #### default * 'question': a 'string' feature. * 'id': a 'int32' feature. * 'question\_id': a 'int32' feature. * 'language': a 'string' feature. * 'comment': a 'string' feature. * 'label': a 'string' feature. * 'numerical\_label': a 'int32' feature. * 'author': a 'string' feature. * 'topic': a 'string' feature. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization The data have been extracted from the Swiss voting advice platform URL. #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information The dataset is licensed under CC BY-NC 4.0. ### Contributions Thanks to @lewtun, @mariamabarham, @thomwolf, @patrickvonplaten, @jvamvas for adding this dataset.

042f78955ba48e6404616762fa6e05e839c3907a

# 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.

xcopa

2022-03-02T23:29:22+00:00

{"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"], "size_categories": ["unknown"], "source_datasets": ["extended|copa"], "task_categories": ["question-answering"], "task_ids": ["multiple-choice-qa"], "paperswithcode_id": "xcopa", "pretty_name": "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": 11669, "num_examples": 100}, {"name": "test", "num_bytes": 56471, "num_examples": 500}], "download_size": 54200, "dataset_size": 68140}, {"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": 11957, "num_examples": 100}, {"name": "test", "num_bytes": 58437, "num_examples": 500}], "download_size": 50346, "dataset_size": 70394}, {"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": 13855, "num_examples": 100}, {"name": "test", "num_bytes": 63189, "num_examples": 500}], "download_size": 55608, "dataset_size": 77044}, {"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": 13324, "num_examples": 100}, {"name": "test", "num_bytes": 64909, "num_examples": 500}], "download_size": 59602, "dataset_size": 78233}, {"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": 13941, "num_examples": 100}, {"name": "test", "num_bytes": 68569, "num_examples": 500}], "download_size": 56734, "dataset_size": 82510}, {"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": 12666, "num_examples": 100}, {"name": "test", "num_bytes": 60533, "num_examples": 500}], "download_size": 53862, "dataset_size": 73199}, {"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": 36995, "num_examples": 100}, {"name": "test", "num_bytes": 176112, "num_examples": 500}], "download_size": 91348, "dataset_size": 213107}, {"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": 21817, "num_examples": 100}, {"name": "test", "num_bytes": 104023, "num_examples": 500}], "download_size": 65925, "dataset_size": 125840}, {"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": 11899, "num_examples": 100}, {"name": "test", "num_bytes": 57599, "num_examples": 500}], "download_size": 53677, "dataset_size": 69498}, {"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": 11881, "num_examples": 100}, {"name": "test", "num_bytes": 57327, "num_examples": 500}], "download_size": 52078, "dataset_size": 69208}, {"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": 12130, "num_examples": 100}, {"name": "test", "num_bytes": 58019, "num_examples": 500}], "download_size": 52823, "dataset_size": 70149}, {"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": 12457, "num_examples": 100}, {"name": "test", "num_bytes": 58406, "num_examples": 500}], "download_size": 53701, "dataset_size": 70863}, {"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": 12382, "num_examples": 100}, {"name": "test", "num_bytes": 58936, "num_examples": 500}], "download_size": 53410, "dataset_size": 71318}, {"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": 12180, "num_examples": 100}, {"name": "test", "num_bytes": 58607, "num_examples": 500}], "download_size": 52888, "dataset_size": 70787}, {"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": 12372, "num_examples": 100}, {"name": "test", "num_bytes": 59442, "num_examples": 500}], "download_size": 54488, "dataset_size": 71814}, {"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": 11347, "num_examples": 100}, {"name": "test", "num_bytes": 54758, "num_examples": 500}], "download_size": 52243, "dataset_size": 66105}, {"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": 11879, "num_examples": 100}, {"name": "test", "num_bytes": 57599, "num_examples": 500}], "download_size": 52223, "dataset_size": 69478}, {"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": 11604, "num_examples": 100}, {"name": "test", "num_bytes": 55797, "num_examples": 500}], "download_size": 52087, "dataset_size": 67401}, {"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": 12001, "num_examples": 100}, {"name": "test", "num_bytes": 57895, "num_examples": 500}], "download_size": 52896, "dataset_size": 69896}, {"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": 15093, "num_examples": 100}, {"name": "test", "num_bytes": 70169, "num_examples": 500}], "download_size": 59132, "dataset_size": 85262}, {"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": 11604, "num_examples": 100}, {"name": "test", "num_bytes": 55134, "num_examples": 500}], "download_size": 52634, "dataset_size": 66738}], "configs": [{"config_name": "et", "data_files": [{"split": "validation", "path": "et/validation-*"}, {"split": "test", "path": "et/test-*"}]}, {"config_name": "ht", "data_files": [{"split": "validation", "path": "ht/validation-*"}, {"split": "test", "path": "ht/test-*"}]}, {"config_name": "id", "data_files": [{"split": "validation", "path": "id/validation-*"}, {"split": "test", "path": "id/test-*"}]}, {"config_name": "it", "data_files": [{"split": "validation", "path": "it/validation-*"}, {"split": "test", "path": "it/test-*"}]}, {"config_name": "qu", "data_files": [{"split": "validation", "path": "qu/validation-*"}, {"split": "test", "path": "qu/test-*"}]}, {"config_name": "sw", "data_files": [{"split": "validation", "path": "sw/validation-*"}, {"split": "test", "path": "sw/test-*"}]}, {"config_name": "ta", "data_files": [{"split": "validation", "path": "ta/validation-*"}, {"split": "test", "path": "ta/test-*"}]}, {"config_name": "th", "data_files": [{"split": "validation", "path": "th/validation-*"}, {"split": "test", "path": "th/test-*"}]}, {"config_name": "tr", "data_files": [{"split": "validation", "path": "tr/validation-*"}, {"split": "test", "path": "tr/test-*"}]}, {"config_name": "translation-et", "data_files": [{"split": "validation", "path": "translation-et/validation-*"}, {"split": "test", "path": "translation-et/test-*"}]}, {"config_name": "translation-ht", "data_files": [{"split": "validation", "path": "translation-ht/validation-*"}, {"split": "test", "path": "translation-ht/test-*"}]}, {"config_name": "translation-id", "data_files": [{"split": "validation", "path": "translation-id/validation-*"}, {"split": "test", "path": "translation-id/test-*"}]}, {"config_name": "translation-it", "data_files": [{"split": "validation", "path": "translation-it/validation-*"}, {"split": "test", "path": "translation-it/test-*"}]}, {"config_name": "translation-sw", "data_files": [{"split": "validation", "path": "translation-sw/validation-*"}, {"split": "test", "path": "translation-sw/test-*"}]}, {"config_name": "translation-ta", "data_files": [{"split": "validation", "path": "translation-ta/validation-*"}, {"split": "test", "path": "translation-ta/test-*"}]}, {"config_name": "translation-th", "data_files": [{"split": "validation", "path": "translation-th/validation-*"}, {"split": "test", "path": "translation-th/test-*"}]}, {"config_name": "translation-tr", "data_files": [{"split": "validation", "path": "translation-tr/validation-*"}, {"split": "test", "path": "translation-tr/test-*"}]}, {"config_name": "translation-vi", "data_files": [{"split": "validation", "path": "translation-vi/validation-*"}, {"split": "test", "path": "translation-vi/test-*"}]}, {"config_name": "translation-zh", "data_files": [{"split": "validation", "path": "translation-zh/validation-*"}, {"split": "test", "path": "translation-zh/test-*"}]}, {"config_name": "vi", "data_files": [{"split": "validation", "path": "vi/validation-*"}, {"split": "test", "path": "vi/test-*"}]}, {"config_name": "zh", "data_files": [{"split": "validation", "path": "zh/validation-*"}, {"split": "test", "path": "zh/test-*"}]}]}

2024-01-04T16:55:46+00:00

TAGS #task_categories-question-answering #task_ids-multiple-choice-qa #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-multilingual #size_categories-unknown #source_datasets-extended|copa #language-Estonian #language-Haitian #language-Indonesian #language-Italian #language-Quechua #language-Swahili (macrolanguage) #language-Tamil #language-Thai #language-Turkish #language-Vietnamese #language-Chinese #license-cc-by-4.0 #region-us

Dataset Card for "xcopa" ======================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: * Point of Contact: * 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 ### 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. #### 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. #### 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. #### 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. #### 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. ### 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 Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information Creative Commons Attribution 4.0 International (CC BY 4.0). ### Contributions Thanks to @patrickvonplaten, @lewtun, @thomwolf for adding this dataset.

f691f311a11f84773a899e9bfc7da2bae51b0b02

# Dataset Card for X-CSR ## 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-instances) - [Data Splits](#data-instances) - [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) ## Dataset Description - **Homepage:** https://inklab.usc.edu//XCSR/ - **Repository:** https://github.com/INK-USC/XCSR - **Paper:** https://arxiv.org/abs/2106.06937 - **Leaderboard:** https://inklab.usc.edu//XCSR/leaderboard - **Point of Contact:** https://yuchenlin.xyz/ ### Dataset Summary To evaluate multi-lingual language models (ML-LMs) for commonsense reasoning in a cross-lingual zero-shot transfer setting (X-CSR), i.e., training in English and test in other languages, we create two benchmark datasets, namely X-CSQA and X-CODAH. Specifically, we automatically translate the original CSQA and CODAH datasets, which only have English versions, to 15 other languages, forming development and test sets for studying X-CSR. As our goal is to evaluate different ML-LMs in a unified evaluation protocol for X-CSR, we argue that such translated examples, although might contain noise, can serve as a starting benchmark for us to obtain meaningful analysis, before more human-translated datasets will be available in the future. ### Supported Tasks and Leaderboards https://inklab.usc.edu//XCSR/leaderboard ### Languages The total 16 languages for X-CSR: {en, zh, de, es, fr, it, jap, nl, pl, pt, ru, ar, vi, hi, sw, ur}. ## Dataset Structure ### Data Instances An example of the X-CSQA dataset: ``` { "id": "be1920f7ba5454ad", # an id shared by all languages "lang": "en", # one of the 16 language codes. "question": { "stem": "What will happen to your knowledge with more learning?", # question text "choices": [ {"label": "A", "text": "headaches" }, {"label": "B", "text": "bigger brain" }, {"label": "C", "text": "education" }, {"label": "D", "text": "growth" }, {"label": "E", "text": "knowing more" } ] }, "answerKey": "D" # hidden for test data. } ``` An example of the X-CODAH dataset: ``` { "id": "b8eeef4a823fcd4b", # an id shared by all languages "lang": "en", # one of the 16 language codes. "question_tag": "o", # one of 6 question types "question": { "stem": " ", # always a blank as a dummy question "choices": [ {"label": "A", "text": "Jennifer loves her school very much, she plans to drop every courses."}, {"label": "B", "text": "Jennifer loves her school very much, she is never absent even when she's sick."}, {"label": "C", "text": "Jennifer loves her school very much, she wants to get a part-time job."}, {"label": "D", "text": "Jennifer loves her school very much, she quits school happily."} ] }, "answerKey": "B" # hidden for test data. } ``` ### Data Fields - id: an id shared by all languages - lang: one of the 16 language codes. - question_tag: one of 6 question types - stem: always a blank as a dummy question - choices: a list of answers, each answer has: - label: a string answer identifier for each answer - text: the answer text ### Data Splits - X-CSQA: There are 8,888 examples for training in English, 1,000 for development in each language, and 1,074 examples for testing in each language. - X-CODAH: There are 8,476 examples for training in English, 300 for development in each language, and 1,000 examples for testing in each language. ## Dataset Creation ### Curation Rationale To evaluate multi-lingual language models (ML-LMs) for commonsense reasoning in a cross-lingual zero-shot transfer setting (X-CSR), i.e., training in English and test in other languages, we create two benchmark datasets, namely X-CSQA and X-CODAH. The details of the dataset construction, especially the translation procedures, can be found in section A of the appendix of the [paper](https://inklab.usc.edu//XCSR/XCSR_paper.pdf). ### 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 [Needs More Information] ### Citation Information ``` # X-CSR @inproceedings{lin-etal-2021-common, title = "Common Sense Beyond {E}nglish: Evaluating and Improving Multilingual Language Models for Commonsense Reasoning", author = "Lin, Bill Yuchen and Lee, Seyeon and Qiao, Xiaoyang and Ren, Xiang", booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.acl-long.102", doi = "10.18653/v1/2021.acl-long.102", pages = "1274--1287", abstract = "Commonsense reasoning research has so far been limited to English. We aim to evaluate and improve popular multilingual language models (ML-LMs) to help advance commonsense reasoning (CSR) beyond English. We collect the Mickey corpus, consisting of 561k sentences in 11 different languages, which can be used for analyzing and improving ML-LMs. We propose Mickey Probe, a language-general probing task for fairly evaluating the common sense of popular ML-LMs across different languages. In addition, we also create two new datasets, X-CSQA and X-CODAH, by translating their English versions to 14 other languages, so that we can evaluate popular ML-LMs for cross-lingual commonsense reasoning. To improve the performance beyond English, we propose a simple yet effective method {---} multilingual contrastive pretraining (MCP). It significantly enhances sentence representations, yielding a large performance gain on both benchmarks (e.g., +2.7{\%} accuracy for X-CSQA over XLM-R{\_}L).", } # CSQA @inproceedings{Talmor2019commonsenseqaaq, address = {Minneapolis, Minnesota}, author = {Talmor, Alon and Herzig, Jonathan and Lourie, Nicholas and Berant, Jonathan}, booktitle = {Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)}, doi = {10.18653/v1/N19-1421}, pages = {4149--4158}, publisher = {Association for Computational Linguistics}, title = {CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge}, url = {https://www.aclweb.org/anthology/N19-1421}, year = {2019} } # CODAH @inproceedings{Chen2019CODAHAA, address = {Minneapolis, USA}, author = {Chen, Michael and D{'}Arcy, Mike and Liu, Alisa and Fernandez, Jared and Downey, Doug}, booktitle = {Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for {NLP}}, doi = {10.18653/v1/W19-2008}, pages = {63--69}, publisher = {Association for Computational Linguistics}, title = {CODAH: An Adversarially-Authored Question Answering Dataset for Common Sense}, url = {https://www.aclweb.org/anthology/W19-2008}, year = {2019} } ``` ### Contributions Thanks to [Bill Yuchen Lin](https://yuchenlin.xyz/), [Seyeon Lee](https://seyeon-lee.github.io/), [Xiaoyang Qiao](https://www.linkedin.com/in/xiaoyang-qiao/), [Xiang Ren](http://www-bcf.usc.edu/~xiangren/) for adding this dataset.

xcsr

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced", "machine-generated"], "language": ["ar", "de", "en", "es", "fr", "hi", "it", "ja", "nl", "pl", "pt", "ru", "sw", "ur", "vi", "zh"], "license": ["mit"], "multilinguality": ["multilingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["extended|codah", "extended|commonsense_qa"], "task_categories": ["question-answering"], "task_ids": ["multiple-choice-qa"], "pretty_name": "X-CSR", "dataset_info": [{"config_name": "X-CODAH-ar", "features": [{"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "question_tag", "dtype": "string"}, {"name": "question", "struct": [{"name": "stem", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "label", "dtype": "string"}, {"name": "text", "dtype": "string"}]}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 568026, "num_examples": 1000}, {"name": "validation", "num_bytes": 165022, "num_examples": 300}], "download_size": 265474, "dataset_size": 733048}, {"config_name": "X-CODAH-de", "features": [{"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "question_tag", "dtype": "string"}, {"name": "question", "struct": [{"name": "stem", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "label", "dtype": "string"}, {"name": "text", "dtype": "string"}]}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 476087, "num_examples": 1000}, {"name": "validation", "num_bytes": 138764, "num_examples": 300}], "download_size": 259705, "dataset_size": 614851}, {"config_name": "X-CODAH-en", "features": [{"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "question_tag", "dtype": "string"}, {"name": "question", "struct": [{"name": "stem", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "label", "dtype": "string"}, {"name": "text", "dtype": "string"}]}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 417000, "num_examples": 1000}, {"name": "validation", "num_bytes": 121811, "num_examples": 300}], "download_size": 217262, "dataset_size": 538811}, {"config_name": "X-CODAH-es", "features": [{"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "question_tag", "dtype": "string"}, {"name": "question", "struct": [{"name": "stem", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "label", "dtype": "string"}, {"name": "text", "dtype": "string"}]}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 450954, "num_examples": 1000}, {"name": "validation", "num_bytes": 130678, "num_examples": 300}], "download_size": 242647, "dataset_size": 581632}, {"config_name": "X-CODAH-fr", "features": [{"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "question_tag", "dtype": "string"}, {"name": "question", "struct": [{"name": "stem", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "label", "dtype": "string"}, {"name": "text", "dtype": "string"}]}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 477525, "num_examples": 1000}, {"name": "validation", "num_bytes": 137889, "num_examples": 300}], "download_size": 244998, "dataset_size": 615414}, {"config_name": "X-CODAH-hi", "features": [{"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "question_tag", "dtype": "string"}, {"name": "question", "struct": [{"name": "stem", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "label", "dtype": "string"}, {"name": "text", "dtype": "string"}]}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 973733, "num_examples": 1000}, {"name": "validation", "num_bytes": 283004, "num_examples": 300}], "download_size": 336862, "dataset_size": 1256737}, {"config_name": "X-CODAH-it", "features": [{"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "question_tag", "dtype": "string"}, {"name": "question", "struct": [{"name": "stem", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "label", "dtype": "string"}, {"name": "text", "dtype": "string"}]}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 457055, "num_examples": 1000}, {"name": "validation", "num_bytes": 133504, "num_examples": 300}], "download_size": 241780, "dataset_size": 590559}, {"config_name": "X-CODAH-jap", "features": [{"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "question_tag", "dtype": "string"}, {"name": "question", "struct": [{"name": "stem", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "label", "dtype": "string"}, {"name": "text", "dtype": "string"}]}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 538415, "num_examples": 1000}, {"name": "validation", "num_bytes": 157392, "num_examples": 300}], "download_size": 264995, "dataset_size": 695807}, {"config_name": "X-CODAH-nl", "features": [{"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "question_tag", "dtype": "string"}, {"name": "question", "struct": [{"name": "stem", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "label", "dtype": "string"}, {"name": "text", "dtype": "string"}]}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 448728, "num_examples": 1000}, {"name": "validation", "num_bytes": 130018, "num_examples": 300}], "download_size": 237855, "dataset_size": 578746}, {"config_name": "X-CODAH-pl", "features": [{"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "question_tag", "dtype": "string"}, {"name": "question", "struct": [{"name": "stem", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "label", "dtype": "string"}, {"name": "text", "dtype": "string"}]}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 438538, "num_examples": 1000}, {"name": "validation", "num_bytes": 127750, "num_examples": 300}], "download_size": 254894, "dataset_size": 566288}, {"config_name": "X-CODAH-pt", "features": [{"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "question_tag", "dtype": "string"}, {"name": "question", "struct": [{"name": "stem", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "label", "dtype": "string"}, {"name": "text", "dtype": "string"}]}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 455583, "num_examples": 1000}, {"name": "validation", "num_bytes": 131933, "num_examples": 300}], "download_size": 238858, "dataset_size": 587516}, {"config_name": "X-CODAH-ru", "features": [{"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "question_tag", "dtype": "string"}, {"name": "question", "struct": [{"name": "stem", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "label", "dtype": "string"}, {"name": "text", "dtype": "string"}]}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 674567, "num_examples": 1000}, {"name": "validation", "num_bytes": 193713, "num_examples": 300}], "download_size": 314200, "dataset_size": 868280}, {"config_name": "X-CODAH-sw", "features": [{"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "question_tag", "dtype": "string"}, {"name": "question", "struct": [{"name": "stem", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "label", "dtype": "string"}, {"name": "text", "dtype": "string"}]}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 423421, "num_examples": 1000}, {"name": "validation", "num_bytes": 124770, "num_examples": 300}], "download_size": 214100, "dataset_size": 548191}, {"config_name": "X-CODAH-ur", "features": [{"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "question_tag", "dtype": "string"}, {"name": "question", "struct": [{"name": "stem", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "label", "dtype": "string"}, {"name": "text", "dtype": "string"}]}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 687123, "num_examples": 1000}, {"name": "validation", "num_bytes": 199737, "num_examples": 300}], "download_size": 294475, "dataset_size": 886860}, {"config_name": "X-CODAH-vi", "features": [{"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "question_tag", "dtype": "string"}, {"name": "question", "struct": [{"name": "stem", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "label", "dtype": "string"}, {"name": "text", "dtype": "string"}]}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 543089, "num_examples": 1000}, {"name": "validation", "num_bytes": 156888, "num_examples": 300}], "download_size": 251390, "dataset_size": 699977}, {"config_name": "X-CODAH-zh", "features": [{"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "question_tag", "dtype": "string"}, {"name": "question", "struct": [{"name": "stem", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "label", "dtype": "string"}, {"name": "text", "dtype": "string"}]}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 394660, "num_examples": 1000}, {"name": "validation", "num_bytes": 115025, "num_examples": 300}], "download_size": 237827, "dataset_size": 509685}, {"config_name": "X-CSQA-ar", "features": [{"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "question", "struct": [{"name": "stem", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "label", "dtype": "string"}, {"name": "text", "dtype": "string"}]}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 288645, "num_examples": 1074}, {"name": "validation", "num_bytes": 273580, "num_examples": 1000}], "download_size": 255626, "dataset_size": 562225}, {"config_name": "X-CSQA-de", "features": [{"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "question", "struct": [{"name": "stem", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "label", "dtype": "string"}, {"name": "text", "dtype": "string"}]}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 234170, "num_examples": 1074}, {"name": "validation", "num_bytes": 222840, "num_examples": 1000}], "download_size": 242762, "dataset_size": 457010}, {"config_name": "X-CSQA-en", "features": [{"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "question", "struct": [{"name": "stem", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "label", "dtype": "string"}, {"name": "text", "dtype": "string"}]}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 215617, "num_examples": 1074}, {"name": "validation", "num_bytes": 205079, "num_examples": 1000}], "download_size": 222677, "dataset_size": 420696}, {"config_name": "X-CSQA-es", "features": [{"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "question", "struct": [{"name": "stem", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "label", "dtype": "string"}, {"name": "text", "dtype": "string"}]}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 236817, "num_examples": 1074}, {"name": "validation", "num_bytes": 224497, "num_examples": 1000}], "download_size": 238810, "dataset_size": 461314}, {"config_name": "X-CSQA-fr", "features": [{"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "question", "struct": [{"name": "stem", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "label", "dtype": "string"}, {"name": "text", "dtype": "string"}]}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 243952, "num_examples": 1074}, {"name": "validation", "num_bytes": 231396, "num_examples": 1000}], "download_size": 244676, "dataset_size": 475348}, {"config_name": "X-CSQA-hi", "features": [{"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "question", "struct": [{"name": "stem", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "label", "dtype": "string"}, {"name": "text", "dtype": "string"}]}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 415011, "num_examples": 1074}, {"name": "validation", "num_bytes": 396318, "num_examples": 1000}], "download_size": 304090, "dataset_size": 811329}, {"config_name": "X-CSQA-it", "features": [{"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "question", "struct": [{"name": "stem", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "label", "dtype": "string"}, {"name": "text", "dtype": "string"}]}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 232604, "num_examples": 1074}, {"name": "validation", "num_bytes": 220902, "num_examples": 1000}], "download_size": 236130, "dataset_size": 453506}, {"config_name": "X-CSQA-jap", "features": [{"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "question", "struct": [{"name": "stem", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "label", "dtype": "string"}, {"name": "text", "dtype": "string"}]}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 250846, "num_examples": 1074}, {"name": "validation", "num_bytes": 240404, "num_examples": 1000}], "download_size": 249420, "dataset_size": 491250}, {"config_name": "X-CSQA-nl", "features": [{"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "question", "struct": [{"name": "stem", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "label", "dtype": "string"}, {"name": "text", "dtype": "string"}]}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 226949, "num_examples": 1074}, {"name": "validation", "num_bytes": 216194, "num_examples": 1000}], "download_size": 231078, "dataset_size": 443143}, {"config_name": "X-CSQA-pl", "features": [{"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "question", "struct": [{"name": "stem", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "label", "dtype": "string"}, {"name": "text", "dtype": "string"}]}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 231479, "num_examples": 1074}, {"name": "validation", "num_bytes": 219814, "num_examples": 1000}], "download_size": 245829, "dataset_size": 451293}, {"config_name": "X-CSQA-pt", "features": [{"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "question", "struct": [{"name": "stem", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "label", "dtype": "string"}, {"name": "text", "dtype": "string"}]}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 235469, "num_examples": 1074}, {"name": "validation", "num_bytes": 222785, "num_examples": 1000}], "download_size": 238902, "dataset_size": 458254}, {"config_name": "X-CSQA-ru", "features": [{"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "question", "struct": [{"name": "stem", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "label", "dtype": "string"}, {"name": "text", "dtype": "string"}]}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 341749, "num_examples": 1074}, {"name": "validation", "num_bytes": 323724, "num_examples": 1000}], "download_size": 296252, "dataset_size": 665473}, {"config_name": "X-CSQA-sw", "features": [{"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "question", "struct": [{"name": "stem", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "label", "dtype": "string"}, {"name": "text", "dtype": "string"}]}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 222215, "num_examples": 1074}, {"name": "validation", "num_bytes": 211426, "num_examples": 1000}], "download_size": 214954, "dataset_size": 433641}, {"config_name": "X-CSQA-ur", "features": [{"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "question", "struct": [{"name": "stem", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "label", "dtype": "string"}, {"name": "text", "dtype": "string"}]}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 306129, "num_examples": 1074}, {"name": "validation", "num_bytes": 292001, "num_examples": 1000}], "download_size": 267789, "dataset_size": 598130}, {"config_name": "X-CSQA-vi", "features": [{"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "question", "struct": [{"name": "stem", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "label", "dtype": "string"}, {"name": "text", "dtype": "string"}]}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 265210, "num_examples": 1074}, {"name": "validation", "num_bytes": 253502, "num_examples": 1000}], "download_size": 244641, "dataset_size": 518712}, {"config_name": "X-CSQA-zh", "features": [{"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "question", "struct": [{"name": "stem", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "label", "dtype": "string"}, {"name": "text", "dtype": "string"}]}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 197444, "num_examples": 1074}, {"name": "validation", "num_bytes": 188273, "num_examples": 1000}], "download_size": 207379, "dataset_size": 385717}], "configs": [{"config_name": "X-CODAH-ar", "data_files": [{"split": "test", "path": "X-CODAH-ar/test-*"}, {"split": "validation", "path": "X-CODAH-ar/validation-*"}]}, {"config_name": "X-CODAH-de", "data_files": [{"split": "test", "path": "X-CODAH-de/test-*"}, {"split": "validation", "path": "X-CODAH-de/validation-*"}]}, {"config_name": "X-CODAH-en", "data_files": [{"split": "test", "path": "X-CODAH-en/test-*"}, {"split": "validation", "path": "X-CODAH-en/validation-*"}]}, {"config_name": "X-CODAH-es", "data_files": [{"split": "test", "path": "X-CODAH-es/test-*"}, {"split": "validation", "path": "X-CODAH-es/validation-*"}]}, {"config_name": "X-CODAH-fr", "data_files": [{"split": "test", "path": "X-CODAH-fr/test-*"}, {"split": "validation", "path": "X-CODAH-fr/validation-*"}]}, {"config_name": "X-CODAH-hi", "data_files": [{"split": "test", "path": "X-CODAH-hi/test-*"}, {"split": "validation", "path": "X-CODAH-hi/validation-*"}]}, {"config_name": "X-CODAH-it", "data_files": [{"split": "test", "path": "X-CODAH-it/test-*"}, {"split": "validation", "path": "X-CODAH-it/validation-*"}]}, {"config_name": "X-CODAH-jap", "data_files": [{"split": "test", "path": "X-CODAH-jap/test-*"}, {"split": "validation", "path": "X-CODAH-jap/validation-*"}]}, {"config_name": "X-CODAH-nl", "data_files": [{"split": "test", "path": "X-CODAH-nl/test-*"}, {"split": "validation", "path": "X-CODAH-nl/validation-*"}]}, {"config_name": "X-CODAH-pl", "data_files": [{"split": "test", "path": "X-CODAH-pl/test-*"}, {"split": "validation", "path": "X-CODAH-pl/validation-*"}]}, {"config_name": "X-CODAH-pt", "data_files": [{"split": "test", "path": "X-CODAH-pt/test-*"}, {"split": "validation", "path": "X-CODAH-pt/validation-*"}]}, {"config_name": "X-CODAH-ru", "data_files": [{"split": "test", "path": "X-CODAH-ru/test-*"}, {"split": "validation", "path": "X-CODAH-ru/validation-*"}]}, {"config_name": "X-CODAH-sw", "data_files": [{"split": "test", "path": "X-CODAH-sw/test-*"}, {"split": "validation", "path": "X-CODAH-sw/validation-*"}]}, {"config_name": "X-CODAH-ur", "data_files": [{"split": "test", "path": "X-CODAH-ur/test-*"}, {"split": "validation", "path": "X-CODAH-ur/validation-*"}]}, {"config_name": "X-CODAH-vi", "data_files": [{"split": "test", "path": "X-CODAH-vi/test-*"}, {"split": "validation", "path": "X-CODAH-vi/validation-*"}]}, {"config_name": "X-CODAH-zh", "data_files": [{"split": "test", "path": "X-CODAH-zh/test-*"}, {"split": "validation", "path": "X-CODAH-zh/validation-*"}]}, {"config_name": "X-CSQA-ar", "data_files": [{"split": "test", "path": "X-CSQA-ar/test-*"}, {"split": "validation", "path": "X-CSQA-ar/validation-*"}]}, {"config_name": "X-CSQA-de", "data_files": [{"split": "test", "path": "X-CSQA-de/test-*"}, {"split": "validation", "path": "X-CSQA-de/validation-*"}]}, {"config_name": "X-CSQA-en", "data_files": [{"split": "test", "path": "X-CSQA-en/test-*"}, {"split": "validation", "path": "X-CSQA-en/validation-*"}]}, {"config_name": "X-CSQA-es", "data_files": [{"split": "test", "path": "X-CSQA-es/test-*"}, {"split": "validation", "path": "X-CSQA-es/validation-*"}]}, {"config_name": "X-CSQA-fr", "data_files": [{"split": "test", "path": "X-CSQA-fr/test-*"}, {"split": "validation", "path": "X-CSQA-fr/validation-*"}]}, {"config_name": "X-CSQA-hi", "data_files": [{"split": "test", "path": "X-CSQA-hi/test-*"}, {"split": "validation", "path": "X-CSQA-hi/validation-*"}]}, {"config_name": "X-CSQA-it", "data_files": [{"split": "test", "path": "X-CSQA-it/test-*"}, {"split": "validation", "path": "X-CSQA-it/validation-*"}]}, {"config_name": "X-CSQA-jap", "data_files": [{"split": "test", "path": "X-CSQA-jap/test-*"}, {"split": "validation", "path": "X-CSQA-jap/validation-*"}]}, {"config_name": "X-CSQA-nl", "data_files": [{"split": "test", "path": "X-CSQA-nl/test-*"}, {"split": "validation", "path": "X-CSQA-nl/validation-*"}]}, {"config_name": "X-CSQA-pl", "data_files": [{"split": "test", "path": "X-CSQA-pl/test-*"}, {"split": "validation", "path": "X-CSQA-pl/validation-*"}]}, {"config_name": "X-CSQA-pt", "data_files": [{"split": "test", "path": "X-CSQA-pt/test-*"}, {"split": "validation", "path": "X-CSQA-pt/validation-*"}]}, {"config_name": "X-CSQA-ru", "data_files": [{"split": "test", "path": "X-CSQA-ru/test-*"}, {"split": "validation", "path": "X-CSQA-ru/validation-*"}]}, {"config_name": "X-CSQA-sw", "data_files": [{"split": "test", "path": "X-CSQA-sw/test-*"}, {"split": "validation", "path": "X-CSQA-sw/validation-*"}]}, {"config_name": "X-CSQA-ur", "data_files": [{"split": "test", "path": "X-CSQA-ur/test-*"}, {"split": "validation", "path": "X-CSQA-ur/validation-*"}]}, {"config_name": "X-CSQA-vi", "data_files": [{"split": "test", "path": "X-CSQA-vi/test-*"}, {"split": "validation", "path": "X-CSQA-vi/validation-*"}]}, {"config_name": "X-CSQA-zh", "data_files": [{"split": "test", "path": "X-CSQA-zh/test-*"}, {"split": "validation", "path": "X-CSQA-zh/validation-*"}]}]}

2024-01-04T17:03:17+00:00

TAGS #task_categories-question-answering #task_ids-multiple-choice-qa #annotations_creators-crowdsourced #language_creators-crowdsourced #language_creators-machine-generated #multilinguality-multilingual #size_categories-1K<n<10K #source_datasets-extended|codah #source_datasets-extended|commonsense_qa #language-Arabic #language-German #language-English #language-Spanish #language-French #language-Hindi #language-Italian #language-Japanese #language-Dutch #language-Polish #language-Portuguese #language-Russian #language-Swahili (macrolanguage) #language-Urdu #language-Vietnamese #language-Chinese #license-mit #arxiv-2106.06937 #region-us

# Dataset Card for X-CSR ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information ## Dataset Description - Homepage: URL/XCSR/ - Repository: URL - Paper: URL - Leaderboard: URL/XCSR/leaderboard - Point of Contact: URL ### Dataset Summary To evaluate multi-lingual language models (ML-LMs) for commonsense reasoning in a cross-lingual zero-shot transfer setting (X-CSR), i.e., training in English and test in other languages, we create two benchmark datasets, namely X-CSQA and X-CODAH. Specifically, we automatically translate the original CSQA and CODAH datasets, which only have English versions, to 15 other languages, forming development and test sets for studying X-CSR. As our goal is to evaluate different ML-LMs in a unified evaluation protocol for X-CSR, we argue that such translated examples, although might contain noise, can serve as a starting benchmark for us to obtain meaningful analysis, before more human-translated datasets will be available in the future. ### Supported Tasks and Leaderboards URL/XCSR/leaderboard ### Languages The total 16 languages for X-CSR: {en, zh, de, es, fr, it, jap, nl, pl, pt, ru, ar, vi, hi, sw, ur}. ## Dataset Structure ### Data Instances An example of the X-CSQA dataset: An example of the X-CODAH dataset: ### Data Fields - id: an id shared by all languages - lang: one of the 16 language codes. - question_tag: one of 6 question types - stem: always a blank as a dummy question - choices: a list of answers, each answer has: - label: a string answer identifier for each answer - text: the answer text ### Data Splits - X-CSQA: There are 8,888 examples for training in English, 1,000 for development in each language, and 1,074 examples for testing in each language. - X-CODAH: There are 8,476 examples for training in English, 300 for development in each language, and 1,000 examples for testing in each language. ## Dataset Creation ### Curation Rationale To evaluate multi-lingual language models (ML-LMs) for commonsense reasoning in a cross-lingual zero-shot transfer setting (X-CSR), i.e., training in English and test in other languages, we create two benchmark datasets, namely X-CSQA and X-CODAH. The details of the dataset construction, especially the translation procedures, can be found in section A of the appendix of the paper. ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to Bill Yuchen Lin, Seyeon Lee, Xiaoyang Qiao, Xiang Ren for adding this dataset.

8003c4161c1f4072c3a4acb55f6e2690c7e08553

# Dataset Card for xed_english_finnish ## 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/Helsinki-NLP/XED) - **Paper:** [Arxiv](https://arxiv.org/abs/2011.01612) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This is the XED dataset. The dataset consists of emotion annotated movie subtitles from OPUS. We use Plutchik's 8 core emotions to annotate. The data is multilabel. The original annotations have been sourced for mainly English and Finnish. For the English data we used Stanford NER (named entity recognition) (Finkel et al., 2005) to replace names and locations with the tags: [PERSON] and [LOCATION] respectively. For the Finnish data, we replaced names and locations using the Turku NER corpus (Luoma et al., 2020). ### Supported Tasks and Leaderboards Sentiment Classification, Multilabel Classification, Multilabel Classification, Intent Classification ### Languages English, Finnish ## Dataset Structure ### Data Instances ``` { "sentence": "A confession that you hired [PERSON] ... and are responsible for my father's murder." "labels": [1, 6] # anger, sadness } ``` ### Data Fields - sentence: a line from the dataset - labels: labels corresponding to the emotion as an integer Where the number indicates the emotion in ascending alphabetical order: anger:1, anticipation:2, disgust:3, fear:4, joy:5, sadness:6, surprise:7, trust:8, with neutral:0 where applicable. ### Data Splits For English: Number of unique data points: 17528 ('en_annotated' config) + 9675 ('en_neutral' config) Number of emotions: 8 (+neutral) For Finnish: Number of unique data points: 14449 ('fi_annotated' config) + 10794 ('fi_neutral' config) Number of emotions: 8 (+neutral) ## 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 License: Creative Commons Attribution 4.0 International License (CC-BY) ### Citation Information @inproceedings{ohman2020xed, title={XED: A Multilingual Dataset for Sentiment Analysis and Emotion Detection}, author={{\"O}hman, Emily and P{\`a}mies, Marc and Kajava, Kaisla and Tiedemann, J{\"o}rg}, booktitle={The 28th International Conference on Computational Linguistics (COLING 2020)}, year={2020} } ### Contributions Thanks to [@lhoestq](https://github.com/lhoestq), [@harshalmittal4](https://github.com/harshalmittal4) for adding this dataset.

xed_en_fi

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["en", "fi"], "license": ["cc-by-4.0"], "multilinguality": ["multilingual"], "size_categories": ["10K<n<100K", "1K<n<10K"], "source_datasets": ["extended|other-OpenSubtitles2016"], "task_categories": ["text-classification"], "task_ids": ["intent-classification", "multi-class-classification", "multi-label-classification", "sentiment-classification"], "paperswithcode_id": "xed", "pretty_name": "XedEnglishFinnish", "config_names": ["en_annotated", "en_neutral", "fi_annotated", "fi_neutral"], "dataset_info": [{"config_name": "en_annotated", "features": [{"name": "sentence", "dtype": "string"}, {"name": "labels", "sequence": {"class_label": {"names": {"0": "neutral", "1": "anger", "2": "anticipation", "3": "disgust", "4": "fear", "5": "joy", "6": "sadness", "7": "surprise", "8": "trust"}}}}], "splits": [{"name": "train", "num_bytes": 1018485, "num_examples": 17528}], "download_size": 2421235, "dataset_size": 1018485}, {"config_name": "en_neutral", "features": [{"name": "sentence", "dtype": "string"}, {"name": "labels", "dtype": {"class_label": {"names": {"0": "neutral", "1": "anger", "2": "anticipation", "3": "disgust", "4": "fear", "5": "joy", "6": "sadness", "7": "surprise", "8": "trust"}}}}], "splits": [{"name": "train", "num_bytes": 401129, "num_examples": 9675}], "download_size": 2421235, "dataset_size": 401129}, {"config_name": "fi_annotated", "features": [{"name": "sentence", "dtype": "string"}, {"name": "labels", "sequence": {"class_label": {"names": {"0": "neutral", "1": "anger", "2": "anticipation", "3": "disgust", "4": "fear", "5": "joy", "6": "sadness", "7": "surprise", "8": "trust"}}}}], "splits": [{"name": "train", "num_bytes": 756224, "num_examples": 14449}], "download_size": 2421235, "dataset_size": 756224}, {"config_name": "fi_neutral", "features": [{"name": "sentence", "dtype": "string"}, {"name": "labels", "dtype": {"class_label": {"names": {"0": "neutral", "1": "anger", "2": "anticipation", "3": "disgust", "4": "fear", "5": "joy", "6": "sadness", "7": "surprise", "8": "trust"}}}}], "splits": [{"name": "train", "num_bytes": 427499, "num_examples": 10794}], "download_size": 2421235, "dataset_size": 427499}]}

2024-01-18T11:18:43+00:00

TAGS #task_categories-text-classification #task_ids-intent-classification #task_ids-multi-class-classification #task_ids-multi-label-classification #task_ids-sentiment-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-multilingual #size_categories-10K<n<100K #size_categories-1K<n<10K #source_datasets-extended|other-OpenSubtitles2016 #language-English #language-Finnish #license-cc-by-4.0 #arxiv-2011.01612 #region-us

# Dataset Card for xed_english_finnish ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: - Repository: Github - Paper: Arxiv - Leaderboard: - Point of Contact: ### Dataset Summary This is the XED dataset. The dataset consists of emotion annotated movie subtitles from OPUS. We use Plutchik's 8 core emotions to annotate. The data is multilabel. The original annotations have been sourced for mainly English and Finnish. For the English data we used Stanford NER (named entity recognition) (Finkel et al., 2005) to replace names and locations with the tags: [PERSON] and [LOCATION] respectively. For the Finnish data, we replaced names and locations using the Turku NER corpus (Luoma et al., 2020). ### Supported Tasks and Leaderboards Sentiment Classification, Multilabel Classification, Multilabel Classification, Intent Classification ### Languages English, Finnish ## Dataset Structure ### Data Instances ### Data Fields - sentence: a line from the dataset - labels: labels corresponding to the emotion as an integer Where the number indicates the emotion in ascending alphabetical order: anger:1, anticipation:2, disgust:3, fear:4, joy:5, sadness:6, surprise:7, trust:8, with neutral:0 where applicable. ### Data Splits For English: Number of unique data points: 17528 ('en_annotated' config) + 9675 ('en_neutral' config) Number of emotions: 8 (+neutral) For Finnish: Number of unique data points: 14449 ('fi_annotated' config) + 10794 ('fi_neutral' config) Number of emotions: 8 (+neutral) ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information License: Creative Commons Attribution 4.0 International License (CC-BY) @inproceedings{ohman2020xed, title={XED: A Multilingual Dataset for Sentiment Analysis and Emotion Detection}, author={{\"O}hman, Emily and P{\'a}mies, Marc and Kajava, Kaisla and Tiedemann, J{\"o}rg}, booktitle={The 28th International Conference on Computational Linguistics (COLING 2020)}, year={2020} } ### Contributions Thanks to @lhoestq, @harshalmittal4 for adding this dataset.

11926279ebd121c823cb5511ede5c1789764870b

# Dataset Card for XGLUE ## 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:** [XGLUE homepage](https://microsoft.github.io/XGLUE/) - **Paper:** [XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation](https://arxiv.org/abs/2004.01401) - **Point of Contact:** [xglue@microsoft.com](mailto:xglue@microsoft.com?subject=XGLUE Feedback) ### Dataset Summary XGLUE is a new benchmark dataset to evaluate the performance of cross-lingual pre-trained models with respect to cross-lingual natural language understanding and generation. XGLUE is composed of 11 tasks spans 19 languages. For each task, the training data is only available in English. This means that to succeed at XGLUE, a model must have a strong zero-shot cross-lingual transfer capability to learn from the English data of a specific task and transfer what it learned to other languages. Comparing to its concurrent work XTREME, XGLUE has two characteristics: First, it includes cross-lingual NLU and cross-lingual NLG tasks at the same time; Second, besides including 5 existing cross-lingual tasks (i.e. NER, POS, MLQA, PAWS-X and XNLI), XGLUE selects 6 new tasks from Bing scenarios as well, including News Classification (NC), Query-Ad Matching (QADSM), Web Page Ranking (WPR), QA Matching (QAM), Question Generation (QG) and News Title Generation (NTG). Such diversities of languages, tasks and task origin provide a comprehensive benchmark for quantifying the quality of a pre-trained model on cross-lingual natural language understanding and generation. The training data of each task is in English while the validation and test data is present in multiple different languages. The following table shows which languages are present as validation and test data for each config.  Therefore, for each config, a cross-lingual pre-trained model should be fine-tuned on the English training data, and evaluated on for all languages. ### Supported Tasks and Leaderboards The XGLUE leaderboard can be found on the [homepage](https://microsoft.github.io/XGLUE/) and consists of a XGLUE-Understanding Score (the average of the tasks `ner`, `pos`, `mlqa`, `nc`, `xnli`, `paws-x`, `qadsm`, `wpr`, `qam`) and a XGLUE-Generation Score (the average of the tasks `qg`, `ntg`). ### Languages For all tasks (configurations), the "train" split is in English (`en`). For each task, the "validation" and "test" splits are present in these languages: - ner: `en`, `de`, `es`, `nl` - pos: `en`, `de`, `es`, `nl`, `bg`, `el`, `fr`, `pl`, `tr`, `vi`, `zh`, `ur`, `hi`, `it`, `ar`, `ru`, `th` - mlqa: `en`, `de`, `ar`, `es`, `hi`, `vi`, `zh` - nc: `en`, `de`, `es`, `fr`, `ru` - xnli: `en`, `ar`, `bg`, `de`, `el`, `es`, `fr`, `hi`, `ru`, `sw`, `th`, `tr`, `ur`, `vi`, `zh` - paws-x: `en`, `de`, `es`, `fr` - qadsm: `en`, `de`, `fr` - wpr: `en`, `de`, `es`, `fr`, `it`, `pt`, `zh` - qam: `en`, `de`, `fr` - qg: `en`, `de`, `es`, `fr`, `it`, `pt` - ntg: `en`, `de`, `es`, `fr`, `ru` ## Dataset Structure ### Data Instances #### ner An example of 'test.nl' looks as follows. ```json { "ner": [ "O", "O", "O", "B-LOC", "O", "B-LOC", "O", "B-LOC", "O", "O", "O", "O", "O", "O", "O", "B-PER", "I-PER", "O", "O", "B-LOC", "O", "O" ], "words": [ "Dat", "is", "in", "Itali\u00eb", ",", "Spanje", "of", "Engeland", "misschien", "geen", "probleem", ",", "maar", "volgens", "'", "Der", "Kaiser", "'", "in", "Duitsland", "wel", "." ] } ``` #### pos An example of 'test.fr' looks as follows. ```json { "pos": [ "PRON", "VERB", "SCONJ", "ADP", "PRON", "CCONJ", "DET", "NOUN", "ADP", "NOUN", "CCONJ", "NOUN", "ADJ", "PRON", "PRON", "AUX", "ADV", "VERB", "PUNCT", "PRON", "VERB", "VERB", "DET", "ADJ", "NOUN", "ADP", "DET", "NOUN", "PUNCT" ], "words": [ "Je", "sens", "qu'", "entre", "\u00e7a", "et", "les", "films", "de", "m\u00e9decins", "et", "scientifiques", "fous", "que", "nous", "avons", "d\u00e9j\u00e0", "vus", ",", "nous", "pourrions", "emprunter", "un", "autre", "chemin", "pour", "l'", "origine", "." ] } ``` #### mlqa An example of 'test.hi' looks as follows. ```json { "answers": { "answer_start": [ 378 ], "text": [ "\u0909\u0924\u094d\u0924\u0930 \u092a\u0942\u0930\u094d\u0935" ] }, "context": "\u0909\u0938\u0940 \"\u090f\u0930\u093f\u092f\u093e XX \" \u0928\u093e\u092e\u0915\u0930\u0923 \u092a\u094d\u0930\u0923\u093e\u0932\u0940 \u0915\u093e \u092a\u094d\u0930\u092f\u094b\u0917 \u0928\u0947\u0935\u093e\u0926\u093e \u092a\u0930\u0940\u0915\u094d\u0937\u0923 \u0938\u094d\u0925\u0932 \u0915\u0947 \u0905\u0928\u094d\u092f \u092d\u093e\u0917\u094b\u0902 \u0915\u0947 \u0932\u093f\u090f \u0915\u093f\u092f\u093e \u0917\u092f\u093e \u0939\u0948\u0964\u092e\u0942\u0932 \u0930\u0942\u092a \u092e\u0947\u0902 6 \u092c\u091f\u0947 10 \u092e\u0940\u0932 \u0915\u093e \u092f\u0939 \u0906\u092f\u0924\u093e\u0915\u093e\u0930 \u0905\u0921\u094d\u0921\u093e \u0905\u092c \u0924\u0925\u093e\u0915\u0925\u093f\u0924 '\u0917\u094d\u0930\u0942\u092e \u092c\u0949\u0915\u094d\u0938 \" \u0915\u093e \u090f\u0915 \u092d\u093e\u0917 \u0939\u0948, \u091c\u094b \u0915\u093f 23 \u092c\u091f\u0947 25.3 \u092e\u0940\u0932 \u0915\u093e \u090f\u0915 \u092a\u094d\u0930\u0924\u093f\u092c\u0902\u0927\u093f\u0924 \u0939\u0935\u093e\u0908 \u0915\u094d\u0937\u0947\u0924\u094d\u0930 \u0939\u0948\u0964 \u092f\u0939 \u0915\u094d\u0937\u0947\u0924\u094d\u0930 NTS \u0915\u0947 \u0906\u0902\u0924\u0930\u093f\u0915 \u0938\u0921\u093c\u0915 \u092a\u094d\u0930\u092c\u0902\u0927\u0928 \u0938\u0947 \u091c\u0941\u0921\u093c\u093e \u0939\u0948, \u091c\u093f\u0938\u0915\u0940 \u092a\u0915\u094d\u0915\u0940 \u0938\u0921\u093c\u0915\u0947\u0902 \u0926\u0915\u094d\u0937\u093f\u0923 \u092e\u0947\u0902 \u092e\u0930\u0915\u0930\u0940 \u0915\u0940 \u0913\u0930 \u0914\u0930 \u092a\u0936\u094d\u091a\u093f\u092e \u092e\u0947\u0902 \u092f\u0941\u0915\u094d\u0915\u093e \u092b\u094d\u0932\u0948\u091f \u0915\u0940 \u0913\u0930 \u091c\u093e\u0924\u0940 \u0939\u0948\u0902\u0964 \u091d\u0940\u0932 \u0938\u0947 \u0909\u0924\u094d\u0924\u0930 \u092a\u0942\u0930\u094d\u0935 \u0915\u0940 \u0913\u0930 \u092c\u0922\u093c\u0924\u0947 \u0939\u0941\u090f \u0935\u094d\u092f\u093e\u092a\u0915 \u0914\u0930 \u0914\u0930 \u0938\u0941\u0935\u094d\u092f\u0935\u0938\u094d\u0925\u093f\u0924 \u0917\u094d\u0930\u0942\u092e \u091d\u0940\u0932 \u0915\u0940 \u0938\u0921\u093c\u0915\u0947\u0902 \u090f\u0915 \u0926\u0930\u094d\u0930\u0947 \u0915\u0947 \u091c\u0930\u093f\u092f\u0947 \u092a\u0947\u091a\u0940\u0926\u093e \u092a\u0939\u093e\u0921\u093c\u093f\u092f\u094b\u0902 \u0938\u0947 \u0939\u094b\u0915\u0930 \u0917\u0941\u091c\u0930\u0924\u0940 \u0939\u0948\u0902\u0964 \u092a\u0939\u0932\u0947 \u0938\u0921\u093c\u0915\u0947\u0902 \u0917\u094d\u0930\u0942\u092e \u0918\u093e\u091f\u0940", "question": "\u091d\u0940\u0932 \u0915\u0947 \u0938\u093e\u092a\u0947\u0915\u094d\u0937 \u0917\u094d\u0930\u0942\u092e \u0932\u0947\u0915 \u0930\u094b\u0921 \u0915\u0939\u093e\u0901 \u091c\u093e\u0924\u0940 \u0925\u0940?" } ``` #### nc An example of 'test.es' looks as follows. ```json { "news_body": "El bizcocho es seguramente el producto m\u00e1s b\u00e1sico y sencillo de toda la reposter\u00eda : consiste en poco m\u00e1s que mezclar unos cuantos ingredientes, meterlos al horno y esperar a que se hagan. Por obra y gracia del impulsor qu\u00edmico, tambi\u00e9n conocido como \"levadura de tipo Royal\", despu\u00e9s de un rato de calorcito esta combinaci\u00f3n de harina, az\u00facar, huevo, grasa -aceite o mantequilla- y l\u00e1cteo se transforma en uno de los productos m\u00e1s deliciosos que existen para desayunar o merendar . Por muy manazas que seas, es m\u00e1s que probable que tu bizcocho casero supere en calidad a cualquier infamia industrial envasada. Para lograr un bizcocho digno de admiraci\u00f3n s\u00f3lo tienes que respetar unas pocas normas que afectan a los ingredientes, proporciones, mezclado, horneado y desmoldado. Todas las tienes resumidas en unos dos minutos el v\u00eddeo de arriba, en el que adem \u00e1s aprender\u00e1s alg\u00fan truquillo para que tu bizcochaco quede m\u00e1s fino, jugoso, esponjoso y amoroso. M\u00e1s en MSN:", "news_category": "foodanddrink", "news_title": "Cocina para lerdos: las leyes del bizcocho" } ``` #### xnli An example of 'validation.th' looks as follows. ```json { "hypothesis": "\u0e40\u0e02\u0e32\u0e42\u0e17\u0e23\u0e2b\u0e32\u0e40\u0e40\u0e21\u0e48\u0e02\u0e2d\u0e07\u0e40\u0e02\u0e32\u0e2d\u0e22\u0e48\u0e32\u0e07\u0e23\u0e27\u0e14\u0e40\u0e23\u0e47\u0e27\u0e2b\u0e25\u0e31\u0e07\u0e08\u0e32\u0e01\u0e17\u0e35\u0e48\u0e23\u0e16\u0e42\u0e23\u0e07\u0e40\u0e23\u0e35\u0e22\u0e19\u0e2a\u0e48\u0e07\u0e40\u0e02\u0e32\u0e40\u0e40\u0e25\u0e49\u0e27", "label": 1, "premise": "\u0e41\u0e25\u0e30\u0e40\u0e02\u0e32\u0e1e\u0e39\u0e14\u0e27\u0e48\u0e32, \u0e21\u0e48\u0e32\u0e21\u0e4a\u0e32 \u0e1c\u0e21\u0e2d\u0e22\u0e39\u0e48\u0e1a\u0e49\u0e32\u0e19" } ``` #### paws-x An example of 'test.es' looks as follows. ```json { "label": 1, "sentence1": "La excepci\u00f3n fue entre fines de 2005 y 2009 cuando jug\u00f3 en Suecia con Carlstad United BK, Serbia con FK Borac \u010ca\u010dak y el FC Terek Grozny de Rusia.", "sentence2": "La excepci\u00f3n se dio entre fines del 2005 y 2009, cuando jug\u00f3 con Suecia en el Carlstad United BK, Serbia con el FK Borac \u010ca\u010dak y el FC Terek Grozny de Rusia." } ``` #### qadsm An example of 'train' looks as follows. ```json { "ad_description": "Your New England Cruise Awaits! Holland America Line Official Site.", "ad_title": "New England Cruises", "query": "cruise portland maine", "relevance_label": 1 } ``` #### wpr An example of 'test.zh' looks as follows. ```json { "query": "maxpro\u5b98\u7f51", "relavance_label": 0, "web_page_snippet": "\u5728\u7ebf\u8d2d\u4e70\uff0c\u552e\u540e\u670d\u52a1\u3002vivo\u667a\u80fd\u624b\u673a\u5f53\u5b63\u660e\u661f\u673a\u578b\u6709NEX\uff0cvivo X21\uff0cvivo X20\uff0c\uff0cvivo X23\u7b49\uff0c\u5728vivo\u5b98\u7f51\u8d2d\u4e70\u624b\u673a\u53ef\u4ee5\u4eab\u53d712 \u671f\u514d\u606f\u4ed8\u6b3e\u3002 \u54c1\u724c Funtouch OS \u4f53\u9a8c\u5e97 | ...", "wed_page_title": "vivo\u667a\u80fd\u624b\u673a\u5b98\u65b9\u7f51\u7ad9-AI\u975e\u51e1\u6444\u5f71X23" } ``` #### qam An example of 'validation.en' looks as follows. ```json { "annswer": "Erikson has stated that after the last novel of the Malazan Book of the Fallen was finished, he and Esslemont would write a comprehensive guide tentatively named The Encyclopaedia Malazica.", "label": 0, "question": "main character of malazan book of the fallen" } ``` #### qg An example of 'test.de' looks as follows. ```json { "answer_passage": "Medien bei WhatsApp automatisch speichern. Tippen Sie oben rechts unter WhatsApp auf die drei Punkte oder auf die Men\u00fc-Taste Ihres Smartphones. Dort wechseln Sie in die \"Einstellungen\" und von hier aus weiter zu den \"Chat-Einstellungen\". Unter dem Punkt \"Medien Auto-Download\" k\u00f6nnen Sie festlegen, wann die WhatsApp-Bilder heruntergeladen werden sollen.", "question": "speichenn von whats app bilder unterbinden" } ``` #### ntg An example of 'test.en' looks as follows. ```json { "news_body": "Check out this vintage Willys Pickup! As they say, the devil is in the details, and it's not every day you see such attention paid to every last area of a restoration like with this 1961 Willys Pickup . Already the Pickup has a unique look that shares some styling with the Jeep, plus some original touches you don't get anywhere else. It's a classy way to show up to any event, all thanks to Hollywood Motors . A burgundy paint job contrasts with white lower panels and the roof. Plenty of tasteful chrome details grace the exterior, including the bumpers, headlight bezels, crossmembers on the grille, hood latches, taillight bezels, exhaust finisher, tailgate hinges, etc. Steel wheels painted white and chrome hubs are a tasteful addition. Beautiful oak side steps and bed strips add a touch of craftsmanship to this ride. This truck is of real showroom quality, thanks to the astoundingly detailed restoration work performed on it, making this Willys Pickup a fierce contender for best of show. Under that beautiful hood is a 225 Buick V6 engine mated to a three-speed manual transmission, so you enjoy an ideal level of control. Four wheel drive is functional, making it that much more utilitarian and downright cool. The tires are new, so you can enjoy a lot of life out of them, while the wheels and hubs are in great condition. Just in case, a fifth wheel with a tire and a side mount are included. Just as important, this Pickup runs smoothly, so you can go cruising or even hit the open road if you're interested in participating in some classic rallies. You might associate Willys with the famous Jeep CJ, but the automaker did produce a fair amount of trucks. The Pickup is quite the unique example, thanks to distinct styling that really turns heads, making it a favorite at quite a few shows. Source: Hollywood Motors Check These Rides Out Too: Fear No Trails With These Off-Roaders 1965 Pontiac GTO: American Icon For Sale In Canada Low-Mileage 1955 Chevy 3100 Represents Turn In Pickup Market", "news_title": "This 1961 Willys Pickup Will Let You Cruise In Style" } ``` ### Data Fields #### ner In the following each data field in ner is explained. The data fields are the same among all splits. - `words`: a list of words composing the sentence. - `ner`: a list of entitity classes corresponding to each word respectively. #### pos In the following each data field in pos is explained. The data fields are the same among all splits. - `words`: a list of words composing the sentence. - `pos`: a list of "part-of-speech" classes corresponding to each word respectively. #### mlqa In the following each data field in mlqa is explained. The data fields are the same among all splits. - `context`: a string, the context containing the answer. - `question`: a string, the question to be answered. - `answers`: a string, the answer to `question`. #### nc In the following each data field in nc is explained. The data fields are the same among all splits. - `news_title`: a string, to the title of the news report. - `news_body`: a string, to the actual news report. - `news_category`: a string, the category of the news report, *e.g.* `foodanddrink` #### xnli In the following each data field in xnli is explained. The data fields are the same among all splits. - `premise`: a string, the context/premise, *i.e.* the first sentence for natural language inference. - `hypothesis`: a string, a sentence whereas its relation to `premise` is to be classified, *i.e.* the second sentence for natural language inference. - `label`: a class catory (int), natural language inference relation class between `hypothesis` and `premise`. One of 0: entailment, 1: contradiction, 2: neutral. #### paws-x In the following each data field in paws-x is explained. The data fields are the same among all splits. - `sentence1`: a string, a sentence. - `sentence2`: a string, a sentence whereas the sentence is either a paraphrase of `sentence1` or not. - `label`: a class label (int), whether `sentence2` is a paraphrase of `sentence1` One of 0: different, 1: same. #### qadsm In the following each data field in qadsm is explained. The data fields are the same among all splits. - `query`: a string, the search query one would insert into a search engine. - `ad_title`: a string, the title of the advertisement. - `ad_description`: a string, the content of the advertisement, *i.e.* the main body. - `relevance_label`: a class label (int), how relevant the advertisement `ad_title` + `ad_description` is to the search query `query`. One of 0: Bad, 1: Good. #### wpr In the following each data field in wpr is explained. The data fields are the same among all splits. - `query`: a string, the search query one would insert into a search engine. - `web_page_title`: a string, the title of a web page. - `web_page_snippet`: a string, the content of a web page, *i.e.* the main body. - `relavance_label`: a class label (int), how relevant the web page `web_page_snippet` + `web_page_snippet` is to the search query `query`. One of 0: Bad, 1: Fair, 2: Good, 3: Excellent, 4: Perfect. #### qam In the following each data field in qam is explained. The data fields are the same among all splits. - `question`: a string, a question. - `answer`: a string, a possible answer to `question`. - `label`: a class label (int), whether the `answer` is relevant to the `question`. One of 0: False, 1: True. #### qg In the following each data field in qg is explained. The data fields are the same among all splits. - `answer_passage`: a string, a detailed answer to the `question`. - `question`: a string, a question. #### ntg In the following each data field in ntg is explained. The data fields are the same among all splits. - `news_body`: a string, the content of a news article. - `news_title`: a string, the title corresponding to the news article `news_body`. ### Data Splits #### ner The following table shows the number of data samples/number of rows for each split in ner. | |train|validation.en|validation.de|validation.es|validation.nl|test.en|test.de|test.es|test.nl| |---|----:|------------:|------------:|------------:|------------:|------:|------:|------:|------:| |ner|14042| 3252| 2874| 1923| 2895| 3454| 3007| 1523| 5202| #### pos The following table shows the number of data samples/number of rows for each split in pos. | |train|validation.en|validation.de|validation.es|validation.nl|validation.bg|validation.el|validation.fr|validation.pl|validation.tr|validation.vi|validation.zh|validation.ur|validation.hi|validation.it|validation.ar|validation.ru|validation.th|test.en|test.de|test.es|test.nl|test.bg|test.el|test.fr|test.pl|test.tr|test.vi|test.zh|test.ur|test.hi|test.it|test.ar|test.ru|test.th| |---|----:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:| |pos|25376| 2001| 798| 1399| 717| 1114| 402| 1475| 2214| 987| 799| 499| 551| 1658| 563| 908| 578| 497| 2076| 976| 425| 595| 1115| 455| 415| 2214| 982| 799| 499| 534| 1683| 481| 679| 600| 497| #### mlqa The following table shows the number of data samples/number of rows for each split in mlqa. | |train|validation.en|validation.de|validation.ar|validation.es|validation.hi|validation.vi|validation.zh|test.en|test.de|test.ar|test.es|test.hi|test.vi|test.zh| |----|----:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------:|------:|------:|------:|------:|------:|------:| |mlqa|87599| 1148| 512| 517| 500| 507| 511| 504| 11590| 4517| 5335| 5253| 4918| 5495| 5137| #### nc The following table shows the number of data samples/number of rows for each split in nc. | |train |validation.en|validation.de|validation.es|validation.fr|validation.ru|test.en|test.de|test.es|test.fr|test.ru| |---|-----:|------------:|------------:|------------:|------------:|------------:|------:|------:|------:|------:|------:| |nc |100000| 10000| 10000| 10000| 10000| 10000| 10000| 10000| 10000| 10000| 10000| #### xnli The following table shows the number of data samples/number of rows for each split in xnli. | |train |validation.en|validation.ar|validation.bg|validation.de|validation.el|validation.es|validation.fr|validation.hi|validation.ru|validation.sw|validation.th|validation.tr|validation.ur|validation.vi|validation.zh|test.en|test.ar|test.bg|test.de|test.el|test.es|test.fr|test.hi|test.ru|test.sw|test.th|test.tr|test.ur|test.vi|test.zh| |----|-----:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:| |xnli|392702| 2490| 2490| 2490| 2490| 2490| 2490| 2490| 2490| 2490| 2490| 2490| 2490| 2490| 2490| 2490| 5010| 5010| 5010| 5010| 5010| 5010| 5010| 5010| 5010| 5010| 5010| 5010| 5010| 5010| 5010| #### nc The following table shows the number of data samples/number of rows for each split in nc. | |train |validation.en|validation.de|validation.es|validation.fr|validation.ru|test.en|test.de|test.es|test.fr|test.ru| |---|-----:|------------:|------------:|------------:|------------:|------------:|------:|------:|------:|------:|------:| |nc |100000| 10000| 10000| 10000| 10000| 10000| 10000| 10000| 10000| 10000| 10000| #### xnli The following table shows the number of data samples/number of rows for each split in xnli. | |train |validation.en|validation.ar|validation.bg|validation.de|validation.el|validation.es|validation.fr|validation.hi|validation.ru|validation.sw|validation.th|validation.tr|validation.ur|validation.vi|validation.zh|test.en|test.ar|test.bg|test.de|test.el|test.es|test.fr|test.hi|test.ru|test.sw|test.th|test.tr|test.ur|test.vi|test.zh| |----|-----:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:| |xnli|392702| 2490| 2490| 2490| 2490| 2490| 2490| 2490| 2490| 2490| 2490| 2490| 2490| 2490| 2490| 2490| 5010| 5010| 5010| 5010| 5010| 5010| 5010| 5010| 5010| 5010| 5010| 5010| 5010| 5010| 5010| #### paws-x The following table shows the number of data samples/number of rows for each split in paws-x. | |train|validation.en|validation.de|validation.es|validation.fr|test.en|test.de|test.es|test.fr| |------|----:|------------:|------------:|------------:|------------:|------:|------:|------:|------:| |paws-x|49401| 2000| 2000| 2000| 2000| 2000| 2000| 2000| 2000| #### qadsm The following table shows the number of data samples/number of rows for each split in qadsm. | |train |validation.en|validation.de|validation.fr|test.en|test.de|test.fr| |-----|-----:|------------:|------------:|------------:|------:|------:|------:| |qadsm|100000| 10000| 10000| 10000| 10000| 10000| 10000| #### wpr The following table shows the number of data samples/number of rows for each split in wpr. | |train|validation.en|validation.de|validation.es|validation.fr|validation.it|validation.pt|validation.zh|test.en|test.de|test.es|test.fr|test.it|test.pt|test.zh| |---|----:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------:|------:|------:|------:|------:|------:|------:| |wpr|99997| 10008| 10004| 10004| 10005| 10003| 10001| 10002| 10004| 9997| 10006| 10020| 10001| 10015| 9999| #### qam The following table shows the number of data samples/number of rows for each split in qam. | |train |validation.en|validation.de|validation.fr|test.en|test.de|test.fr| |---|-----:|------------:|------------:|------------:|------:|------:|------:| |qam|100000| 10000| 10000| 10000| 10000| 10000| 10000| #### qg The following table shows the number of data samples/number of rows for each split in qg. | |train |validation.en|validation.de|validation.es|validation.fr|validation.it|validation.pt|test.en|test.de|test.es|test.fr|test.it|test.pt| |---|-----:|------------:|------------:|------------:|------------:|------------:|------------:|------:|------:|------:|------:|------:|------:| |qg |100000| 10000| 10000| 10000| 10000| 10000| 10000| 10000| 10000| 10000| 10000| 10000| 10000| #### ntg The following table shows the number of data samples/number of rows for each split in ntg. | |train |validation.en|validation.de|validation.es|validation.fr|validation.ru|test.en|test.de|test.es|test.fr|test.ru| |---|-----:|------------:|------------:|------------:|------------:|------------:|------:|------:|------:|------:|------:| |ntg|300000| 10000| 10000| 10000| 10000| 10000| 10000| 10000| 10000| 10000| 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 [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 The dataset is maintained mainly by Yaobo Liang, Yeyun Gong, Nan Duan, Ming Gong, Linjun Shou, and Daniel Campos from Microsoft Research. ### Licensing Information The XGLUE datasets are intended for non-commercial research purposes only to promote advancement in the field of artificial intelligence and related areas, and is made available free of charge without extending any license or other intellectual property rights. The dataset is provided “as is” without warranty and usage of the data has risks since we may not own the underlying rights in the documents. We are not be liable for any damages related to use of the dataset. Feedback is voluntarily given and can be used as we see fit. Upon violation of any of these terms, your rights to use the dataset will end automatically. If you have questions about use of the dataset or any research outputs in your products or services, we encourage you to undertake your own independent legal review. For other questions, please feel free to contact us. ### Citation Information If you use this dataset, please cite it. Additionally, since XGLUE is also built out of exiting 5 datasets, please ensure you cite all of them. An example: ``` We evaluate our model using the XGLUE benchmark \cite{Liang2020XGLUEAN}, a cross-lingual evaluation benchmark consiting of Named Entity Resolution (NER) \cite{Sang2002IntroductionTT} \cite{Sang2003IntroductionTT}, Part of Speech Tagging (POS) \cite{11234/1-3105}, News Classification (NC), MLQA \cite{Lewis2019MLQAEC}, XNLI \cite{Conneau2018XNLIEC}, PAWS-X \cite{Yang2019PAWSXAC}, Query-Ad Matching (QADSM), Web Page Ranking (WPR), QA Matching (QAM), Question Generation (QG) and News Title Generation (NTG). ``` ``` @article{Liang2020XGLUEAN, title={XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation}, author={Yaobo Liang and Nan Duan and Yeyun Gong and Ning Wu and Fenfei Guo and Weizhen Qi and Ming Gong and Linjun Shou and Daxin Jiang and Guihong Cao and Xiaodong Fan and Ruofei Zhang and Rahul Agrawal and Edward Cui and Sining Wei and Taroon Bharti and Ying Qiao and Jiun-Hung Chen and Winnie Wu and Shuguang Liu and Fan Yang and Daniel Campos and Rangan Majumder and Ming Zhou}, journal={arXiv}, year={2020}, volume={abs/2004.01401} } @misc{11234/1-3105, title={Universal Dependencies 2.5}, author={Zeman, Daniel and Nivre, Joakim and Abrams, Mitchell and Aepli, No{\"e}mi and Agi{\'c}, {\v Z}eljko and Ahrenberg, Lars and Aleksandravi{\v c}i{\=u}t{\.e}, Gabriel{\.e} and Antonsen, Lene and Aplonova, Katya and Aranzabe, Maria Jesus and Arutie, Gashaw and Asahara, Masayuki and Ateyah, Luma and Attia, Mohammed and Atutxa, Aitziber and Augustinus, Liesbeth and Badmaeva, Elena and Ballesteros, Miguel and Banerjee, Esha and Bank, Sebastian and Barbu Mititelu, Verginica and Basmov, Victoria and Batchelor, Colin and Bauer, John and Bellato, Sandra and Bengoetxea, Kepa and Berzak, Yevgeni and Bhat, Irshad Ahmad and Bhat, Riyaz Ahmad and Biagetti, Erica and Bick, Eckhard and Bielinskien{\.e}, Agn{\.e} and Blokland, Rogier and Bobicev, Victoria and Boizou, Lo{\"{\i}}c and Borges V{\"o}lker, Emanuel and B{\"o}rstell, Carl and Bosco, Cristina and Bouma, Gosse and Bowman, Sam and Boyd, Adriane and Brokait{\.e}, Kristina and Burchardt, Aljoscha and Candito, Marie and Caron, Bernard and Caron, Gauthier and Cavalcanti, Tatiana and Cebiro{\u g}lu Eryi{\u g}it, G{\"u}l{\c s}en and Cecchini, Flavio Massimiliano and Celano, Giuseppe G. A. and {\v C}{\'e}pl{\"o}, Slavom{\'{\i}}r and Cetin, Savas and Chalub, Fabricio and Choi, Jinho and Cho, Yongseok and Chun, Jayeol and Cignarella, Alessandra T. and Cinkov{\'a}, Silvie and Collomb, Aur{\'e}lie and {\c C}{\"o}ltekin, {\c C}a{\u g}r{\i} and Connor, Miriam and Courtin, Marine and Davidson, Elizabeth and de Marneffe, Marie-Catherine and de Paiva, Valeria and de Souza, Elvis and Diaz de Ilarraza, Arantza and Dickerson, Carly and Dione, Bamba and Dirix, Peter and Dobrovoljc, Kaja and Dozat, Timothy and Droganova, Kira and Dwivedi, Puneet and Eckhoff, Hanne and Eli, Marhaba and Elkahky, Ali and Ephrem, Binyam and Erina, Olga and Erjavec, Toma{\v z} and Etienne, Aline and Evelyn, Wograine and Farkas, Rich{\'a}rd and Fernandez Alcalde, Hector and Foster, Jennifer and Freitas, Cl{\'a}udia and Fujita, Kazunori and Gajdo{\v s}ov{\'a}, Katar{\'{\i}}na and Galbraith, Daniel and Garcia, Marcos and G{\"a}rdenfors, Moa and Garza, Sebastian and Gerdes, Kim and Ginter, Filip and Goenaga, Iakes and Gojenola, Koldo and G{\"o}k{\i}rmak, Memduh and Goldberg, Yoav and G{\'o}mez Guinovart, Xavier and Gonz{\'a}lez Saavedra, Berta and Grici{\=u}t{\.e}, Bernadeta and Grioni, Matias and Gr{\=u}z{\={\i}}tis, Normunds and Guillaume, Bruno and Guillot-Barbance, C{\'e}line and Habash, Nizar and Haji{\v c}, Jan and Haji{\v c} jr., Jan and H{\"a}m{\"a}l{\"a}inen, Mika and H{\`a} M{\~y}, Linh and Han, Na-Rae and Harris, Kim and Haug, Dag and Heinecke, Johannes and Hennig, Felix and Hladk{\'a}, Barbora and Hlav{\'a}{\v c}ov{\'a}, Jaroslava and Hociung, Florinel and Hohle, Petter and Hwang, Jena and Ikeda, Takumi and Ion, Radu and Irimia, Elena and Ishola, {\d O}l{\'a}j{\'{\i}}d{\'e} and Jel{\'{\i}}nek, Tom{\'a}{\v s} and Johannsen, Anders and J{\o}rgensen, Fredrik and Juutinen, Markus and Ka{\c s}{\i}kara, H{\"u}ner and Kaasen, Andre and Kabaeva, Nadezhda and Kahane, Sylvain and Kanayama, Hiroshi and Kanerva, Jenna and Katz, Boris and Kayadelen, Tolga and Kenney, Jessica and Kettnerov{\'a}, V{\'a}clava and Kirchner, Jesse and Klementieva, Elena and K{\"o}hn, Arne and Kopacewicz, Kamil and Kotsyba, Natalia and Kovalevskait{\.e}, Jolanta and Krek, Simon and Kwak, Sookyoung and Laippala, Veronika and Lambertino, Lorenzo and Lam, Lucia and Lando, Tatiana and Larasati, Septina Dian and Lavrentiev, Alexei and Lee, John and L{\^e} H{\`{\^o}}ng, Phương and Lenci, Alessandro and Lertpradit, Saran and Leung, Herman and Li, Cheuk Ying and Li, Josie and Li, Keying and Lim, {KyungTae} and Liovina, Maria and Li, Yuan and Ljube{\v s}i{\'c}, Nikola and Loginova, Olga and Lyashevskaya, Olga and Lynn, Teresa and Macketanz, Vivien and Makazhanov, Aibek and Mandl, Michael and Manning, Christopher and Manurung, Ruli and M{\u a}r{\u a}nduc, C{\u a}t{\u a}lina and Mare{\v c}ek, David and Marheinecke, Katrin and Mart{\'{\i}}nez Alonso, H{\'e}ctor and Martins, Andr{\'e} and Ma{\v s}ek, Jan and Matsumoto, Yuji and {McDonald}, Ryan and {McGuinness}, Sarah and Mendon{\c c}a, Gustavo and Miekka, Niko and Misirpashayeva, Margarita and Missil{\"a}, Anna and Mititelu, C{\u a}t{\u a}lin and Mitrofan, Maria and Miyao, Yusuke and Montemagni, Simonetta and More, Amir and Moreno Romero, Laura and Mori, Keiko Sophie and Morioka, Tomohiko and Mori, Shinsuke and Moro, Shigeki and Mortensen, Bjartur and Moskalevskyi, Bohdan and Muischnek, Kadri and Munro, Robert and Murawaki, Yugo and M{\"u}{\"u}risep, Kaili and Nainwani, Pinkey and Navarro Hor{\~n}iacek, Juan Ignacio and Nedoluzhko, Anna and Ne{\v s}pore-B{\=e}rzkalne, Gunta and Nguy{\~{\^e}}n Th{\d i}, Lương and Nguy{\~{\^e}}n Th{\d i} Minh, Huy{\`{\^e}}n and Nikaido, Yoshihiro and Nikolaev, Vitaly and Nitisaroj, Rattima and Nurmi, Hanna and Ojala, Stina and Ojha, Atul Kr. and Ol{\'u}{\`o}kun, Ad{\'e}day{\d o}̀ and Omura, Mai and Osenova, Petya and {\"O}stling, Robert and {\O}vrelid, Lilja and Partanen, Niko and Pascual, Elena and Passarotti, Marco and Patejuk, Agnieszka and Paulino-Passos, Guilherme and Peljak-{\L}api{\'n}ska, Angelika and Peng, Siyao and Perez, Cenel-Augusto and Perrier, Guy and Petrova, Daria and Petrov, Slav and Phelan, Jason and Piitulainen, Jussi and Pirinen, Tommi A and Pitler, Emily and Plank, Barbara and Poibeau, Thierry and Ponomareva, Larisa and Popel, Martin and Pretkalni{\c n}a, Lauma and Pr{\'e}vost, Sophie and Prokopidis, Prokopis and Przepi{\'o}rkowski, Adam and Puolakainen, Tiina and Pyysalo, Sampo and Qi, Peng and R{\"a}{\"a}bis, Andriela and Rademaker, Alexandre and Ramasamy, Loganathan and Rama, Taraka and Ramisch, Carlos and Ravishankar, Vinit and Real, Livy and Reddy, Siva and Rehm, Georg and Riabov, Ivan and Rie{\ss}ler, Michael and Rimkut{\.e}, Erika and Rinaldi, Larissa and Rituma, Laura and Rocha, Luisa and Romanenko, Mykhailo and Rosa, Rudolf and Rovati, Davide and Roșca, Valentin and Rudina, Olga and Rueter, Jack and Sadde, Shoval and Sagot, Beno{\^{\i}}t and Saleh, Shadi and Salomoni, Alessio and Samard{\v z}i{\'c}, Tanja and Samson, Stephanie and Sanguinetti, Manuela and S{\"a}rg, Dage and Saul{\={\i}}te, Baiba and Sawanakunanon, Yanin and Schneider, Nathan and Schuster, Sebastian and Seddah, Djam{\'e} and Seeker, Wolfgang and Seraji, Mojgan and Shen, Mo and Shimada, Atsuko and Shirasu, Hiroyuki and Shohibussirri, Muh and Sichinava, Dmitry and Silveira, Aline and Silveira, Natalia and Simi, Maria and Simionescu, Radu and Simk{\'o}, Katalin and {\v S}imkov{\'a}, M{\'a}ria and Simov, Kiril and Smith, Aaron and Soares-Bastos, Isabela and Spadine, Carolyn and Stella, Antonio and Straka, Milan and Strnadov{\'a}, Jana and Suhr, Alane and Sulubacak, Umut and Suzuki, Shingo and Sz{\'a}nt{\'o}, Zsolt and Taji, Dima and Takahashi, Yuta and Tamburini, Fabio and Tanaka, Takaaki and Tellier, Isabelle and Thomas, Guillaume and Torga, Liisi and Trosterud, Trond and Trukhina, Anna and Tsarfaty, Reut and Tyers, Francis and Uematsu, Sumire and Ure{\v s}ov{\'a}, Zde{\v n}ka and Uria, Larraitz and Uszkoreit, Hans and Utka, Andrius and Vajjala, Sowmya and van Niekerk, Daniel and van Noord, Gertjan and Varga, Viktor and Villemonte de la Clergerie, Eric and Vincze, Veronika and Wallin, Lars and Walsh, Abigail and Wang, Jing Xian and Washington, Jonathan North and Wendt, Maximilan and Williams, Seyi and Wir{\'e}n, Mats and Wittern, Christian and Woldemariam, Tsegay and Wong, Tak-sum and Wr{\'o}blewska, Alina and Yako, Mary and Yamazaki, Naoki and Yan, Chunxiao and Yasuoka, Koichi and Yavrumyan, Marat M. and Yu, Zhuoran and {\v Z}abokrtsk{\'y}, Zden{\v e}k and Zeldes, Amir and Zhang, Manying and Zhu, Hanzhi}, url={http://hdl.handle.net/11234/1-3105}, note={{LINDAT}/{CLARIAH}-{CZ} digital library at the Institute of Formal and Applied Linguistics ({{\'U}FAL}), Faculty of Mathematics and Physics, Charles University}, copyright={Licence Universal Dependencies v2.5}, year={2019} } @article{Sang2003IntroductionTT, title={Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition}, author={Erik F. Tjong Kim Sang and Fien De Meulder}, journal={ArXiv}, year={2003}, volume={cs.CL/0306050} } @article{Sang2002IntroductionTT, title={Introduction to the CoNLL-2002 Shared Task: Language-Independent Named Entity Recognition}, author={Erik F. Tjong Kim Sang}, journal={ArXiv}, year={2002}, volume={cs.CL/0209010} } @inproceedings{Conneau2018XNLIEC, title={XNLI: Evaluating Cross-lingual Sentence Representations}, author={Alexis Conneau and Guillaume Lample and Ruty Rinott and Adina Williams and Samuel R. Bowman and Holger Schwenk and Veselin Stoyanov}, booktitle={EMNLP}, year={2018} } @article{Lewis2019MLQAEC, title={MLQA: Evaluating Cross-lingual Extractive Question Answering}, author={Patrick Lewis and Barlas Oguz and Ruty Rinott and Sebastian Riedel and Holger Schwenk}, journal={ArXiv}, year={2019}, volume={abs/1910.07475} } @article{Yang2019PAWSXAC, title={PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification}, author={Yinfei Yang and Yuan Zhang and Chris Tar and Jason Baldridge}, journal={ArXiv}, year={2019}, volume={abs/1908.11828} } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

xglue

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced", "expert-generated", "found", "machine-generated"], "language_creators": ["crowdsourced", "expert-generated", "found", "machine-generated"], "language": ["ar", "bg", "de", "el", "en", "es", "fr", "hi", "it", "nl", "pl", "pt", "ru", "sw", "th", "tr", "ur", "vi", "zh"], "license": ["other"], "multilinguality": ["multilingual", "translation"], "size_categories": ["100K<n<1M", "10K<n<100K"], "source_datasets": ["extended|conll2003", "extended|squad", "extended|xnli", "original"], "task_categories": ["question-answering", "summarization", "text-classification", "text2text-generation", "token-classification"], "task_ids": ["acceptability-classification", "extractive-qa", "named-entity-recognition", "natural-language-inference", "news-articles-headline-generation", "open-domain-qa", "parsing", "topic-classification"], "pretty_name": "XGLUE", "config_names": ["mlqa", "nc", "ner", "ntg", "paws-x", "pos", "qadsm", "qam", "qg", "wpr", "xnli"], "license_details": "Licence Universal Dependencies v2.5", "tags": ["paraphrase-identification", "question-answering"], "dataset_info": [{"config_name": "ner", "features": [{"name": "words", "sequence": "string"}, {"name": "ner", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC", "7": "B-MISC", "8": "I-MISC"}}}}], "splits": [{"name": "train", "num_bytes": 3445854, "num_examples": 14042}, {"name": "validation.en", "num_bytes": 866569, "num_examples": 3252}, {"name": "validation.de", "num_bytes": 917967, "num_examples": 2874}, {"name": "validation.es", "num_bytes": 888551, "num_examples": 1923}, {"name": "validation.nl", "num_bytes": 659144, "num_examples": 2895}, {"name": "test.en", "num_bytes": 784976, "num_examples": 3454}, {"name": "test.de", "num_bytes": 922741, "num_examples": 3007}, {"name": "test.es", "num_bytes": 864804, "num_examples": 1523}, {"name": "test.nl", "num_bytes": 1196660, "num_examples": 5202}], "download_size": 875905871, "dataset_size": 10547266}, {"config_name": "pos", "features": [{"name": "words", "sequence": "string"}, {"name": "pos", "sequence": {"class_label": {"names": {"0": "ADJ", "1": "ADP", "2": "ADV", "3": "AUX", "4": "CCONJ", "5": "DET", "6": "INTJ", "7": "NOUN", "8": "NUM", "9": "PART", "10": "PRON", "11": "PROPN", "12": "PUNCT", "13": "SCONJ", "14": "SYM", "15": "VERB", "16": "X"}}}}], "splits": [{"name": "train", "num_bytes": 7279459, "num_examples": 25376}, {"name": "validation.en", "num_bytes": 421410, "num_examples": 2001}, {"name": "validation.de", "num_bytes": 219328, "num_examples": 798}, {"name": "validation.es", "num_bytes": 620491, "num_examples": 1399}, {"name": "validation.nl", "num_bytes": 198003, "num_examples": 717}, {"name": "validation.bg", "num_bytes": 346802, "num_examples": 1114}, {"name": "validation.el", "num_bytes": 229447, "num_examples": 402}, {"name": "validation.fr", "num_bytes": 600964, "num_examples": 1475}, {"name": "validation.pl", "num_bytes": 620694, "num_examples": 2214}, {"name": "validation.tr", "num_bytes": 186196, "num_examples": 987}, {"name": "validation.vi", "num_bytes": 203669, "num_examples": 799}, {"name": "validation.zh", "num_bytes": 212579, "num_examples": 499}, {"name": "validation.ur", "num_bytes": 284016, "num_examples": 551}, {"name": "validation.hi", "num_bytes": 838700, "num_examples": 1658}, {"name": "validation.it", "num_bytes": 198608, "num_examples": 563}, {"name": "validation.ar", "num_bytes": 592943, "num_examples": 908}, {"name": "validation.ru", "num_bytes": 261563, "num_examples": 578}, {"name": "validation.th", "num_bytes": 272834, "num_examples": 497}, {"name": "test.en", "num_bytes": 420613, "num_examples": 2076}, {"name": "test.de", "num_bytes": 291759, "num_examples": 976}, {"name": "test.es", "num_bytes": 200003, "num_examples": 425}, {"name": "test.nl", "num_bytes": 193337, "num_examples": 595}, {"name": "test.bg", "num_bytes": 339460, "num_examples": 1115}, {"name": "test.el", "num_bytes": 235137, "num_examples": 455}, {"name": "test.fr", "num_bytes": 166865, "num_examples": 415}, {"name": "test.pl", "num_bytes": 600534, "num_examples": 2214}, {"name": "test.tr", "num_bytes": 186519, "num_examples": 982}, {"name": "test.vi", "num_bytes": 211408, "num_examples": 799}, {"name": "test.zh", "num_bytes": 202055, "num_examples": 499}, {"name": "test.ur", "num_bytes": 288189, "num_examples": 534}, {"name": "test.hi", "num_bytes": 839659, "num_examples": 1683}, {"name": "test.it", "num_bytes": 173861, "num_examples": 481}, {"name": "test.ar", "num_bytes": 561709, "num_examples": 679}, {"name": "test.ru", "num_bytes": 255393, "num_examples": 600}, {"name": "test.th", "num_bytes": 272834, "num_examples": 497}], "download_size": 875905871, "dataset_size": 19027041}, {"config_name": "mlqa", "features": [{"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 75307933, "num_examples": 87599}, {"name": "validation.en", "num_bytes": 1255587, "num_examples": 1148}, {"name": "validation.de", "num_bytes": 454258, "num_examples": 512}, {"name": "validation.ar", "num_bytes": 785493, "num_examples": 517}, {"name": "validation.es", "num_bytes": 388625, "num_examples": 500}, {"name": "validation.hi", "num_bytes": 1092167, "num_examples": 507}, {"name": "validation.vi", "num_bytes": 692227, "num_examples": 511}, {"name": "validation.zh", "num_bytes": 411213, "num_examples": 504}, {"name": "test.en", "num_bytes": 13264513, "num_examples": 11590}, {"name": "test.de", "num_bytes": 4070659, "num_examples": 4517}, {"name": "test.ar", "num_bytes": 7976090, "num_examples": 5335}, {"name": "test.es", "num_bytes": 4044224, "num_examples": 5253}, {"name": "test.hi", "num_bytes": 11385051, "num_examples": 4918}, {"name": "test.vi", "num_bytes": 7559078, "num_examples": 5495}, {"name": "test.zh", "num_bytes": 4092921, "num_examples": 5137}], "download_size": 875905871, "dataset_size": 132780039}, {"config_name": "nc", "features": [{"name": "news_title", "dtype": "string"}, {"name": "news_body", "dtype": "string"}, {"name": "news_category", "dtype": {"class_label": {"names": {"0": "foodanddrink", "1": "sports", "2": "travel", "3": "finance", "4": "lifestyle", "5": "news", "6": "entertainment", "7": "health", "8": "video", "9": "autos"}}}}], "splits": [{"name": "train", "num_bytes": 280615806, "num_examples": 100000}, {"name": "validation.en", "num_bytes": 33389140, "num_examples": 10000}, {"name": "validation.de", "num_bytes": 26757254, "num_examples": 10000}, {"name": "validation.es", "num_bytes": 31781308, "num_examples": 10000}, {"name": "validation.fr", "num_bytes": 27154099, "num_examples": 10000}, {"name": "validation.ru", "num_bytes": 46053007, "num_examples": 10000}, {"name": "test.en", "num_bytes": 34437987, "num_examples": 10000}, {"name": "test.de", "num_bytes": 26632007, "num_examples": 10000}, {"name": "test.es", "num_bytes": 31350078, "num_examples": 10000}, {"name": "test.fr", "num_bytes": 27589545, "num_examples": 10000}, {"name": "test.ru", "num_bytes": 46183830, "num_examples": 10000}], "download_size": 875905871, "dataset_size": 611944061}, {"config_name": "xnli", "features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 74444346, "num_examples": 392702}, {"name": "validation.en", "num_bytes": 433471, "num_examples": 2490}, {"name": "validation.ar", "num_bytes": 633009, "num_examples": 2490}, {"name": "validation.bg", "num_bytes": 774069, "num_examples": 2490}, {"name": "validation.de", "num_bytes": 494612, "num_examples": 2490}, {"name": "validation.el", "num_bytes": 841234, "num_examples": 2490}, {"name": "validation.es", "num_bytes": 478430, "num_examples": 2490}, {"name": "validation.fr", "num_bytes": 510112, "num_examples": 2490}, {"name": "validation.hi", "num_bytes": 1023923, "num_examples": 2490}, {"name": "validation.ru", "num_bytes": 786450, "num_examples": 2490}, {"name": "validation.sw", "num_bytes": 429858, "num_examples": 2490}, {"name": "validation.th", "num_bytes": 1061168, "num_examples": 2490}, {"name": "validation.tr", "num_bytes": 459316, "num_examples": 2490}, {"name": "validation.ur", "num_bytes": 699960, "num_examples": 2490}, {"name": "validation.vi", "num_bytes": 590688, "num_examples": 2490}, {"name": "validation.zh", "num_bytes": 384859, "num_examples": 2490}, {"name": "test.en", "num_bytes": 875142, "num_examples": 5010}, {"name": "test.ar", "num_bytes": 1294561, "num_examples": 5010}, {"name": "test.bg", "num_bytes": 1573042, "num_examples": 5010}, {"name": "test.de", "num_bytes": 996487, "num_examples": 5010}, {"name": "test.el", "num_bytes": 1704793, "num_examples": 5010}, {"name": "test.es", "num_bytes": 969821, "num_examples": 5010}, {"name": "test.fr", "num_bytes": 1029247, "num_examples": 5010}, {"name": "test.hi", "num_bytes": 2073081, "num_examples": 5010}, {"name": "test.ru", "num_bytes": 1603474, "num_examples": 5010}, {"name": "test.sw", "num_bytes": 871659, "num_examples": 5010}, {"name": "test.th", "num_bytes": 2147023, "num_examples": 5010}, {"name": "test.tr", "num_bytes": 934942, "num_examples": 5010}, {"name": "test.ur", "num_bytes": 1416246, "num_examples": 5010}, {"name": "test.vi", "num_bytes": 1190225, "num_examples": 5010}, {"name": "test.zh", "num_bytes": 777937, "num_examples": 5010}], "download_size": 875905871, "dataset_size": 103503185}, {"config_name": "paws-x", "features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "different", "1": "same"}}}}], "splits": [{"name": "train", "num_bytes": 12018349, "num_examples": 49401}, {"name": "validation.en", "num_bytes": 484287, "num_examples": 2000}, {"name": "validation.de", "num_bytes": 506009, "num_examples": 2000}, {"name": "validation.es", "num_bytes": 505888, "num_examples": 2000}, {"name": "validation.fr", "num_bytes": 525031, "num_examples": 2000}, {"name": "test.en", "num_bytes": 486734, "num_examples": 2000}, {"name": "test.de", "num_bytes": 516214, "num_examples": 2000}, {"name": "test.es", "num_bytes": 511111, "num_examples": 2000}, {"name": "test.fr", "num_bytes": 527101, "num_examples": 2000}], "download_size": 875905871, "dataset_size": 16080724}, {"config_name": "qadsm", "features": [{"name": "query", "dtype": "string"}, {"name": "ad_title", "dtype": "string"}, {"name": "ad_description", "dtype": "string"}, {"name": "relevance_label", "dtype": {"class_label": {"names": {"0": "Bad", "1": "Good"}}}}], "splits": [{"name": "train", "num_bytes": 12528141, "num_examples": 100000}, {"name": "validation.en", "num_bytes": 1248839, "num_examples": 10000}, {"name": "validation.de", "num_bytes": 1566011, "num_examples": 10000}, {"name": "validation.fr", "num_bytes": 1651804, "num_examples": 10000}, {"name": "test.en", "num_bytes": 1236997, "num_examples": 10000}, {"name": "test.de", "num_bytes": 1563985, "num_examples": 10000}, {"name": "test.fr", "num_bytes": 1594118, "num_examples": 10000}], "download_size": 875905871, "dataset_size": 21389895}, {"config_name": "wpr", "features": [{"name": "query", "dtype": "string"}, {"name": "web_page_title", "dtype": "string"}, {"name": "web_page_snippet", "dtype": "string"}, {"name": "relavance_label", "dtype": {"class_label": {"names": {"0": "Bad", "1": "Fair", "2": "Good", "3": "Excellent", "4": "Perfect"}}}}], "splits": [{"name": "train", "num_bytes": 33885931, "num_examples": 99997}, {"name": "validation.en", "num_bytes": 3417760, "num_examples": 10008}, {"name": "validation.de", "num_bytes": 2929029, "num_examples": 10004}, {"name": "validation.es", "num_bytes": 2451026, "num_examples": 10004}, {"name": "validation.fr", "num_bytes": 3055899, "num_examples": 10005}, {"name": "validation.it", "num_bytes": 2416388, "num_examples": 10003}, {"name": "validation.pt", "num_bytes": 2449797, "num_examples": 10001}, {"name": "validation.zh", "num_bytes": 3118577, "num_examples": 10002}, {"name": "test.en", "num_bytes": 3402487, "num_examples": 10004}, {"name": "test.de", "num_bytes": 2923577, "num_examples": 9997}, {"name": "test.es", "num_bytes": 2422895, "num_examples": 10006}, {"name": "test.fr", "num_bytes": 3059392, "num_examples": 10020}, {"name": "test.it", "num_bytes": 2403736, "num_examples": 10001}, {"name": "test.pt", "num_bytes": 2462350, "num_examples": 10015}, {"name": "test.zh", "num_bytes": 3141598, "num_examples": 9999}], "download_size": 875905871, "dataset_size": 73540442}, {"config_name": "qam", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "False", "1": "True"}}}}], "splits": [{"name": "train", "num_bytes": 28357964, "num_examples": 100000}, {"name": "validation.en", "num_bytes": 3085501, "num_examples": 10000}, {"name": "validation.de", "num_bytes": 3304031, "num_examples": 10000}, {"name": "validation.fr", "num_bytes": 3142833, "num_examples": 10000}, {"name": "test.en", "num_bytes": 3082297, "num_examples": 10000}, {"name": "test.de", "num_bytes": 3309496, "num_examples": 10000}, {"name": "test.fr", "num_bytes": 3140213, "num_examples": 10000}], "download_size": 875905871, "dataset_size": 47422335}, {"config_name": "qg", "features": [{"name": "answer_passage", "dtype": "string"}, {"name": "question", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 27464034, "num_examples": 100000}, {"name": "validation.en", "num_bytes": 3047040, "num_examples": 10000}, {"name": "validation.de", "num_bytes": 3270877, "num_examples": 10000}, {"name": "validation.es", "num_bytes": 3341775, "num_examples": 10000}, {"name": "validation.fr", "num_bytes": 3175615, "num_examples": 10000}, {"name": "validation.it", "num_bytes": 3191193, "num_examples": 10000}, {"name": "validation.pt", "num_bytes": 3328434, "num_examples": 10000}, {"name": "test.en", "num_bytes": 3043813, "num_examples": 10000}, {"name": "test.de", "num_bytes": 3270190, "num_examples": 10000}, {"name": "test.es", "num_bytes": 3353522, "num_examples": 10000}, {"name": "test.fr", "num_bytes": 3178352, "num_examples": 10000}, {"name": "test.it", "num_bytes": 3195684, "num_examples": 10000}, {"name": "test.pt", "num_bytes": 3340296, "num_examples": 10000}], "download_size": 875905871, "dataset_size": 66200825}, {"config_name": "ntg", "features": [{"name": "news_body", "dtype": "string"}, {"name": "news_title", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 890709581, "num_examples": 300000}, {"name": "validation.en", "num_bytes": 34317076, "num_examples": 10000}, {"name": "validation.de", "num_bytes": 27404379, "num_examples": 10000}, {"name": "validation.es", "num_bytes": 30896109, "num_examples": 10000}, {"name": "validation.fr", "num_bytes": 27261523, "num_examples": 10000}, {"name": "validation.ru", "num_bytes": 43247386, "num_examples": 10000}, {"name": "test.en", "num_bytes": 33697284, "num_examples": 10000}, {"name": "test.de", "num_bytes": 26738202, "num_examples": 10000}, {"name": "test.es", "num_bytes": 31111489, "num_examples": 10000}, {"name": "test.fr", "num_bytes": 26997447, "num_examples": 10000}, {"name": "test.ru", "num_bytes": 44050350, "num_examples": 10000}], "download_size": 875905871, "dataset_size": 1216430826}]}

2023-06-30T08:06:30+00:00

TAGS #task_categories-question-answering #task_categories-summarization #task_categories-text-classification #task_categories-text2text-generation #task_categories-token-classification #task_ids-acceptability-classification #task_ids-extractive-qa #task_ids-named-entity-recognition #task_ids-natural-language-inference #task_ids-news-articles-headline-generation #task_ids-open-domain-qa #task_ids-parsing #task_ids-topic-classification #annotations_creators-crowdsourced #annotations_creators-expert-generated #annotations_creators-found #annotations_creators-machine-generated #language_creators-crowdsourced #language_creators-expert-generated #language_creators-found #language_creators-machine-generated #multilinguality-multilingual #multilinguality-translation #size_categories-100K<n<1M #size_categories-10K<n<100K #source_datasets-extended|conll2003 #source_datasets-extended|squad #source_datasets-extended|xnli #source_datasets-original #language-Arabic #language-Bulgarian #language-German #language-Modern Greek (1453-) #language-English #language-Spanish #language-French #language-Hindi #language-Italian #language-Dutch #language-Polish #language-Portuguese #language-Russian #language-Swahili (macrolanguage) #language-Thai #language-Turkish #language-Urdu #language-Vietnamese #language-Chinese #license-other #paraphrase-identification #question-answering #arxiv-2004.01401 #region-us

Dataset Card for XGLUE ====================== Table of Contents ----------------- * Table of Contents * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: XGLUE homepage * Paper: XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation * Point of Contact: xglue@URL ### Dataset Summary XGLUE is a new benchmark dataset to evaluate the performance of cross-lingual pre-trained models with respect to cross-lingual natural language understanding and generation. XGLUE is composed of 11 tasks spans 19 languages. For each task, the training data is only available in English. This means that to succeed at XGLUE, a model must have a strong zero-shot cross-lingual transfer capability to learn from the English data of a specific task and transfer what it learned to other languages. Comparing to its concurrent work XTREME, XGLUE has two characteristics: First, it includes cross-lingual NLU and cross-lingual NLG tasks at the same time; Second, besides including 5 existing cross-lingual tasks (i.e. NER, POS, MLQA, PAWS-X and XNLI), XGLUE selects 6 new tasks from Bing scenarios as well, including News Classification (NC), Query-Ad Matching (QADSM), Web Page Ranking (WPR), QA Matching (QAM), Question Generation (QG) and News Title Generation (NTG). Such diversities of languages, tasks and task origin provide a comprehensive benchmark for quantifying the quality of a pre-trained model on cross-lingual natural language understanding and generation. The training data of each task is in English while the validation and test data is present in multiple different languages. The following table shows which languages are present as validation and test data for each config. !Available Languages for Test and Validation Data Therefore, for each config, a cross-lingual pre-trained model should be fine-tuned on the English training data, and evaluated on for all languages. ### Supported Tasks and Leaderboards The XGLUE leaderboard can be found on the homepage and consists of a XGLUE-Understanding Score (the average of the tasks 'ner', 'pos', 'mlqa', 'nc', 'xnli', 'paws-x', 'qadsm', 'wpr', 'qam') and a XGLUE-Generation Score (the average of the tasks 'qg', 'ntg'). ### Languages For all tasks (configurations), the "train" split is in English ('en'). For each task, the "validation" and "test" splits are present in these languages: * ner: 'en', 'de', 'es', 'nl' * pos: 'en', 'de', 'es', 'nl', 'bg', 'el', 'fr', 'pl', 'tr', 'vi', 'zh', 'ur', 'hi', 'it', 'ar', 'ru', 'th' * mlqa: 'en', 'de', 'ar', 'es', 'hi', 'vi', 'zh' * nc: 'en', 'de', 'es', 'fr', 'ru' * xnli: 'en', 'ar', 'bg', 'de', 'el', 'es', 'fr', 'hi', 'ru', 'sw', 'th', 'tr', 'ur', 'vi', 'zh' * paws-x: 'en', 'de', 'es', 'fr' * qadsm: 'en', 'de', 'fr' * wpr: 'en', 'de', 'es', 'fr', 'it', 'pt', 'zh' * qam: 'en', 'de', 'fr' * qg: 'en', 'de', 'es', 'fr', 'it', 'pt' * ntg: 'en', 'de', 'es', 'fr', 'ru' Dataset Structure ----------------- ### Data Instances #### ner An example of 'URL' looks as follows. #### pos An example of 'URL' looks as follows. #### mlqa An example of 'URL' looks as follows. #### nc An example of 'URL' looks as follows. #### xnli An example of 'URL' looks as follows. #### paws-x An example of 'URL' looks as follows. #### qadsm An example of 'train' looks as follows. #### wpr An example of 'URL' looks as follows. #### qam An example of 'URL' looks as follows. #### qg An example of 'URL' looks as follows. #### ntg An example of 'URL' looks as follows. ### Data Fields #### ner In the following each data field in ner is explained. The data fields are the same among all splits. * 'words': a list of words composing the sentence. * 'ner': a list of entitity classes corresponding to each word respectively. #### pos In the following each data field in pos is explained. The data fields are the same among all splits. * 'words': a list of words composing the sentence. * 'pos': a list of "part-of-speech" classes corresponding to each word respectively. #### mlqa In the following each data field in mlqa is explained. The data fields are the same among all splits. * 'context': a string, the context containing the answer. * 'question': a string, the question to be answered. * 'answers': a string, the answer to 'question'. #### nc In the following each data field in nc is explained. The data fields are the same among all splits. * 'news\_title': a string, to the title of the news report. * 'news\_body': a string, to the actual news report. * 'news\_category': a string, the category of the news report, *e.g.* 'foodanddrink' #### xnli In the following each data field in xnli is explained. The data fields are the same among all splits. * 'premise': a string, the context/premise, *i.e.* the first sentence for natural language inference. * 'hypothesis': a string, a sentence whereas its relation to 'premise' is to be classified, *i.e.* the second sentence for natural language inference. * 'label': a class catory (int), natural language inference relation class between 'hypothesis' and 'premise'. One of 0: entailment, 1: contradiction, 2: neutral. #### paws-x In the following each data field in paws-x is explained. The data fields are the same among all splits. * 'sentence1': a string, a sentence. * 'sentence2': a string, a sentence whereas the sentence is either a paraphrase of 'sentence1' or not. * 'label': a class label (int), whether 'sentence2' is a paraphrase of 'sentence1' One of 0: different, 1: same. #### qadsm In the following each data field in qadsm is explained. The data fields are the same among all splits. * 'query': a string, the search query one would insert into a search engine. * 'ad\_title': a string, the title of the advertisement. * 'ad\_description': a string, the content of the advertisement, *i.e.* the main body. * 'relevance\_label': a class label (int), how relevant the advertisement 'ad\_title' + 'ad\_description' is to the search query 'query'. One of 0: Bad, 1: Good. #### wpr In the following each data field in wpr is explained. The data fields are the same among all splits. * 'query': a string, the search query one would insert into a search engine. * 'web\_page\_title': a string, the title of a web page. * 'web\_page\_snippet': a string, the content of a web page, *i.e.* the main body. * 'relavance\_label': a class label (int), how relevant the web page 'web\_page\_snippet' + 'web\_page\_snippet' is to the search query 'query'. One of 0: Bad, 1: Fair, 2: Good, 3: Excellent, 4: Perfect. #### qam In the following each data field in qam is explained. The data fields are the same among all splits. * 'question': a string, a question. * 'answer': a string, a possible answer to 'question'. * 'label': a class label (int), whether the 'answer' is relevant to the 'question'. One of 0: False, 1: True. #### qg In the following each data field in qg is explained. The data fields are the same among all splits. * 'answer\_passage': a string, a detailed answer to the 'question'. * 'question': a string, a question. #### ntg In the following each data field in ntg is explained. The data fields are the same among all splits. * 'news\_body': a string, the content of a news article. * 'news\_title': a string, the title corresponding to the news article 'news\_body'. ### Data Splits #### ner The following table shows the number of data samples/number of rows for each split in ner. #### pos The following table shows the number of data samples/number of rows for each split in pos. #### mlqa The following table shows the number of data samples/number of rows for each split in mlqa. #### nc The following table shows the number of data samples/number of rows for each split in nc. #### xnli The following table shows the number of data samples/number of rows for each split in xnli. #### nc The following table shows the number of data samples/number of rows for each split in nc. #### xnli The following table shows the number of data samples/number of rows for each split in xnli. #### paws-x The following table shows the number of data samples/number of rows for each split in paws-x. #### qadsm The following table shows the number of data samples/number of rows for each split in qadsm. #### wpr The following table shows the number of data samples/number of rows for each split in wpr. #### qam The following table shows the number of data samples/number of rows for each split in qam. #### qg The following table shows the number of data samples/number of rows for each split in qg. #### ntg The following table shows the number of data samples/number of rows for each split in ntg. Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators The dataset is maintained mainly by Yaobo Liang, Yeyun Gong, Nan Duan, Ming Gong, Linjun Shou, and Daniel Campos from Microsoft Research. ### Licensing Information The XGLUE datasets are intended for non-commercial research purposes only to promote advancement in the field of artificial intelligence and related areas, and is made available free of charge without extending any license or other intellectual property rights. The dataset is provided “as is” without warranty and usage of the data has risks since we may not own the underlying rights in the documents. We are not be liable for any damages related to use of the dataset. Feedback is voluntarily given and can be used as we see fit. Upon violation of any of these terms, your rights to use the dataset will end automatically. If you have questions about use of the dataset or any research outputs in your products or services, we encourage you to undertake your own independent legal review. For other questions, please feel free to contact us. If you use this dataset, please cite it. Additionally, since XGLUE is also built out of exiting 5 datasets, please ensure you cite all of them. An example: ### Contributions Thanks to @patrickvonplaten for adding this dataset.

b8dd5d7af51114dbda02c0e3f6133f332186418e

# Dataset Card for "xnli" ## 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/xnli/](https://www.nyu.edu/projects/bowman/xnli/) - **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:** 7.74 GB - **Size of the generated dataset:** 3.23 GB - **Total amount of disk used:** 10.97 GB ### Dataset Summary XNLI is a subset of a few thousand examples from MNLI which has been translated into a 14 different languages (some low-ish resource). As with MNLI, the goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B) and is a classification task (given two sentences, predict one of three labels). ### 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 #### all_languages - **Size of downloaded dataset files:** 483.96 MB - **Size of the generated dataset:** 1.61 GB - **Total amount of disk used:** 2.09 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "hypothesis": "{\"language\": [\"ar\", \"bg\", \"de\", \"el\", \"en\", \"es\", \"fr\", \"hi\", \"ru\", \"sw\", \"th\", \"tr\", \"ur\", \"vi\", \"zh\"], \"translation\": [\"احد اع...", "label": 0, "premise": "{\"ar\": \"واحدة من رقابنا ستقوم بتنفيذ تعليماتك كلها بكل دقة\", \"bg\": \"един от нашите номера ще ви даде инструкции .\", \"de\": \"Eine ..." } ``` #### ar - **Size of downloaded dataset files:** 483.96 MB - **Size of the generated dataset:** 109.32 MB - **Total amount of disk used:** 593.29 MB An example of 'validation' looks as follows. ``` { "hypothesis": "اتصل بأمه حالما أوصلته حافلة المدرسية.", "label": 1, "premise": "وقال، ماما، لقد عدت للمنزل." } ``` #### bg - **Size of downloaded dataset files:** 483.96 MB - **Size of the generated dataset:** 128.32 MB - **Total amount of disk used:** 612.28 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "hypothesis": "\"губиш нещата на следното ниво , ако хората си припомнят .\"...", "label": 0, "premise": "\"по време на сезона и предполагам , че на твоето ниво ще ги загубиш на следващото ниво , ако те решат да си припомнят отбора на ..." } ``` #### de - **Size of downloaded dataset files:** 483.96 MB - **Size of the generated dataset:** 86.17 MB - **Total amount of disk used:** 570.14 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "hypothesis": "Man verliert die Dinge auf die folgende Ebene , wenn sich die Leute erinnern .", "label": 0, "premise": "\"Du weißt , während der Saison und ich schätze , auf deiner Ebene verlierst du sie auf die nächste Ebene , wenn sie sich entschl..." } ``` #### el - **Size of downloaded dataset files:** 483.96 MB - **Size of the generated dataset:** 142.30 MB - **Total amount of disk used:** 626.26 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "hypothesis": "\"Τηλεφώνησε στη μαμά του μόλις το σχολικό λεωφορείο τον άφησε.\"...", "label": 1, "premise": "Και είπε, Μαμά, έφτασα στο σπίτι." } ``` ### Data Fields The data fields are the same among all splits. #### all_languages - `premise`: a multilingual `string` variable, with possible languages including `ar`, `bg`, `de`, `el`, `en`. - `hypothesis`: a multilingual `string` variable, with possible languages including `ar`, `bg`, `de`, `el`, `en`. - `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2). #### ar - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2). #### bg - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2). #### de - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2). #### el - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2). ### Data Splits | name |train |validation|test| |-------------|-----:|---------:|---:| |all_languages|392702| 2490|5010| |ar |392702| 2490|5010| |bg |392702| 2490|5010| |de |392702| 2490|5010| |el |392702| 2490|5010| ## 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{conneau2018xnli, author = {Conneau, Alexis and Rinott, Ruty and Lample, Guillaume and Williams, Adina and Bowman, Samuel R. and Schwenk, Holger and Stoyanov, Veselin}, title = {XNLI: Evaluating Cross-lingual Sentence Representations}, booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing}, year = {2018}, publisher = {Association for Computational Linguistics}, location = {Brussels, Belgium}, } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

xnli

2022-03-02T23:29:22+00:00

{"language": ["ar", "bg", "de", "el", "en", "es", "fr", "hi", "ru", "sw", "th", "tr", "ur", "vi", "zh"], "paperswithcode_id": "xnli", "pretty_name": "Cross-lingual Natural Language Inference", "dataset_info": [{"config_name": "all_languages", "features": [{"name": "premise", "dtype": {"translation": {"languages": ["ar", "bg", "de", "el", "en", "es", "fr", "hi", "ru", "sw", "th", "tr", "ur", "vi", "zh"]}}}, {"name": "hypothesis", "dtype": {"translation_variable_languages": {"languages": ["ar", "bg", "de", "el", "en", "es", "fr", "hi", "ru", "sw", "th", "tr", "ur", "vi", "zh"], "num_languages": 15}}}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 1581471691, "num_examples": 392702}, {"name": "test", "num_bytes": 19387432, "num_examples": 5010}, {"name": "validation", "num_bytes": 9566179, "num_examples": 2490}], "download_size": 963942271, "dataset_size": 1610425302}, {"config_name": "ar", "features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 107399614, "num_examples": 392702}, {"name": "test", "num_bytes": 1294553, "num_examples": 5010}, {"name": "validation", "num_bytes": 633001, "num_examples": 2490}], "download_size": 59215902, "dataset_size": 109327168}, {"config_name": "bg", "features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 125973225, "num_examples": 392702}, {"name": "test", "num_bytes": 1573034, "num_examples": 5010}, {"name": "validation", "num_bytes": 774061, "num_examples": 2490}], "download_size": 66117878, "dataset_size": 128320320}, {"config_name": "de", "features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 84684140, "num_examples": 392702}, {"name": "test", "num_bytes": 996488, "num_examples": 5010}, {"name": "validation", "num_bytes": 494604, "num_examples": 2490}], "download_size": 55973883, "dataset_size": 86175232}, {"config_name": "el", "features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 139753358, "num_examples": 392702}, {"name": "test", "num_bytes": 1704785, "num_examples": 5010}, {"name": "validation", "num_bytes": 841226, "num_examples": 2490}], "download_size": 74551247, "dataset_size": 142299369}, {"config_name": "en", "features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 74444026, "num_examples": 392702}, {"name": "test", "num_bytes": 875134, "num_examples": 5010}, {"name": "validation", "num_bytes": 433463, "num_examples": 2490}], "download_size": 50627367, "dataset_size": 75752623}, {"config_name": "es", "features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 81383284, "num_examples": 392702}, {"name": "test", "num_bytes": 969813, "num_examples": 5010}, {"name": "validation", "num_bytes": 478422, "num_examples": 2490}], "download_size": 53677157, "dataset_size": 82831519}, {"config_name": "fr", "features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 85808779, "num_examples": 392702}, {"name": "test", "num_bytes": 1029239, "num_examples": 5010}, {"name": "validation", "num_bytes": 510104, "num_examples": 2490}], "download_size": 55968680, "dataset_size": 87348122}, {"config_name": "hi", "features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 170593964, "num_examples": 392702}, {"name": "test", "num_bytes": 2073073, "num_examples": 5010}, {"name": "validation", "num_bytes": 1023915, "num_examples": 2490}], "download_size": 70908548, "dataset_size": 173690952}, {"config_name": "ru", "features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 129859615, "num_examples": 392702}, {"name": "test", "num_bytes": 1603466, "num_examples": 5010}, {"name": "validation", "num_bytes": 786442, "num_examples": 2490}], "download_size": 70702606, "dataset_size": 132249523}, {"config_name": "sw", "features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 69285725, "num_examples": 392702}, {"name": "test", "num_bytes": 871651, "num_examples": 5010}, {"name": "validation", "num_bytes": 429850, "num_examples": 2490}], "download_size": 45564152, "dataset_size": 70587226}, {"config_name": "th", "features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 176062892, "num_examples": 392702}, {"name": "test", "num_bytes": 2147015, "num_examples": 5010}, {"name": "validation", "num_bytes": 1061160, "num_examples": 2490}], "download_size": 77222045, "dataset_size": 179271067}, {"config_name": "tr", "features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 71637140, "num_examples": 392702}, {"name": "test", "num_bytes": 934934, "num_examples": 5010}, {"name": "validation", "num_bytes": 459308, "num_examples": 2490}], "download_size": 48509680, "dataset_size": 73031382}, {"config_name": "ur", "features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 96441486, "num_examples": 392702}, {"name": "test", "num_bytes": 1416241, "num_examples": 5010}, {"name": "validation", "num_bytes": 699952, "num_examples": 2490}], "download_size": 46682785, "dataset_size": 98557679}, {"config_name": "vi", "features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 101417430, "num_examples": 392702}, {"name": "test", "num_bytes": 1190217, "num_examples": 5010}, {"name": "validation", "num_bytes": 590680, "num_examples": 2490}], "download_size": 57690058, "dataset_size": 103198327}, {"config_name": "zh", "features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 72224841, "num_examples": 392702}, {"name": "test", "num_bytes": 777929, "num_examples": 5010}, {"name": "validation", "num_bytes": 384851, "num_examples": 2490}], "download_size": 48269855, "dataset_size": 73387621}], "configs": [{"config_name": "all_languages", "data_files": [{"split": "train", "path": "all_languages/train-*"}, {"split": "test", "path": "all_languages/test-*"}, {"split": "validation", "path": "all_languages/validation-*"}]}, {"config_name": "ar", "data_files": [{"split": "train", "path": "ar/train-*"}, {"split": "test", "path": "ar/test-*"}, {"split": "validation", "path": "ar/validation-*"}]}, {"config_name": "bg", "data_files": [{"split": "train", "path": "bg/train-*"}, {"split": "test", "path": "bg/test-*"}, {"split": "validation", "path": "bg/validation-*"}]}, {"config_name": "de", "data_files": [{"split": "train", "path": "de/train-*"}, {"split": "test", "path": "de/test-*"}, {"split": "validation", "path": "de/validation-*"}]}, {"config_name": "el", "data_files": [{"split": "train", "path": "el/train-*"}, {"split": "test", "path": "el/test-*"}, {"split": "validation", "path": "el/validation-*"}]}, {"config_name": "en", "data_files": [{"split": "train", "path": "en/train-*"}, {"split": "test", "path": "en/test-*"}, {"split": "validation", "path": "en/validation-*"}]}, {"config_name": "es", "data_files": [{"split": "train", "path": "es/train-*"}, {"split": "test", "path": "es/test-*"}, {"split": "validation", "path": "es/validation-*"}]}, {"config_name": "fr", "data_files": [{"split": "train", "path": "fr/train-*"}, {"split": "test", "path": "fr/test-*"}, {"split": "validation", "path": "fr/validation-*"}]}, {"config_name": "hi", "data_files": [{"split": "train", "path": "hi/train-*"}, {"split": "test", "path": "hi/test-*"}, {"split": "validation", "path": "hi/validation-*"}]}, {"config_name": "ru", "data_files": [{"split": "train", "path": "ru/train-*"}, {"split": "test", "path": "ru/test-*"}, {"split": "validation", "path": "ru/validation-*"}]}, {"config_name": "sw", "data_files": [{"split": "train", "path": "sw/train-*"}, {"split": "test", "path": "sw/test-*"}, {"split": "validation", "path": "sw/validation-*"}]}, {"config_name": "th", "data_files": [{"split": "train", "path": "th/train-*"}, {"split": "test", "path": "th/test-*"}, {"split": "validation", "path": "th/validation-*"}]}, {"config_name": "tr", "data_files": [{"split": "train", "path": "tr/train-*"}, {"split": "test", "path": "tr/test-*"}, {"split": "validation", "path": "tr/validation-*"}]}, {"config_name": "ur", "data_files": [{"split": "train", "path": "ur/train-*"}, {"split": "test", "path": "ur/test-*"}, {"split": "validation", "path": "ur/validation-*"}]}, {"config_name": "vi", "data_files": [{"split": "train", "path": "vi/train-*"}, {"split": "test", "path": "vi/test-*"}, {"split": "validation", "path": "vi/validation-*"}]}, {"config_name": "zh", "data_files": [{"split": "train", "path": "zh/train-*"}, {"split": "test", "path": "zh/test-*"}, {"split": "validation", "path": "zh/validation-*"}]}]}

2024-01-05T08:30:52+00:00

TAGS #language-Arabic #language-Bulgarian #language-German #language-Modern Greek (1453-) #language-English #language-Spanish #language-French #language-Hindi #language-Russian #language-Swahili (macrolanguage) #language-Thai #language-Turkish #language-Urdu #language-Vietnamese #language-Chinese #region-us

Dataset Card for "xnli" ======================= Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: * Point of Contact: * Size of downloaded dataset files: 7.74 GB * Size of the generated dataset: 3.23 GB * Total amount of disk used: 10.97 GB ### Dataset Summary XNLI is a subset of a few thousand examples from MNLI which has been translated into a 14 different languages (some low-ish resource). As with MNLI, the goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B) and is a classification task (given two sentences, predict one of three labels). ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### all\_languages * Size of downloaded dataset files: 483.96 MB * Size of the generated dataset: 1.61 GB * Total amount of disk used: 2.09 GB An example of 'train' looks as follows. #### ar * Size of downloaded dataset files: 483.96 MB * Size of the generated dataset: 109.32 MB * Total amount of disk used: 593.29 MB An example of 'validation' looks as follows. #### bg * Size of downloaded dataset files: 483.96 MB * Size of the generated dataset: 128.32 MB * Total amount of disk used: 612.28 MB An example of 'train' looks as follows. #### de * Size of downloaded dataset files: 483.96 MB * Size of the generated dataset: 86.17 MB * Total amount of disk used: 570.14 MB An example of 'train' looks as follows. #### el * Size of downloaded dataset files: 483.96 MB * Size of the generated dataset: 142.30 MB * Total amount of disk used: 626.26 MB An example of 'validation' looks as follows. ### Data Fields The data fields are the same among all splits. #### all\_languages * 'premise': a multilingual 'string' variable, with possible languages including 'ar', 'bg', 'de', 'el', 'en'. * 'hypothesis': a multilingual 'string' variable, with possible languages including 'ar', 'bg', 'de', 'el', 'en'. * 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2). #### ar * 'premise': a 'string' feature. * 'hypothesis': a 'string' feature. * 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2). #### bg * 'premise': a 'string' feature. * 'hypothesis': a 'string' feature. * 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2). #### de * 'premise': a 'string' feature. * 'hypothesis': a 'string' feature. * 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2). #### el * 'premise': a 'string' feature. * 'hypothesis': a 'string' feature. * 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2). ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @lewtun, @mariamabarham, @thomwolf, @lhoestq, @patrickvonplaten for adding this dataset.

7089582c27fbbfda244f80bee17b3f1b5e8f28c3

# Dataset Card for XOR 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:** [XOR QA Homepage](https://nlp.cs.washington.edu/xorqa/) - **Repository:** [XOR QA Repository](https://github.com/AkariAsai/XORQA) - **Paper:** [XOR QA Paper](https://arxiv.org/abs/2010.11856) - **Leaderboard:** [XOR QA Leaderboard](https://nlp.cs.washington.edu/xorqa/) - **Point of Contact:** [Akari Asai](akari@cs.washington.edu) ### Dataset Summary XOR-TyDi QA brings together for the first time information-seeking questions, open-retrieval QA, and multilingual QA to create a multilingual open-retrieval QA dataset that enables cross-lingual answer retrieval. It consists of questions written by information-seeking native speakers in 7 typologically diverse languages and answer annotations that are retrieved from multilingual document collections. ### Supported Tasks and Leaderboards There are three sub-tasks: XOR-Retrieve, XOR-EnglishSpan, and XOR-Full. - `XOR-retrieve`: XOR-Retrieve is a cross-lingual retrieval task where a question is written in a target language (e.g., Japanese) and a system is required to retrieve English paragraphs that answer the question. The dataset can be used to train a model for cross-lingual retrieval. Success on this task is typically measured by R@5kt, R@2kt (the recall by computing the fraction of the questions for which the minimal answer is contained in the top 5,000 / 2,000 tokens selected). This task has an active leaderboard which can be found at [leaderboard url](https://nlp.cs.washington.edu/xorqa/) - `XOR-English Span`: XOR-English Span is a cross-lingual retrieval task where a question is written in a target language (e.g., Japanese) and a system is required to output a short answer in English. The dataset can be used to train a model for cross-lingual retrieval. Success on this task is typically measured by F1, EM. This task has an active leaderboard which can be found at [leaderboard url](https://nlp.cs.washington.edu/xorqa/) - `XOR-Full`: XOR-Full is a cross-lingual retrieval task where a question is written in the target language (e.g., Japanese) and a system is required to output a short answer in a target language. Success on this task is typically measured by F1, EM, BLEU This task has an active leaderboard which can be found at [leaderboard url](https://nlp.cs.washington.edu/xorqa/) ### Languages The text in the dataset is available in 7 languages: Arabic `ar`, Bengali `bn`, Finnish `fi`, Japanese `ja`, Korean `ko`, Russian `ru`, Telugu `te` ## Dataset Structure ### Data Instances A typical data point comprises a `question`, it's `answer` the `language` of the question text and the split to which it belongs. ``` { "id": "-3979399588609321314", "question": "Сколько детей было у Наполео́на I Бонапа́рта?", "answers": ["сын"], "lang": "ru", "split": "train" } ``` ### Data Fields - `id`: An identifier for each example in the dataset - `question`: Open domain question - `answers`: The corresponding answer to the question posed - `lang`: BCP-47 language tag - `split`: identifier to differentiate train, validation and test splits ### Data Splits The data is split into a training, validation and test set for each of the two configurations. | | train | validation | test | |--------------|------:|-----------:|-----:| | XOR Retrieve | 15250 | 2113 | 2501 | | XOR Full | 61360 | 3179 | 8177 | ## Dataset Creation ### Curation Rationale This task framework reflects well real-world scenarios where a QA system uses multilingual document collections and answers questions asked by users with diverse linguistic and cultural backgrounds. Despite the common assumption that we can find answers in the target language, web re- sources in non-English languages are largely lim- ited compared to English (information scarcity), or the contents are biased towards their own cul- tures (information asymmetry). To solve these issues, XOR-TYDI QA (Asai et al., 2020) provides a benchmark for developing a multilingual QA system that finds answers in multiple languages. ### Source Data annotation pipeline consists of four steps: 1) collection of realistic questions that require cross-lingual ref- erences by annotating questions from TYDI QA without a same-language answer; 2) question translation from a target language to the pivot language of English where the missing informa- tion may exist; 3) answer span selection in the pivot language given a set of candidate documents; 4) answer verification and translation from the pivot language back to the original language. #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? The Dataset is created by extending TyDiQA dataset and translating the questions into other languages. The answers are obtained by crowdsourcing the questions to Mechanical Turk workders ### Annotations #### Annotation process The English questions from TyDiQA are translated into other languages. The languages are chosen based on the availability of wikipedia data and the availability of tranlators. #### Who are the annotators? The translations are carried out using the professionla tranlation service (Gengo)[https://gengo.com] and the answers are annotated by MechanicalTurk workers ### Personal and Sensitive Information The dataset is created from wikipedia content and the QA task requires preserving the named entities, there by all the Wikipedia Named Entities are preserved in the data. Not much information has been provided about masking sensitive information. ## 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 people associated with the creation of the dataset are Akari Asai, Jungo Kasai, Jonathan H. Clark, Kenton Lee, Eunsol Choi, Hannaneh Hajishirzi ### Licensing Information XOR-TyDi QA is distributed under the [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode) license ### Citation Information ``` @article{xorqa, title = {XOR QA: Cross-lingual Open-Retrieval Question Answering}, author = {Akari Asai and Jungo Kasai and Jonathan H. Clark and Kenton Lee and Eunsol Choi and Hannaneh Hajishirzi} year = {2020} } ``` ### Contributions Thanks to [@sumanthd17](https://github.com/sumanthd17) for adding this dataset.

xor_tydi_qa

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced"], "language_creators": ["expert-generated", "found"], "language": ["ar", "bn", "fi", "ja", "ko", "ru", "te"], "license": ["mit"], "multilinguality": ["multilingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original", "extended|tydiqa"], "task_categories": ["question-answering"], "task_ids": ["open-domain-qa"], "paperswithcode_id": "xor-tydi-qa", "pretty_name": "XOR QA", "dataset_info": [{"config_name": "xor-retrieve", "features": [{"name": "question", "dtype": "string"}, {"name": "lang", "dtype": {"class_label": {"names": {"0": "ar", "1": "bn", "2": "fi", "3": "ja", "4": "ko", "5": "ru", "6": "te"}}}}, {"name": "answers", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1698662, "num_examples": 15250}, {"name": "validation", "num_bytes": 259533, "num_examples": 2110}, {"name": "test", "num_bytes": 219046, "num_examples": 2499}], "download_size": 3702288, "dataset_size": 2177241}, {"config_name": "xor-full", "features": [{"name": "question", "dtype": "string"}, {"name": "lang", "dtype": {"class_label": {"names": {"0": "ar", "1": "bn", "2": "fi", "3": "ja", "4": "ko", "5": "ru", "6": "te"}}}}, {"name": "answers", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7250913, "num_examples": 61360}, {"name": "validation", "num_bytes": 444672, "num_examples": 3473}, {"name": "test", "num_bytes": 706664, "num_examples": 8176}], "download_size": 14018298, "dataset_size": 8402249}]}

2024-01-18T11:18:45+00:00

TAGS #task_categories-question-answering #task_ids-open-domain-qa #annotations_creators-crowdsourced #language_creators-expert-generated #language_creators-found #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-original #source_datasets-extended|tydiqa #language-Arabic #language-Bengali #language-Finnish #language-Japanese #language-Korean #language-Russian #language-Telugu #license-mit #arxiv-2010.11856 #region-us

Dataset Card for XOR QA ======================= Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: XOR QA Homepage * Repository: XOR QA Repository * Paper: XOR QA Paper * Leaderboard: XOR QA Leaderboard * Point of Contact: Akari Asai ### Dataset Summary XOR-TyDi QA brings together for the first time information-seeking questions, open-retrieval QA, and multilingual QA to create a multilingual open-retrieval QA dataset that enables cross-lingual answer retrieval. It consists of questions written by information-seeking native speakers in 7 typologically diverse languages and answer annotations that are retrieved from multilingual document collections. ### Supported Tasks and Leaderboards There are three sub-tasks: XOR-Retrieve, XOR-EnglishSpan, and XOR-Full. * 'XOR-retrieve': XOR-Retrieve is a cross-lingual retrieval task where a question is written in a target language (e.g., Japanese) and a system is required to retrieve English paragraphs that answer the question. The dataset can be used to train a model for cross-lingual retrieval. Success on this task is typically measured by R@5kt, R@2kt (the recall by computing the fraction of the questions for which the minimal answer is contained in the top 5,000 / 2,000 tokens selected). This task has an active leaderboard which can be found at leaderboard url * 'XOR-English Span': XOR-English Span is a cross-lingual retrieval task where a question is written in a target language (e.g., Japanese) and a system is required to output a short answer in English. The dataset can be used to train a model for cross-lingual retrieval. Success on this task is typically measured by F1, EM. This task has an active leaderboard which can be found at leaderboard url * 'XOR-Full': XOR-Full is a cross-lingual retrieval task where a question is written in the target language (e.g., Japanese) and a system is required to output a short answer in a target language. Success on this task is typically measured by F1, EM, BLEU This task has an active leaderboard which can be found at leaderboard url ### Languages The text in the dataset is available in 7 languages: Arabic 'ar', Bengali 'bn', Finnish 'fi', Japanese 'ja', Korean 'ko', Russian 'ru', Telugu 'te' Dataset Structure ----------------- ### Data Instances A typical data point comprises a 'question', it's 'answer' the 'language' of the question text and the split to which it belongs. ### Data Fields * 'id': An identifier for each example in the dataset * 'question': Open domain question * 'answers': The corresponding answer to the question posed * 'lang': BCP-47 language tag * 'split': identifier to differentiate train, validation and test splits ### Data Splits The data is split into a training, validation and test set for each of the two configurations. Dataset Creation ---------------- ### Curation Rationale This task framework reflects well real-world scenarios where a QA system uses multilingual document collections and answers questions asked by users with diverse linguistic and cultural backgrounds. Despite the common assumption that we can find answers in the target language, web re- sources in non-English languages are largely lim- ited compared to English (information scarcity), or the contents are biased towards their own cul- tures (information asymmetry). To solve these issues, XOR-TYDI QA (Asai et al., 2020) provides a benchmark for developing a multilingual QA system that finds answers in multiple languages. ### Source Data annotation pipeline consists of four steps: 1) collection of realistic questions that require cross-lingual ref- erences by annotating questions from TYDI QA without a same-language answer; 2) question translation from a target language to the pivot language of English where the missing informa- tion may exist; 3) answer span selection in the pivot language given a set of candidate documents; 4) answer verification and translation from the pivot language back to the original language. #### Initial Data Collection and Normalization #### Who are the source language producers? The Dataset is created by extending TyDiQA dataset and translating the questions into other languages. The answers are obtained by crowdsourcing the questions to Mechanical Turk workders ### Annotations #### Annotation process The English questions from TyDiQA are translated into other languages. The languages are chosen based on the availability of wikipedia data and the availability of tranlators. #### Who are the annotators? The translations are carried out using the professionla tranlation service (Gengo)[URL] and the answers are annotated by MechanicalTurk workers ### Personal and Sensitive Information The dataset is created from wikipedia content and the QA task requires preserving the named entities, there by all the Wikipedia Named Entities are preserved in the data. Not much information has been provided about masking sensitive information. Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators The people associated with the creation of the dataset are Akari Asai, Jungo Kasai, Jonathan H. Clark, Kenton Lee, Eunsol Choi, Hannaneh Hajishirzi ### Licensing Information XOR-TyDi QA is distributed under the CC BY-SA 4.0 license ### Contributions Thanks to @sumanthd17 for adding this dataset.

51adfef1c1287aab1d2d91b5bead9bcfb9c68583

# Dataset Card for "xquad" ## 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/xquad](https://github.com/deepmind/xquad) - **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:** 146.31 MB - **Size of the generated dataset:** 18.97 MB - **Total amount of disk used:** 165.28 MB ### Dataset Summary XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering performance. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set of SQuAD v1.1 (Rajpurkar et al., 2016) together with their professional translations into ten languages: Spanish, German, Greek, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, and Hindi. Consequently, the dataset is entirely parallel across 11 languages. ### 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 #### xquad.ar - **Size of downloaded dataset files:** 13.30 MB - **Size of the generated dataset:** 1.72 MB - **Total amount of disk used:** 15.03 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "answers": { "answer_start": [527], "text": ["136"] }, "context": "\"Die Verteidigung der Panthers gab nur 308 Punkte ab und belegte den sechsten Platz in der Liga, während sie die NFL mit 24 Inte...", "id": "56beb4343aeaaa14008c925c", "question": "Wie viele Sacks erzielte Jared Allen in seiner Karriere?" } ``` #### xquad.de - **Size of downloaded dataset files:** 13.30 MB - **Size of the generated dataset:** 1.29 MB - **Total amount of disk used:** 14.59 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "answers": { "answer_start": [527], "text": ["136"] }, "context": "\"Die Verteidigung der Panthers gab nur 308 Punkte ab und belegte den sechsten Platz in der Liga, während sie die NFL mit 24 Inte...", "id": "56beb4343aeaaa14008c925c", "question": "Wie viele Sacks erzielte Jared Allen in seiner Karriere?" } ``` #### xquad.el - **Size of downloaded dataset files:** 13.30 MB - **Size of the generated dataset:** 2.21 MB - **Total amount of disk used:** 15.51 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "answers": { "answer_start": [527], "text": ["136"] }, "context": "\"Die Verteidigung der Panthers gab nur 308 Punkte ab und belegte den sechsten Platz in der Liga, während sie die NFL mit 24 Inte...", "id": "56beb4343aeaaa14008c925c", "question": "Wie viele Sacks erzielte Jared Allen in seiner Karriere?" } ``` #### xquad.en - **Size of downloaded dataset files:** 13.30 MB - **Size of the generated dataset:** 1.12 MB - **Total amount of disk used:** 14.42 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "answers": { "answer_start": [527], "text": ["136"] }, "context": "\"Die Verteidigung der Panthers gab nur 308 Punkte ab und belegte den sechsten Platz in der Liga, während sie die NFL mit 24 Inte...", "id": "56beb4343aeaaa14008c925c", "question": "Wie viele Sacks erzielte Jared Allen in seiner Karriere?" } ``` #### xquad.es - **Size of downloaded dataset files:** 13.30 MB - **Size of the generated dataset:** 1.28 MB - **Total amount of disk used:** 14.58 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "answers": { "answer_start": [527], "text": ["136"] }, "context": "\"Die Verteidigung der Panthers gab nur 308 Punkte ab und belegte den sechsten Platz in der Liga, während sie die NFL mit 24 Inte...", "id": "56beb4343aeaaa14008c925c", "question": "Wie viele Sacks erzielte Jared Allen in seiner Karriere?" } ``` ### Data Fields The data fields are the same among all splits. #### xquad.ar - `id`: 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. #### xquad.de - `id`: 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. #### xquad.el - `id`: 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. #### xquad.en - `id`: 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. #### xquad.es - `id`: 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 | validation | | -------- | ---------: | | xquad.ar | 1190 | | xquad.de | 1190 | | xquad.el | 1190 | | xquad.en | 1190 | | xquad.es | 1190 | ## 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{Artetxe:etal:2019, author = {Mikel Artetxe and Sebastian Ruder and Dani Yogatama}, title = {On the cross-lingual transferability of monolingual representations}, journal = {CoRR}, volume = {abs/1910.11856}, year = {2019}, archivePrefix = {arXiv}, eprint = {1910.11856} } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

xquad

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["ar", "de", "el", "en", "es", "hi", "ro", "ru", "th", "tr", "vi", "zh"], "license": ["cc-by-sa-4.0"], "multilinguality": ["multilingual"], "size_categories": ["unknown"], "source_datasets": ["extended|squad"], "task_categories": ["question-answering"], "task_ids": ["extractive-qa"], "paperswithcode_id": "xquad", "pretty_name": "XQuAD", "dataset_info": [{"config_name": "xquad.ar", "features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}], "splits": [{"name": "validation", "num_bytes": 1722775, "num_examples": 1190}], "download_size": 263002, "dataset_size": 1722775}, {"config_name": "xquad.de", "features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}], "splits": [{"name": "validation", "num_bytes": 1283277, "num_examples": 1190}], "download_size": 241957, "dataset_size": 1283277}, {"config_name": "xquad.el", "features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}], "splits": [{"name": "validation", "num_bytes": 2206666, "num_examples": 1190}], "download_size": 324379, "dataset_size": 2206666}, {"config_name": "xquad.en", "features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}], "splits": [{"name": "validation", "num_bytes": 1116099, "num_examples": 1190}], "download_size": 212372, "dataset_size": 1116099}, {"config_name": "xquad.es", "features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}], "splits": [{"name": "validation", "num_bytes": 1273475, "num_examples": 1190}], "download_size": 236874, "dataset_size": 1273475}, {"config_name": "xquad.hi", "features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}], "splits": [{"name": "validation", "num_bytes": 2682951, "num_examples": 1190}], "download_size": 322083, "dataset_size": 2682951}, {"config_name": "xquad.ro", "features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}], "splits": [{"name": "validation", "num_bytes": 1299426, "num_examples": 1190}], "download_size": 244467, "dataset_size": 1299426}, {"config_name": "xquad.ru", "features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}], "splits": [{"name": "validation", "num_bytes": 2136966, "num_examples": 1190}], "download_size": 321728, "dataset_size": 2136966}, {"config_name": "xquad.th", "features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}], "splits": [{"name": "validation", "num_bytes": 2854935, "num_examples": 1190}], "download_size": 337307, "dataset_size": 2854935}, {"config_name": "xquad.tr", "features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}], "splits": [{"name": "validation", "num_bytes": 1210739, "num_examples": 1190}], "download_size": 228364, "dataset_size": 1210739}, {"config_name": "xquad.vi", "features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}], "splits": [{"name": "validation", "num_bytes": 1477215, "num_examples": 1190}], "download_size": 237644, "dataset_size": 1477215}, {"config_name": "xquad.zh", "features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}], "splits": [{"name": "validation", "num_bytes": 984217, "num_examples": 1190}], "download_size": 205768, "dataset_size": 984217}], "configs": [{"config_name": "xquad.ar", "data_files": [{"split": "validation", "path": "xquad.ar/validation-*"}]}, {"config_name": "xquad.de", "data_files": [{"split": "validation", "path": "xquad.de/validation-*"}]}, {"config_name": "xquad.el", "data_files": [{"split": "validation", "path": "xquad.el/validation-*"}]}, {"config_name": "xquad.en", "data_files": [{"split": "validation", "path": "xquad.en/validation-*"}]}, {"config_name": "xquad.es", "data_files": [{"split": "validation", "path": "xquad.es/validation-*"}]}, {"config_name": "xquad.hi", "data_files": [{"split": "validation", "path": "xquad.hi/validation-*"}]}, {"config_name": "xquad.ro", "data_files": [{"split": "validation", "path": "xquad.ro/validation-*"}]}, {"config_name": "xquad.ru", "data_files": [{"split": "validation", "path": "xquad.ru/validation-*"}]}, {"config_name": "xquad.th", "data_files": [{"split": "validation", "path": "xquad.th/validation-*"}]}, {"config_name": "xquad.tr", "data_files": [{"split": "validation", "path": "xquad.tr/validation-*"}]}, {"config_name": "xquad.vi", "data_files": [{"split": "validation", "path": "xquad.vi/validation-*"}]}, {"config_name": "xquad.zh", "data_files": [{"split": "validation", "path": "xquad.zh/validation-*"}]}]}

2024-01-04T17:08:50+00:00

TAGS #task_categories-question-answering #task_ids-extractive-qa #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-multilingual #size_categories-unknown #source_datasets-extended|squad #language-Arabic #language-German #language-Modern Greek (1453-) #language-English #language-Spanish #language-Hindi #language-Romanian #language-Russian #language-Thai #language-Turkish #language-Vietnamese #language-Chinese #license-cc-by-sa-4.0 #arxiv-1910.11856 #region-us

Dataset Card for "xquad" ======================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: * Point of Contact: * Size of downloaded dataset files: 146.31 MB * Size of the generated dataset: 18.97 MB * Total amount of disk used: 165.28 MB ### Dataset Summary XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering performance. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set of SQuAD v1.1 (Rajpurkar et al., 2016) together with their professional translations into ten languages: Spanish, German, Greek, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, and Hindi. Consequently, the dataset is entirely parallel across 11 languages. ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### URL * Size of downloaded dataset files: 13.30 MB * Size of the generated dataset: 1.72 MB * Total amount of disk used: 15.03 MB An example of 'validation' looks as follows. #### URL * Size of downloaded dataset files: 13.30 MB * Size of the generated dataset: 1.29 MB * Total amount of disk used: 14.59 MB An example of 'validation' looks as follows. #### URL * Size of downloaded dataset files: 13.30 MB * Size of the generated dataset: 2.21 MB * Total amount of disk used: 15.51 MB An example of 'validation' looks as follows. #### URL * Size of downloaded dataset files: 13.30 MB * Size of the generated dataset: 1.12 MB * Total amount of disk used: 14.42 MB An example of 'validation' looks as follows. #### URL * Size of downloaded dataset files: 13.30 MB * Size of the generated dataset: 1.28 MB * Total amount of disk used: 14.58 MB An example of 'validation' looks as follows. ### Data Fields The data fields are the same among all splits. #### URL * 'id': 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. #### URL * 'id': 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. #### URL * 'id': 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. #### URL * 'id': 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. #### URL * 'id': 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 Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @lewtun, @patrickvonplaten, @thomwolf for adding this dataset.

c6be7814d83f0bf3bfdf6858c2f7bb1d779b6440

# 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:** [LAReQA](https://github.com/google-research-datasets/lareqa) - **Repository:** [XQuAD-R](https://github.com/google-research-datasets/lareqa) - **Paper:** [LAReQA: Language-agnostic answer retrieval from a multilingual pool](https://arxiv.org/pdf/2004.05484.pdf) - **Point of Contact:** [Noah Constant](mailto:nconstant@google.com) ### Dataset Summary XQuAD-R is a retrieval version of the XQuAD dataset (a cross-lingual extractive QA dataset). Like XQuAD, XQUAD-R is an 11-way parallel dataset, where each question appears in 11 different languages and has 11 parallel correct answers across the languages. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The dataset can be found with the following languages: * Arabic: `xquad-r/ar.json` * German: `xquad-r/de.json` * Greek: `xquad-r/el.json` * English: `xquad-r/en.json` * Spanish: `xquad-r/es.json` * Hindi: `xquad-r/hi.json` * Russian: `xquad-r/ru.json` * Thai: `xquad-r/th.json` * Turkish: `xquad-r/tr.json` * Vietnamese: `xquad-r/vi.json` * Chinese: `xquad-r/zh.json` ## Dataset Structure [More Information Needed] ### Data Instances An example from `en` config: ``` {'id': '56beb4343aeaaa14008c925b', 'context': "The Panthers defense gave up just 308 points, ranking sixth in the league, while also leading the NFL in interceptions with 24 and boasting four Pro Bowl selections. Pro Bowl defensive tackle Kawann Short led the team in sacks with 11, while also forcing three fumbles and recovering two. Fellow lineman Mario Addison added 6½ sacks. The Panthers line also featured veteran defensive end Jared Allen, a 5-time pro bowler who was the NFL's active career sack leader with 136, along with defensive end Kony Ealy, who had 5 sacks in just 9 starts. Behind them, two of the Panthers three starting linebackers were also selected to play in the Pro Bowl: Thomas Davis and Luke Kuechly. Davis compiled 5½ sacks, four forced fumbles, and four interceptions, while Kuechly led the team in tackles (118) forced two fumbles, and intercepted four passes of his own. Carolina's secondary featured Pro Bowl safety Kurt Coleman, who led the team with a career high seven interceptions, while also racking up 88 tackles and Pro Bowl cornerback Josh Norman, who developed into a shutdown corner during the season and had four interceptions, two of which were returned for touchdowns.", 'question': 'How many points did the Panthers defense surrender?', 'answers': {'text': ['308'], 'answer_start': [34]}} ``` ### Data Fields - `id` (`str`): Unique ID for the context-question pair. - `context` (`str`): Context for the question. - `question` (`str`): Question. - `answers` (`dict`): Answers with the following keys: - `text` (`list` of `str`): Texts of the answers. - `answer_start` (`list` of `int`): Start positions for every answer text. ### Data Splits The number of questions and candidate sentences for each language for XQuAD-R is shown in the table below: | | XQuAD-R | | |-----|-----------|------------| | | questions | candidates | | ar | 1190 | 1222 | | de | 1190 | 1276 | | el | 1190 | 1234 | | en | 1190 | 1180 | | es | 1190 | 1215 | | hi | 1190 | 1244 | | ru | 1190 | 1219 | | th | 1190 | 852 | | tr | 1190 | 1167 | | vi | 1190 | 1209 | | zh | 1190 | 1196 | ## Dataset Creation [More Information Needed] ### 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 [More Information Needed] ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information [More Information Needed] ### Dataset Curators The dataset was initially created by Uma Roy, Noah Constant, Rami Al-Rfou, Aditya Barua, Aaron Phillips and Yinfei Yang, during work done at Google Research. ### Licensing Information XQuAD-R is distributed under the [CC BY-SA 4.0 license](https://creativecommons.org/licenses/by-sa/4.0/legalcode). ### Citation Information ``` @article{roy2020lareqa, title={LAReQA: Language-agnostic answer retrieval from a multilingual pool}, author={Roy, Uma and Constant, Noah and Al-Rfou, Rami and Barua, Aditya and Phillips, Aaron and Yang, Yinfei}, journal={arXiv preprint arXiv:2004.05484}, year={2020} } ``` ### Contributions Thanks to [@manandey](https://github.com/manandey) for adding this dataset.

xquad_r

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["ar", "de", "el", "en", "es", "hi", "ru", "th", "tr", "vi", "zh"], "license": ["cc-by-sa-4.0"], "multilinguality": ["multilingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["extended|squad", "extended|xquad"], "task_categories": ["question-answering"], "task_ids": ["extractive-qa"], "paperswithcode_id": "xquad-r", "pretty_name": "LAReQA", "config_names": ["ar", "de", "el", "en", "es", "hi", "ru", "th", "tr", "vi", "zh"], "dataset_info": [{"config_name": "ar", "features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}], "splits": [{"name": "validation", "num_bytes": 1722775, "num_examples": 1190}], "download_size": 263002, "dataset_size": 1722775}, {"config_name": "de", "features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}], "splits": [{"name": "validation", "num_bytes": 1283277, "num_examples": 1190}], "download_size": 241957, "dataset_size": 1283277}, {"config_name": "el", "features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}], "splits": [{"name": "validation", "num_bytes": 2206666, "num_examples": 1190}], "download_size": 324379, "dataset_size": 2206666}, {"config_name": "en", "features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}], "splits": [{"name": "validation", "num_bytes": 1116099, "num_examples": 1190}], "download_size": 212372, "dataset_size": 1116099}, {"config_name": "es", "features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}], "splits": [{"name": "validation", "num_bytes": 1273475, "num_examples": 1190}], "download_size": 236874, "dataset_size": 1273475}, {"config_name": "hi", "features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}], "splits": [{"name": "validation", "num_bytes": 2682951, "num_examples": 1190}], "download_size": 322083, "dataset_size": 2682951}, {"config_name": "ru", "features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}], "splits": [{"name": "validation", "num_bytes": 2136966, "num_examples": 1190}], "download_size": 321728, "dataset_size": 2136966}, {"config_name": "th", "features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}], "splits": [{"name": "validation", "num_bytes": 2854935, "num_examples": 1190}], "download_size": 337307, "dataset_size": 2854935}, {"config_name": "tr", "features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}], "splits": [{"name": "validation", "num_bytes": 1210739, "num_examples": 1190}], "download_size": 228364, "dataset_size": 1210739}, {"config_name": "vi", "features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}], "splits": [{"name": "validation", "num_bytes": 1477215, "num_examples": 1190}], "download_size": 237644, "dataset_size": 1477215}, {"config_name": "zh", "features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}], "splits": [{"name": "validation", "num_bytes": 984217, "num_examples": 1190}], "download_size": 205768, "dataset_size": 984217}], "configs": [{"config_name": "ar", "data_files": [{"split": "validation", "path": "ar/validation-*"}]}, {"config_name": "de", "data_files": [{"split": "validation", "path": "de/validation-*"}]}, {"config_name": "el", "data_files": [{"split": "validation", "path": "el/validation-*"}]}, {"config_name": "en", "data_files": [{"split": "validation", "path": "en/validation-*"}]}, {"config_name": "es", "data_files": [{"split": "validation", "path": "es/validation-*"}]}, {"config_name": "hi", "data_files": [{"split": "validation", "path": "hi/validation-*"}]}, {"config_name": "ru", "data_files": [{"split": "validation", "path": "ru/validation-*"}]}, {"config_name": "th", "data_files": [{"split": "validation", "path": "th/validation-*"}]}, {"config_name": "tr", "data_files": [{"split": "validation", "path": "tr/validation-*"}]}, {"config_name": "vi", "data_files": [{"split": "validation", "path": "vi/validation-*"}]}, {"config_name": "zh", "data_files": [{"split": "validation", "path": "zh/validation-*"}]}]}

2024-01-04T17:11:57+00:00

TAGS #task_categories-question-answering #task_ids-extractive-qa #annotations_creators-expert-generated #language_creators-found #multilinguality-multilingual #size_categories-1K<n<10K #source_datasets-extended|squad #source_datasets-extended|xquad #language-Arabic #language-German #language-Modern Greek (1453-) #language-English #language-Spanish #language-Hindi #language-Russian #language-Thai #language-Turkish #language-Vietnamese #language-Chinese #license-cc-by-sa-4.0 #arxiv-2004.05484 #region-us

Dataset Card for [Dataset Name] =============================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: LAReQA * Repository: XQuAD-R * Paper: LAReQA: Language-agnostic answer retrieval from a multilingual pool * Point of Contact: Noah Constant ### Dataset Summary XQuAD-R is a retrieval version of the XQuAD dataset (a cross-lingual extractive QA dataset). Like XQuAD, XQUAD-R is an 11-way parallel dataset, where each question appears in 11 different languages and has 11 parallel correct answers across the languages. ### Supported Tasks and Leaderboards ### Languages The dataset can be found with the following languages: * Arabic: 'xquad-r/URL' * German: 'xquad-r/URL' * Greek: 'xquad-r/URL' * English: 'xquad-r/URL' * Spanish: 'xquad-r/URL' * Hindi: 'xquad-r/URL' * Russian: 'xquad-r/URL' * Thai: 'xquad-r/URL' * Turkish: 'xquad-r/URL' * Vietnamese: 'xquad-r/URL' * Chinese: 'xquad-r/URL' Dataset Structure ----------------- ### Data Instances An example from 'en' config: ### Data Fields * 'id' ('str'): Unique ID for the context-question pair. * 'context' ('str'): Context for the question. * 'question' ('str'): Question. * 'answers' ('dict'): Answers with the following keys: + 'text' ('list' of 'str'): Texts of the answers. + 'answer\_start' ('list' of 'int'): Start positions for every answer text. ### Data Splits The number of questions and candidate sentences for each language for XQuAD-R is shown in the table below: XQuAD-R: XQuAD-R: ar XQuAD-R: de XQuAD-R: el XQuAD-R: en XQuAD-R: es XQuAD-R: hi XQuAD-R: ru XQuAD-R: th XQuAD-R: tr XQuAD-R: vi XQuAD-R: zh Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators The dataset was initially created by Uma Roy, Noah Constant, Rami Al-Rfou, Aditya Barua, Aaron Phillips and Yinfei Yang, during work done at Google Research. ### Licensing Information XQuAD-R is distributed under the CC BY-SA 4.0 license. ### Contributions Thanks to @manandey for adding this dataset.

40db7604fedb616a9d2b0673d11838fa5be8451c

# Dataset Card for "xsum" ## 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:** https://github.com/EdinburghNLP/XSum - **Paper:** [Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://arxiv.org/abs/1808.08745) - **Point of Contact:** [Shashi Narayan](mailto:shashi.narayan@ed.ac.uk) - **Size of downloaded dataset files:** 257.30 MB - **Size of the generated dataset:** 532.26 MB - **Total amount of disk used:** 789.56 MB ### Dataset Summary Extreme Summarization (XSum) Dataset. There are three features: - document: Input news article. - summary: One sentence summary of the article. - id: BBC ID of the article. ### 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:** 257.30 MB - **Size of the generated dataset:** 532.26 MB - **Total amount of disk used:** 789.56 MB An example of 'validation' looks as follows. ``` { "document": "some-body", "id": "29750031", "summary": "some-sentence" } ``` ### Data Fields The data fields are the same among all splits. #### default - `document`: a `string` feature. - `summary`: a `string` feature. - `id`: a `string` feature. ### Data Splits | name |train |validation|test | |-------|-----:|---------:|----:| |default|204045| 11332|11334| ## 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{Narayan2018DontGM, title={Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization}, author={Shashi Narayan and Shay B. Cohen and Mirella Lapata}, journal={ArXiv}, year={2018}, volume={abs/1808.08745} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@mariamabarham](https://github.com/mariamabarham), [@jbragg](https://github.com/jbragg), [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

EdinburghNLP/xsum

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["summarization"], "task_ids": ["news-articles-summarization"], "paperswithcode_id": "xsum", "pretty_name": "Extreme Summarization (XSum)", "dataset_info": {"features": [{"name": "document", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 479206608, "num_examples": 204045}, {"name": "validation", "num_bytes": 26292901, "num_examples": 11332}, {"name": "test", "num_bytes": 26756165, "num_examples": 11334}], "download_size": 257302866, "dataset_size": 532255674}, "train-eval-index": [{"config": "default", "task": "summarization", "task_id": "summarization", "splits": {"train_split": "train", "eval_split": "test"}, "col_mapping": {"document": "text", "summary": "target"}, "metrics": [{"type": "rouge", "name": "Rouge"}]}]}

2023-04-05T12:45:25+00:00

TAGS #task_categories-summarization #task_ids-news-articles-summarization #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-unknown #arxiv-1808.08745 #region-us

Dataset Card for "xsum" ======================= Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: * Repository: URL * Paper: Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization * Point of Contact: Shashi Narayan * Size of downloaded dataset files: 257.30 MB * Size of the generated dataset: 532.26 MB * Total amount of disk used: 789.56 MB ### Dataset Summary Extreme Summarization (XSum) Dataset. There are three features: * document: Input news article. * summary: One sentence summary of the article. * id: BBC ID of the article. ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### default * Size of downloaded dataset files: 257.30 MB * Size of the generated dataset: 532.26 MB * Total amount of disk used: 789.56 MB An example of 'validation' looks as follows. ### Data Fields The data fields are the same among all splits. #### default * 'document': a 'string' feature. * 'summary': a 'string' feature. * 'id': a 'string' feature. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @thomwolf, @lewtun, @mariamabarham, @jbragg, @lhoestq, @patrickvonplaten for adding this dataset.

599e5d6a27b66410149bff3cea773fe3cb7f09c0

# Dataset Card for XSum Hallucination Annotations ## 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:** [XSUM Hallucination Annotations Homepage](https://research.google/tools/datasets/xsum-hallucination-annotations/) - **Repository:** [XSUM Hallucination Annotations Homepage](https://github.com/google-research-datasets/xsum_hallucination_annotations) - **Paper:** [ACL Web](https://www.aclweb.org/anthology/2020.acl-main.173.pdf) - **Point of Contact:** [xsum-hallucinations-acl20@google.com](mailto:xsum-hallucinations-acl20@google.com) ### Dataset Summary Neural abstractive summarization models are highly prone to hallucinate content that is unfaithful to the input document. The popular metric such as ROUGE fails to show the severity of the problem. This dataset contains a large scale human evaluation of several neural abstractive summarization systems to better understand the types of hallucinations they produce. The dataset consists of faithfulness and factuality annotations of abstractive summaries for the XSum dataset. The dataset has crowdsourced 3 judgements for each of 500 x 5 document-system pairs. This will be a valuable resource to the abstractive summarization community. ### Supported Tasks and Leaderboards * `summarization`: : The dataset can be used to train a model for Summarization,, which consists in summarizing a given document. Success on this task is typically measured by achieving a *high/low* [ROUGE Score](https://huggingface.co/metrics/rouge). ### Languages The text in the dataset is in English which are abstractive summaries for the [XSum dataset](https://www.aclweb.org/anthology/D18-1206.pdf). The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances ##### Faithfulness annotations dataset A typical data point consists of an ID referring to the news article(complete document), summary, and the hallucination span information. An example from the XSum Faithfulness dataset looks as follows: ``` { 'bbcid': 34687720, 'hallucinated_span_end': 114, 'hallucinated_span_start': 1, 'hallucination_type': 1, 'summary': 'rory mcilroy will take a one-shot lead into the final round of the wgc-hsbc champions after carding a three-under', 'system': 'BERTS2S', 'worker_id': 'wid_0' } ``` ##### Factuality annotations dataset A typical data point consists of an ID referring to the news article(complete document), summary, and whether the summary is factual or not. An example from the XSum Factuality dataset looks as follows: ``` { 'bbcid': 29911712, 'is_factual': 0, 'summary': 'more than 50 pupils at a bristol academy have been sent home from school because of a lack of uniform.', 'system': 'BERTS2S', 'worker_id': 'wid_0' } ``` ### Data Fields ##### Faithfulness annotations dataset Raters are shown the news article and the system summary, and are tasked with identifying and annotating the spans that aren't supported by the input article. The file contains the following columns: - `bbcid`: Document id in the XSum corpus. - `system`: Name of neural summarizer. - `summary`: Summary generated by ‘system’. - `hallucination_type`: Type of hallucination: intrinsic (0) or extrinsic (1) - `hallucinated_span`: Hallucinated span in the ‘summary’. - `hallucinated_span_start`: Index of the start of the hallucinated span. - `hallucinated_span_end`: Index of the end of the hallucinated span. - `worker_id`: Worker ID (one of 'wid_0', 'wid_1', 'wid_2') The `hallucination_type` column has NULL value for some entries which have been replaced iwth `-1`. ##### Factuality annotations dataset Raters are shown the news article and the hallucinated system summary, and are tasked with assessing the summary whether it is factual or not. The file contains the following columns: - `bbcid1: Document id in the XSum corpus. - `system`: Name of neural summarizer. - `summary`: Summary generated by ‘system’. - `is_factual`: Yes (1) or No (0) - `worker_id`: Worker ID (one of 'wid_0', 'wid_1', 'wid_2') The `is_factual` column has NULL value for some entries which have been replaced iwth `-1`. ### Data Splits There is only a single split for both the Faithfulness annotations dataset and Factuality annotations dataset. | | train | |--------------------------|------:| | Faithfulness annotations | 11185 | | Factuality annotations | 5597 | ## 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 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode) ### Citation Information ``` @InProceedings{maynez_acl20, author = "Joshua Maynez and Shashi Narayan and Bernd Bohnet and Ryan Thomas Mcdonald", title = "On Faithfulness and Factuality in Abstractive Summarization", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", year = "2020", pages = "1906--1919", address = "Online", } ``` ### Contributions Thanks to [@vineeths96](https://github.com/vineeths96) for adding this dataset.

xsum_factuality

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["extended|other-xsum"], "task_categories": ["summarization"], "task_ids": [], "pretty_name": "XSum Hallucination Annotations", "tags": ["hallucinations"], "dataset_info": [{"config_name": "xsum_factuality", "features": [{"name": "bbcid", "dtype": "int32"}, {"name": "system", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "is_factual", "dtype": {"class_label": {"names": {"0": "no", "1": "yes"}}}}, {"name": "worker_id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 800027, "num_examples": 5597}], "download_size": 2864759, "dataset_size": 800027}, {"config_name": "xsum_faithfulness", "features": [{"name": "bbcid", "dtype": "int32"}, {"name": "system", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "hallucination_type", "dtype": {"class_label": {"names": {"0": "intrinsic", "1": "extrinsic"}}}}, {"name": "hallucinated_span_start", "dtype": "int32"}, {"name": "hallucinated_span_end", "dtype": "int32"}, {"name": "worker_id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1750325, "num_examples": 11185}], "download_size": 2864759, "dataset_size": 1750325}]}

2024-01-18T11:18:47+00:00

TAGS #task_categories-summarization #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-extended|other-xsum #language-English #license-cc-by-4.0 #hallucinations #region-us

Dataset Card for XSum Hallucination Annotations =============================================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: XSUM Hallucination Annotations Homepage * Repository: XSUM Hallucination Annotations Homepage * Paper: ACL Web * Point of Contact: xsum-hallucinations-acl20@URL ### Dataset Summary Neural abstractive summarization models are highly prone to hallucinate content that is unfaithful to the input document. The popular metric such as ROUGE fails to show the severity of the problem. This dataset contains a large scale human evaluation of several neural abstractive summarization systems to better understand the types of hallucinations they produce. The dataset consists of faithfulness and factuality annotations of abstractive summaries for the XSum dataset. The dataset has crowdsourced 3 judgements for each of 500 x 5 document-system pairs. This will be a valuable resource to the abstractive summarization community. ### Supported Tasks and Leaderboards * 'summarization': : The dataset can be used to train a model for Summarization,, which consists in summarizing a given document. Success on this task is typically measured by achieving a *high/low* ROUGE Score. ### Languages The text in the dataset is in English which are abstractive summaries for the XSum dataset. The associated BCP-47 code is 'en'. Dataset Structure ----------------- ### Data Instances ##### Faithfulness annotations dataset A typical data point consists of an ID referring to the news article(complete document), summary, and the hallucination span information. An example from the XSum Faithfulness dataset looks as follows: ##### Factuality annotations dataset A typical data point consists of an ID referring to the news article(complete document), summary, and whether the summary is factual or not. An example from the XSum Factuality dataset looks as follows: ### Data Fields ##### Faithfulness annotations dataset Raters are shown the news article and the system summary, and are tasked with identifying and annotating the spans that aren't supported by the input article. The file contains the following columns: * 'bbcid': Document id in the XSum corpus. * 'system': Name of neural summarizer. * 'summary': Summary generated by ‘system’. * 'hallucination\_type': Type of hallucination: intrinsic (0) or extrinsic (1) * 'hallucinated\_span': Hallucinated span in the ‘summary’. * 'hallucinated\_span\_start': Index of the start of the hallucinated span. * 'hallucinated\_span\_end': Index of the end of the hallucinated span. * 'worker\_id': Worker ID (one of 'wid\_0', 'wid\_1', 'wid\_2') The 'hallucination\_type' column has NULL value for some entries which have been replaced iwth '-1'. ##### Factuality annotations dataset Raters are shown the news article and the hallucinated system summary, and are tasked with assessing the summary whether it is factual or not. The file contains the following columns: * 'bbcid1: Document id in the XSum corpus. * 'system': Name of neural summarizer. * 'summary': Summary generated by ‘system’. * 'is\_factual': Yes (1) or No (0) * 'worker\_id': Worker ID (one of 'wid\_0', 'wid\_1', 'wid\_2') The 'is\_factual' column has NULL value for some entries which have been replaced iwth '-1'. ### Data Splits There is only a single split for both the Faithfulness annotations dataset and Factuality annotations dataset. Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information Creative Commons Attribution 4.0 International ### Contributions Thanks to @vineeths96 for adding this dataset.

16076f13195ba20a65f3af3ca9fba9c843f12124

# Dataset Card for "xtreme" ## 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/xtreme](https://github.com/google-research/xtreme) - **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:** 15.88 GB - **Size of the generated dataset:** 1.08 GB - **Total amount of disk used:** 16.96 GB ### Dataset Summary The Cross-lingual Natural Language Inference (XNLI) corpus is a crowd-sourced collection of 5,000 test and 2,500 dev pairs for the MultiNLI corpus. The pairs are annotated with textual entailment and translated into 14 languages: French, Spanish, German, Greek, Bulgarian, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, Hindi, Swahili and Urdu. This results in 112.5k annotated pairs. Each premise can be associated with the corresponding hypothesis in the 15 languages, summing up to more than 1.5M combinations. The corpus is made to evaluate how to perform inference in any language (including low-resources ones like Swahili or Urdu) when only English NLI data is available at training time. One solution is cross-lingual sentence encoding, for which XNLI is an evaluation benchmark. The Cross-lingual TRansfer Evaluation of Multilingual Encoders (XTREME) benchmark is a benchmark for the evaluation of the cross-lingual generalization ability of pre-trained multilingual models. It covers 40 typologically diverse languages (spanning 12 language families) and includes nine tasks that collectively require reasoning about different levels of syntax and semantics. The languages in XTREME are selected to maximize language diversity, coverage in existing tasks, and availability of training data. Among these are many under-studied languages, such as the Dravidian languages Tamil (spoken in southern India, Sri Lanka, and Singapore), Telugu and Malayalam (spoken mainly in southern India), and the Niger-Congo languages Swahili and Yoruba, spoken in Africa. ### 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 #### MLQA.ar.ar - **Size of downloaded dataset files:** 75.72 MB - **Size of the generated dataset:** 9.20 MB - **Total amount of disk used:** 84.91 MB An example of 'validation' looks as follows. ``` ``` #### MLQA.ar.de - **Size of downloaded dataset files:** 75.72 MB - **Size of the generated dataset:** 2.55 MB - **Total amount of disk used:** 78.27 MB An example of 'validation' looks as follows. ``` ``` #### MLQA.ar.en - **Size of downloaded dataset files:** 75.72 MB - **Size of the generated dataset:** 9.04 MB - **Total amount of disk used:** 84.76 MB An example of 'validation' looks as follows. ``` ``` #### MLQA.ar.es - **Size of downloaded dataset files:** 75.72 MB - **Size of the generated dataset:** 3.27 MB - **Total amount of disk used:** 78.99 MB An example of 'validation' looks as follows. ``` ``` #### MLQA.ar.hi - **Size of downloaded dataset files:** 75.72 MB - **Size of the generated dataset:** 3.32 MB - **Total amount of disk used:** 79.04 MB An example of 'validation' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### MLQA.ar.ar - `id`: a `string` feature. - `title`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `answer_start`: a `int32` feature. - `text`: a `string` feature. #### MLQA.ar.de - `id`: a `string` feature. - `title`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `answer_start`: a `int32` feature. - `text`: a `string` feature. #### MLQA.ar.en - `id`: a `string` feature. - `title`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `answer_start`: a `int32` feature. - `text`: a `string` feature. #### MLQA.ar.es - `id`: a `string` feature. - `title`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `answer_start`: a `int32` feature. - `text`: a `string` feature. #### MLQA.ar.hi - `id`: a `string` feature. - `title`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `answer_start`: a `int32` feature. - `text`: a `string` feature. ### Data Splits | name |validation|test| |----------|---------:|---:| |MLQA.ar.ar| 517|5335| |MLQA.ar.de| 207|1649| |MLQA.ar.en| 517|5335| |MLQA.ar.es| 161|1978| |MLQA.ar.hi| 186|1831| ## 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{conneau2018xnli, author = {Conneau, Alexis and Rinott, Ruty and Lample, Guillaume and Williams, Adina and Bowman, Samuel R. and Schwenk, Holger and Stoyanov, Veselin}, title = {XNLI: Evaluating Cross-lingual Sentence Representations}, booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing}, year = {2018}, publisher = {Association for Computational Linguistics}, location = {Brussels, Belgium}, } @article{hu2020xtreme, author = {Junjie Hu and Sebastian Ruder and Aditya Siddhant and Graham Neubig and Orhan Firat and Melvin Johnson}, title = {XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization}, journal = {CoRR}, volume = {abs/2003.11080}, year = {2020}, archivePrefix = {arXiv}, eprint = {2003.11080} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@jplu](https://github.com/jplu), [@lewtun](https://github.com/lewtun), [@lvwerra](https://github.com/lvwerra), [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset.

xtreme

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["af", "ar", "bg", "bn", "de", "el", "en", "es", "et", "eu", "fa", "fi", "fr", "he", "hi", "hu", "id", "it", "ja", "jv", "ka", "kk", "ko", "ml", "mr", "ms", "my", "nl", "pt", "ru", "sw", "ta", "te", "th", "tl", "tr", "ur", "vi", "yo", "zh"], "license": ["apache-2.0", "cc-by-4.0", "cc-by-2.0", "cc-by-sa-4.0", "other", "cc-by-nc-4.0"], "multilinguality": ["multilingual", "translation"], "size_categories": ["n<1K", "1K<n<10K", "10K<n<100K", "100K<n<1M"], "source_datasets": ["extended|xnli", "extended|paws-x", "extended|wikiann", "extended|xquad", "extended|mlqa", "extended|tydiqa", "extended|tatoeba", "extended|squad"], "task_categories": ["multiple-choice", "question-answering", "token-classification", "text-classification", "text-retrieval", "token-classification"], "task_ids": ["multiple-choice-qa", "extractive-qa", "open-domain-qa", "natural-language-inference", "named-entity-recognition", "part-of-speech"], "paperswithcode_id": "xtreme", "pretty_name": "XTREME", "config_names": ["MLQA.ar.ar", "MLQA.ar.de", "MLQA.ar.en", "MLQA.ar.es", "MLQA.ar.hi", "MLQA.ar.vi", "MLQA.ar.zh", "MLQA.de.ar", "MLQA.de.de", "MLQA.de.en", "MLQA.de.es", "MLQA.de.hi", "MLQA.de.vi", "MLQA.de.zh", "MLQA.en.ar", "MLQA.en.de", "MLQA.en.en", "MLQA.en.es", "MLQA.en.hi", "MLQA.en.vi", "MLQA.en.zh", "MLQA.es.ar", "MLQA.es.de", "MLQA.es.en", "MLQA.es.es", "MLQA.es.hi", "MLQA.es.vi", "MLQA.es.zh", "MLQA.hi.ar", "MLQA.hi.de", "MLQA.hi.en", "MLQA.hi.es", "MLQA.hi.hi", "MLQA.hi.vi", "MLQA.hi.zh", "MLQA.vi.ar", "MLQA.vi.de", "MLQA.vi.en", "MLQA.vi.es", "MLQA.vi.hi", "MLQA.vi.vi", "MLQA.vi.zh", "MLQA.zh.ar", "MLQA.zh.de", "MLQA.zh.en", "MLQA.zh.es", "MLQA.zh.hi", "MLQA.zh.vi", "MLQA.zh.zh", "PAN-X.af", "PAN-X.ar", "PAN-X.bg", "PAN-X.bn", "PAN-X.de", "PAN-X.el", "PAN-X.en", "PAN-X.es", "PAN-X.et", "PAN-X.eu", "PAN-X.fa", "PAN-X.fi", "PAN-X.fr", "PAN-X.he", "PAN-X.hi", "PAN-X.hu", "PAN-X.id", "PAN-X.it", "PAN-X.ja", "PAN-X.jv", "PAN-X.ka", "PAN-X.kk", "PAN-X.ko", "PAN-X.ml", "PAN-X.mr", "PAN-X.ms", "PAN-X.my", "PAN-X.nl", "PAN-X.pt", "PAN-X.ru", "PAN-X.sw", "PAN-X.ta", "PAN-X.te", "PAN-X.th", "PAN-X.tl", "PAN-X.tr", "PAN-X.ur", "PAN-X.vi", "PAN-X.yo", "PAN-X.zh", "PAWS-X.de", "PAWS-X.en", "PAWS-X.es", "PAWS-X.fr", "PAWS-X.ja", "PAWS-X.ko", "PAWS-X.zh", "SQuAD", "XNLI", "XQuAD", "bucc18.de", "bucc18.fr", "bucc18.ru", "bucc18.zh", "tatoeba.afr", "tatoeba.ara", "tatoeba.ben", "tatoeba.bul", "tatoeba.cmn", "tatoeba.deu", "tatoeba.ell", "tatoeba.est", "tatoeba.eus", "tatoeba.fin", "tatoeba.fra", "tatoeba.heb", "tatoeba.hin", "tatoeba.hun", "tatoeba.ind", "tatoeba.ita", "tatoeba.jav", "tatoeba.jpn", "tatoeba.kat", "tatoeba.kaz", "tatoeba.kor", "tatoeba.mal", "tatoeba.mar", "tatoeba.nld", "tatoeba.pes", "tatoeba.por", "tatoeba.rus", "tatoeba.spa", "tatoeba.swh", "tatoeba.tam", "tatoeba.tel", "tatoeba.tgl", "tatoeba.tha", "tatoeba.tur", "tatoeba.urd", "tatoeba.vie", "tydiqa", "udpos.Afrikans", "udpos.Arabic", "udpos.Basque", "udpos.Bulgarian", "udpos.Chinese", "udpos.Dutch", "udpos.English", "udpos.Estonian", "udpos.Finnish", "udpos.French", "udpos.German", "udpos.Greek", "udpos.Hebrew", "udpos.Hindi", "udpos.Hungarian", "udpos.Indonesian", "udpos.Italian", "udpos.Japanese", "udpos.Kazakh", "udpos.Korean", "udpos.Marathi", "udpos.Persian", "udpos.Portuguese", "udpos.Russian", "udpos.Spanish", "udpos.Tagalog", "udpos.Tamil", "udpos.Telugu", "udpos.Thai", "udpos.Turkish", "udpos.Urdu", "udpos.Vietnamese", "udpos.Yoruba"], "language_bcp47": ["fa-IR"], "license_details": "Licence Universal Dependencies v2.5", "tags": ["parallel-sentence-retrieval", "paraphrase-identification"], "dataset_info": [{"config_name": "XNLI", "features": [{"name": "language", "dtype": "string"}, {"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "gold_label", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 20359500, "num_examples": 75150}, {"name": "validation", "num_bytes": 10049303, "num_examples": 37350}], "download_size": 17865352, "dataset_size": 30408803}, {"config_name": "tydiqa", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 52948607, "num_examples": 49881}, {"name": "validation", "num_bytes": 5006461, "num_examples": 5077}], "download_size": 63621485, "dataset_size": 57955068}, {"config_name": "SQuAD", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 79317110, "num_examples": 87599}, {"name": "validation", "num_bytes": 10472653, "num_examples": 10570}], "download_size": 35142551, "dataset_size": 89789763}, {"config_name": "PAN-X.af", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "langs", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 259709, "num_examples": 1000}, {"name": "test", "num_bytes": 257204, "num_examples": 1000}, {"name": "train", "num_bytes": 1321396, "num_examples": 5000}], "download_size": 234008884, "dataset_size": 1838309}, {"config_name": "PAN-X.ar", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "langs", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 1808303, "num_examples": 10000}, {"name": "test", "num_bytes": 1811983, "num_examples": 10000}, {"name": "train", "num_bytes": 3634136, "num_examples": 20000}], "download_size": 234008884, "dataset_size": 7254422}, {"config_name": "PAN-X.bg", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "langs", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 2310314, "num_examples": 10000}, {"name": "test", "num_bytes": 2306158, "num_examples": 10000}, {"name": "train", "num_bytes": 4600773, "num_examples": 20000}], "download_size": 234008884, "dataset_size": 9217245}, {"config_name": "PAN-X.bn", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "langs", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 159088, "num_examples": 1000}, {"name": "test", "num_bytes": 159282, "num_examples": 1000}, {"name": "train", "num_bytes": 1568845, "num_examples": 10000}], "download_size": 234008884, "dataset_size": 1887215}, {"config_name": "PAN-X.de", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "langs", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 2381565, "num_examples": 10000}, {"name": "test", "num_bytes": 2377639, "num_examples": 10000}, {"name": "train", "num_bytes": 4762352, "num_examples": 20000}], "download_size": 234008884, "dataset_size": 9521556}, {"config_name": "PAN-X.el", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "langs", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 2533806, "num_examples": 10000}, {"name": "test", "num_bytes": 2547594, "num_examples": 10000}, {"name": "train", "num_bytes": 5063176, "num_examples": 20000}], "download_size": 234008884, "dataset_size": 10144576}, {"config_name": "PAN-X.en", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "langs", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 1920069, "num_examples": 10000}, {"name": "test", "num_bytes": 1916220, "num_examples": 10000}, {"name": "train", "num_bytes": 3823474, "num_examples": 20000}], "download_size": 234008884, "dataset_size": 7659763}, {"config_name": "PAN-X.es", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "langs", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 1592525, "num_examples": 10000}, {"name": "test", "num_bytes": 1602291, "num_examples": 10000}, {"name": "train", "num_bytes": 3199161, "num_examples": 20000}], "download_size": 234008884, "dataset_size": 6393977}, {"config_name": "PAN-X.et", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "langs", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 2030160, "num_examples": 10000}, {"name": "test", "num_bytes": 2021409, "num_examples": 10000}, {"name": "train", "num_bytes": 3023211, "num_examples": 15000}], "download_size": 234008884, "dataset_size": 7074780}, {"config_name": "PAN-X.eu", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "langs", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 2296335, "num_examples": 10000}, {"name": "test", "num_bytes": 2249835, "num_examples": 10000}, {"name": "train", "num_bytes": 2292327, "num_examples": 10000}], "download_size": 234008884, "dataset_size": 6838497}, {"config_name": "PAN-X.fa", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "langs", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 1782306, "num_examples": 10000}, {"name": "test", "num_bytes": 1770284, "num_examples": 10000}, {"name": "train", "num_bytes": 3529354, "num_examples": 20000}], "download_size": 234008884, "dataset_size": 7081944}, {"config_name": "PAN-X.fi", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "langs", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 2131769, "num_examples": 10000}, {"name": "test", "num_bytes": 2130665, "num_examples": 10000}, {"name": "train", "num_bytes": 4273793, "num_examples": 20000}], "download_size": 234008884, "dataset_size": 8536227}, {"config_name": "PAN-X.fr", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "langs", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 1664190, "num_examples": 10000}, {"name": "test", "num_bytes": 1675785, "num_examples": 10000}, {"name": "train", "num_bytes": 3335424, "num_examples": 20000}], "download_size": 234008884, "dataset_size": 6675399}, {"config_name": "PAN-X.he", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "langs", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 2332760, "num_examples": 10000}, {"name": "test", "num_bytes": 2318756, "num_examples": 10000}, {"name": "train", "num_bytes": 4667100, "num_examples": 20000}], "download_size": 234008884, "dataset_size": 9318616}, {"config_name": "PAN-X.hi", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "langs", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 190671, "num_examples": 1000}, {"name": "test", "num_bytes": 196190, "num_examples": 1000}, {"name": "train", "num_bytes": 964212, "num_examples": 5000}], "download_size": 234008884, "dataset_size": 1351073}, {"config_name": "PAN-X.hu", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "langs", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 2211851, "num_examples": 10000}, {"name": "test", "num_bytes": 2249779, "num_examples": 10000}, {"name": "train", "num_bytes": 4499914, "num_examples": 20000}], "download_size": 234008884, "dataset_size": 8961544}, {"config_name": "PAN-X.id", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "langs", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 1537979, "num_examples": 10000}, {"name": "test", "num_bytes": 1536879, "num_examples": 10000}, {"name": "train", "num_bytes": 3084007, "num_examples": 20000}], "download_size": 234008884, "dataset_size": 6158865}, {"config_name": "PAN-X.it", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "langs", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 1908529, "num_examples": 10000}, {"name": "test", "num_bytes": 1928408, "num_examples": 10000}, {"name": "train", "num_bytes": 3874663, "num_examples": 20000}], "download_size": 234008884, "dataset_size": 7711600}, {"config_name": "PAN-X.ja", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "langs", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 6323003, "num_examples": 10000}, {"name": "test", "num_bytes": 6448960, "num_examples": 10000}, {"name": "train", "num_bytes": 12670401, "num_examples": 20000}], "download_size": 234008884, "dataset_size": 25442364}, {"config_name": "PAN-X.jv", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "langs", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 14600, "num_examples": 100}, {"name": "test", "num_bytes": 16917, "num_examples": 100}, {"name": "train", "num_bytes": 16106, "num_examples": 100}], "download_size": 234008884, "dataset_size": 47623}, {"config_name": "PAN-X.ka", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "langs", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 2806901, "num_examples": 10000}, {"name": "test", "num_bytes": 2824641, "num_examples": 10000}, {"name": "train", "num_bytes": 2777362, "num_examples": 10000}], "download_size": 234008884, "dataset_size": 8408904}, {"config_name": "PAN-X.kk", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "langs", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 238109, "num_examples": 1000}, {"name": "test", "num_bytes": 236724, "num_examples": 1000}, {"name": "train", "num_bytes": 240276, "num_examples": 1000}], "download_size": 234008884, "dataset_size": 715109}, {"config_name": "PAN-X.ko", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "langs", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 2138167, "num_examples": 10000}, {"name": "test", "num_bytes": 2138294, "num_examples": 10000}, {"name": "train", "num_bytes": 4284733, "num_examples": 20000}], "download_size": 234008884, "dataset_size": 8561194}, {"config_name": "PAN-X.ml", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "langs", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 290755, "num_examples": 1000}, {"name": "test", "num_bytes": 276926, "num_examples": 1000}, {"name": "train", "num_bytes": 2865204, "num_examples": 10000}], "download_size": 234008884, "dataset_size": 3432885}, {"config_name": "PAN-X.mr", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "langs", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 245358, "num_examples": 1000}, {"name": "test", "num_bytes": 255904, "num_examples": 1000}, {"name": "train", "num_bytes": 1248259, "num_examples": 5000}], "download_size": 234008884, "dataset_size": 1749521}, {"config_name": "PAN-X.ms", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "langs", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 147515, "num_examples": 1000}, {"name": "test", "num_bytes": 147168, "num_examples": 1000}, {"name": "train", "num_bytes": 2965048, "num_examples": 20000}], "download_size": 234008884, "dataset_size": 3259731}, {"config_name": "PAN-X.my", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "langs", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 40428, "num_examples": 100}, {"name": "test", "num_bytes": 37366, "num_examples": 100}, {"name": "train", "num_bytes": 32735, "num_examples": 100}], "download_size": 234008884, "dataset_size": 110529}, {"config_name": "PAN-X.nl", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "langs", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 2016856, "num_examples": 10000}, {"name": "test", "num_bytes": 2038638, "num_examples": 10000}, {"name": "train", "num_bytes": 4062189, "num_examples": 20000}], "download_size": 234008884, "dataset_size": 8117683}, {"config_name": "PAN-X.pt", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "langs", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 1575141, "num_examples": 10000}, {"name": "test", "num_bytes": 1562625, "num_examples": 10000}, {"name": "train", "num_bytes": 3149283, "num_examples": 20000}], "download_size": 234008884, "dataset_size": 6287049}, {"config_name": "PAN-X.ru", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "langs", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 2053169, "num_examples": 10000}, {"name": "test", "num_bytes": 2074145, "num_examples": 10000}, {"name": "train", "num_bytes": 4121791, "num_examples": 20000}], "download_size": 234008884, "dataset_size": 8249105}, {"config_name": "PAN-X.sw", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "langs", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 136368, "num_examples": 1000}, {"name": "test", "num_bytes": 140231, "num_examples": 1000}, {"name": "train", "num_bytes": 135911, "num_examples": 1000}], "download_size": 234008884, "dataset_size": 412510}, {"config_name": "PAN-X.ta", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "langs", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 277625, "num_examples": 1000}, {"name": "test", "num_bytes": 278114, "num_examples": 1000}, {"name": "train", "num_bytes": 4122130, "num_examples": 15000}], "download_size": 234008884, "dataset_size": 4677869}, {"config_name": "PAN-X.te", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "langs", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 293281, "num_examples": 1000}, {"name": "test", "num_bytes": 296963, "num_examples": 1000}, {"name": "train", "num_bytes": 295410, "num_examples": 1000}], "download_size": 234008884, "dataset_size": 885654}, {"config_name": "PAN-X.th", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "langs", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 13262737, "num_examples": 10000}, {"name": "test", "num_bytes": 13586928, "num_examples": 10000}, {"name": "train", "num_bytes": 27133029, "num_examples": 20000}], "download_size": 234008884, "dataset_size": 53982694}, {"config_name": "PAN-X.tl", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "langs", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 114156, "num_examples": 1000}, {"name": "test", "num_bytes": 117904, "num_examples": 1000}, {"name": "train", "num_bytes": 1168717, "num_examples": 10000}], "download_size": 234008884, "dataset_size": 1400777}, {"config_name": "PAN-X.tr", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "langs", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 1915352, "num_examples": 10000}, {"name": "test", "num_bytes": 1911503, "num_examples": 10000}, {"name": "train", "num_bytes": 3779170, "num_examples": 20000}], "download_size": 234008884, "dataset_size": 7606025}, {"config_name": "PAN-X.ur", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "langs", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 152148, "num_examples": 1000}, {"name": "test", "num_bytes": 151922, "num_examples": 1000}, {"name": "train", "num_bytes": 3072276, "num_examples": 20000}], "download_size": 234008884, "dataset_size": 3376346}, {"config_name": "PAN-X.vi", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "langs", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 1565143, "num_examples": 10000}, {"name": "test", "num_bytes": 1580216, "num_examples": 10000}, {"name": "train", "num_bytes": 3153227, "num_examples": 20000}], "download_size": 234008884, "dataset_size": 6298586}, {"config_name": "PAN-X.yo", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "langs", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 13245, "num_examples": 100}, {"name": "test", "num_bytes": 13533, "num_examples": 100}, {"name": "train", "num_bytes": 14709, "num_examples": 100}], "download_size": 234008884, "dataset_size": 41487}, {"config_name": "PAN-X.zh", "features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}, {"name": "langs", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 4491325, "num_examples": 10000}, {"name": "test", "num_bytes": 4363172, "num_examples": 10000}, {"name": "train", "num_bytes": 8832051, "num_examples": 20000}], "download_size": 234008884, "dataset_size": 17686548}, {"config_name": "MLQA.ar.ar", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 8368114, "num_examples": 5335}, {"name": "validation", "num_bytes": 824108, "num_examples": 517}], "download_size": 75719050, "dataset_size": 9192222}, {"config_name": "MLQA.ar.de", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 2183942, "num_examples": 1649}, {"name": "validation", "num_bytes": 364837, "num_examples": 207}], "download_size": 75719050, "dataset_size": 2548779}, {"config_name": "MLQA.ar.vi", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 3290629, "num_examples": 2047}, {"name": "validation", "num_bytes": 288446, "num_examples": 163}], "download_size": 75719050, "dataset_size": 3579075}, {"config_name": "MLQA.ar.zh", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 3229872, "num_examples": 1912}, {"name": "validation", "num_bytes": 340049, "num_examples": 188}], "download_size": 75719050, "dataset_size": 3569921}, {"config_name": "MLQA.ar.en", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 8225662, "num_examples": 5335}, {"name": "validation", "num_bytes": 810089, "num_examples": 517}], "download_size": 75719050, "dataset_size": 9035751}, {"config_name": "MLQA.ar.es", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 3041378, "num_examples": 1978}, {"name": "validation", "num_bytes": 228180, "num_examples": 161}], "download_size": 75719050, "dataset_size": 3269558}, {"config_name": "MLQA.ar.hi", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 3039396, "num_examples": 1831}, {"name": "validation", "num_bytes": 281770, "num_examples": 186}], "download_size": 75719050, "dataset_size": 3321166}, {"config_name": "MLQA.de.ar", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 1620006, "num_examples": 1649}, {"name": "validation", "num_bytes": 200174, "num_examples": 207}], "download_size": 75719050, "dataset_size": 1820180}, {"config_name": "MLQA.de.de", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 4366102, "num_examples": 4517}, {"name": "validation", "num_bytes": 488367, "num_examples": 512}], "download_size": 75719050, "dataset_size": 4854469}, {"config_name": "MLQA.de.vi", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 1688483, "num_examples": 1675}, {"name": "validation", "num_bytes": 216075, "num_examples": 182}], "download_size": 75719050, "dataset_size": 1904558}, {"config_name": "MLQA.de.zh", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 1679180, "num_examples": 1621}, {"name": "validation", "num_bytes": 184318, "num_examples": 190}], "download_size": 75719050, "dataset_size": 1863498}, {"config_name": "MLQA.de.en", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 4343144, "num_examples": 4517}, {"name": "validation", "num_bytes": 485894, "num_examples": 512}], "download_size": 75719050, "dataset_size": 4829038}, {"config_name": "MLQA.de.es", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 1716615, "num_examples": 1776}, {"name": "validation", "num_bytes": 170582, "num_examples": 196}], "download_size": 75719050, "dataset_size": 1887197}, {"config_name": "MLQA.de.hi", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 1371074, "num_examples": 1430}, {"name": "validation", "num_bytes": 153871, "num_examples": 163}], "download_size": 75719050, "dataset_size": 1524945}, {"config_name": "MLQA.vi.ar", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 3205185, "num_examples": 2047}, {"name": "validation", "num_bytes": 230335, "num_examples": 163}], "download_size": 75719050, "dataset_size": 3435520}, {"config_name": "MLQA.vi.de", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 2227033, "num_examples": 1675}, {"name": "validation", "num_bytes": 277185, "num_examples": 182}], "download_size": 75719050, "dataset_size": 2504218}, {"config_name": "MLQA.vi.vi", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 7922085, "num_examples": 5495}, {"name": "validation", "num_bytes": 726518, "num_examples": 511}], "download_size": 75719050, "dataset_size": 8648603}, {"config_name": "MLQA.vi.zh", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 2989660, "num_examples": 1943}, {"name": "validation", "num_bytes": 269389, "num_examples": 184}], "download_size": 75719050, "dataset_size": 3259049}, {"config_name": "MLQA.vi.en", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 7843431, "num_examples": 5495}, {"name": "validation", "num_bytes": 719273, "num_examples": 511}], "download_size": 75719050, "dataset_size": 8562704}, {"config_name": "MLQA.vi.es", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 2866597, "num_examples": 2018}, {"name": "validation", "num_bytes": 283461, "num_examples": 189}], "download_size": 75719050, "dataset_size": 3150058}, {"config_name": "MLQA.vi.hi", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 2776664, "num_examples": 1947}, {"name": "validation", "num_bytes": 255007, "num_examples": 177}], "download_size": 75719050, "dataset_size": 3031671}, {"config_name": "MLQA.zh.ar", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 1731483, "num_examples": 1912}, {"name": "validation", "num_bytes": 175349, "num_examples": 188}], "download_size": 75719050, "dataset_size": 1906832}, {"config_name": "MLQA.zh.de", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 1390018, "num_examples": 1621}, {"name": "validation", "num_bytes": 174605, "num_examples": 190}], "download_size": 75719050, "dataset_size": 1564623}, {"config_name": "MLQA.zh.vi", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 1806186, "num_examples": 1943}, {"name": "validation", "num_bytes": 172934, "num_examples": 184}], "download_size": 75719050, "dataset_size": 1979120}, {"config_name": "MLQA.zh.zh", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 4422350, "num_examples": 5137}, {"name": "validation", "num_bytes": 443810, "num_examples": 504}], "download_size": 75719050, "dataset_size": 4866160}, {"config_name": "MLQA.zh.en", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 4450985, "num_examples": 5137}, {"name": "validation", "num_bytes": 446868, "num_examples": 504}], "download_size": 75719050, "dataset_size": 4897853}, {"config_name": "MLQA.zh.es", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 1736283, "num_examples": 1947}, {"name": "validation", "num_bytes": 138073, "num_examples": 161}], "download_size": 75719050, "dataset_size": 1874356}, {"config_name": "MLQA.zh.hi", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 1578219, "num_examples": 1767}, {"name": "validation", "num_bytes": 184401, "num_examples": 189}], "download_size": 75719050, "dataset_size": 1762620}, {"config_name": "MLQA.en.ar", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 6739219, "num_examples": 5335}, {"name": "validation", "num_bytes": 630843, "num_examples": 517}], "download_size": 75719050, "dataset_size": 7370062}, {"config_name": "MLQA.en.de", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 5056722, "num_examples": 4517}, {"name": "validation", "num_bytes": 594936, "num_examples": 512}], "download_size": 75719050, "dataset_size": 5651658}, {"config_name": "MLQA.en.vi", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 7056698, "num_examples": 5495}, {"name": "validation", "num_bytes": 640646, "num_examples": 511}], "download_size": 75719050, "dataset_size": 7697344}, {"config_name": "MLQA.en.zh", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 6539307, "num_examples": 5137}, {"name": "validation", "num_bytes": 608444, "num_examples": 504}], "download_size": 75719050, "dataset_size": 7147751}, {"config_name": "MLQA.en.en", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 14004648, "num_examples": 11590}, {"name": "validation", "num_bytes": 1329112, "num_examples": 1148}], "download_size": 75719050, "dataset_size": 15333760}, {"config_name": "MLQA.en.es", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 6179249, "num_examples": 5253}, {"name": "validation", "num_bytes": 555462, "num_examples": 500}], "download_size": 75719050, "dataset_size": 6734711}, {"config_name": "MLQA.en.hi", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 6378866, "num_examples": 4918}, {"name": "validation", "num_bytes": 623171, "num_examples": 507}], "download_size": 75719050, "dataset_size": 7002037}, {"config_name": "MLQA.es.ar", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 1740282, "num_examples": 1978}, {"name": "validation", "num_bytes": 148649, "num_examples": 161}], "download_size": 75719050, "dataset_size": 1888931}, {"config_name": "MLQA.es.de", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 1404025, "num_examples": 1776}, {"name": "validation", "num_bytes": 144186, "num_examples": 196}], "download_size": 75719050, "dataset_size": 1548211}, {"config_name": "MLQA.es.vi", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 1747969, "num_examples": 2018}, {"name": "validation", "num_bytes": 176841, "num_examples": 189}], "download_size": 75719050, "dataset_size": 1924810}, {"config_name": "MLQA.es.zh", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 1678451, "num_examples": 1947}, {"name": "validation", "num_bytes": 126646, "num_examples": 161}], "download_size": 75719050, "dataset_size": 1805097}, {"config_name": "MLQA.es.en", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 4362737, "num_examples": 5253}, {"name": "validation", "num_bytes": 419068, "num_examples": 500}], "download_size": 75719050, "dataset_size": 4781805}, {"config_name": "MLQA.es.es", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 4394333, "num_examples": 5253}, {"name": "validation", "num_bytes": 422071, "num_examples": 500}], "download_size": 75719050, "dataset_size": 4816404}, {"config_name": "MLQA.es.hi", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 1523523, "num_examples": 1723}, {"name": "validation", "num_bytes": 181834, "num_examples": 187}], "download_size": 75719050, "dataset_size": 1705357}, {"config_name": "MLQA.hi.ar", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 4445589, "num_examples": 1831}, {"name": "validation", "num_bytes": 410424, "num_examples": 186}], "download_size": 75719050, "dataset_size": 4856013}, {"config_name": "MLQA.hi.de", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 3022864, "num_examples": 1430}, {"name": "validation", "num_bytes": 301713, "num_examples": 163}], "download_size": 75719050, "dataset_size": 3324577}, {"config_name": "MLQA.hi.vi", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 4743484, "num_examples": 1947}, {"name": "validation", "num_bytes": 419106, "num_examples": 177}], "download_size": 75719050, "dataset_size": 5162590}, {"config_name": "MLQA.hi.zh", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 4354875, "num_examples": 1767}, {"name": "validation", "num_bytes": 424246, "num_examples": 189}], "download_size": 75719050, "dataset_size": 4779121}, {"config_name": "MLQA.hi.en", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 11449261, "num_examples": 4918}, {"name": "validation", "num_bytes": 1097857, "num_examples": 507}], "download_size": 75719050, "dataset_size": 12547118}, {"config_name": "MLQA.hi.es", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 3862229, "num_examples": 1723}, {"name": "validation", "num_bytes": 420402, "num_examples": 187}], "download_size": 75719050, "dataset_size": 4282631}, {"config_name": "MLQA.hi.hi", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 11810475, "num_examples": 4918}, {"name": "validation", "num_bytes": 1136784, "num_examples": 507}], "download_size": 75719050, "dataset_size": 12947259}, {"config_name": "XQuAD.ar", "features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "validation", "num_bytes": 1722799, "num_examples": 1190}], "download_size": 1582988, "dataset_size": 1722799}, {"config_name": "XQuAD.de", "features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "validation", "num_bytes": 1283301, "num_examples": 1190}], "download_size": 669810, "dataset_size": 1283301}, {"config_name": "XQuAD.vi", "features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "validation", "num_bytes": 1477239, "num_examples": 1190}], "download_size": 911401, "dataset_size": 1477239}, {"config_name": "XQuAD.zh", "features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "validation", "num_bytes": 984241, "num_examples": 1190}], "download_size": 808652, "dataset_size": 984241}, {"config_name": "XQuAD.en", "features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "validation", "num_bytes": 1116123, "num_examples": 1190}], "download_size": 609383, "dataset_size": 1116123}, {"config_name": "XQuAD.es", "features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "validation", "num_bytes": 1273499, "num_examples": 1190}], "download_size": 684322, "dataset_size": 1273499}, {"config_name": "XQuAD.hi", "features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "validation", "num_bytes": 2682975, "num_examples": 1190}], "download_size": 1680538, "dataset_size": 2682975}, {"config_name": "XQuAD.el", "features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "validation", "num_bytes": 2206690, "num_examples": 1190}], "download_size": 1918889, "dataset_size": 2206690}, {"config_name": "XQuAD.ru", "features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "validation", "num_bytes": 2136990, "num_examples": 1190}], "download_size": 1896368, "dataset_size": 2136990}, {"config_name": "XQuAD.th", "features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "validation", "num_bytes": 2854959, "num_examples": 1190}], "download_size": 1809143, "dataset_size": 2854959}, {"config_name": "XQuAD.tr", "features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "answer_start", "dtype": "int32"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "validation", "num_bytes": 1210763, "num_examples": 1190}], "download_size": 729506, "dataset_size": 1210763}, {"config_name": "bucc18.de", "features": [{"name": "source_sentence", "dtype": "string"}, {"name": "target_sentence", "dtype": "string"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 248707, "num_examples": 1038}, {"name": "test", "num_bytes": 2325701, "num_examples": 9580}], "download_size": 30719200, "dataset_size": 2574408}, {"config_name": "bucc18.fr", "features": [{"name": "source_sentence", "dtype": "string"}, {"name": "target_sentence", "dtype": "string"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 212513, "num_examples": 929}, {"name": "test", "num_bytes": 2082419, "num_examples": 9086}], "download_size": 22706544, "dataset_size": 2294932}, {"config_name": "bucc18.zh", "features": [{"name": "source_sentence", "dtype": "string"}, {"name": "target_sentence", "dtype": "string"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 55739, "num_examples": 257}, {"name": "test", "num_bytes": 415925, "num_examples": 1899}], "download_size": 7114794, "dataset_size": 471664}, {"config_name": "bucc18.ru", "features": [{"name": "source_sentence", "dtype": "string"}, {"name": "target_sentence", "dtype": "string"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 761347, "num_examples": 2374}, {"name": "test", "num_bytes": 4641678, "num_examples": 14435}], "download_size": 41354312, "dataset_size": 5403025}, {"config_name": "PAWS-X.de", "features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 500009, "num_examples": 2000}, {"name": "test", "num_bytes": 510194, "num_examples": 2000}, {"name": "train", "num_bytes": 12451883, "num_examples": 49380}], "download_size": 30282057, "dataset_size": 13462086}, {"config_name": "PAWS-X.en", "features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 478291, "num_examples": 2000}, {"name": "test", "num_bytes": 480738, "num_examples": 2000}, {"name": "train", "num_bytes": 11827719, "num_examples": 49175}], "download_size": 30282057, "dataset_size": 12786748}, {"config_name": "PAWS-X.es", "features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 494069, "num_examples": 1961}, {"name": "test", "num_bytes": 505047, "num_examples": 2000}, {"name": "train", "num_bytes": 12462107, "num_examples": 49401}], "download_size": 30282057, "dataset_size": 13461223}, {"config_name": "PAWS-X.fr", "features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 516111, "num_examples": 1988}, {"name": "test", "num_bytes": 521031, "num_examples": 2000}, {"name": "train", "num_bytes": 12948512, "num_examples": 49399}], "download_size": 30282057, "dataset_size": 13985654}, {"config_name": "PAWS-X.ja", "features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 647774, "num_examples": 2000}, {"name": "test", "num_bytes": 654640, "num_examples": 2000}, {"name": "train", "num_bytes": 14695653, "num_examples": 49401}], "download_size": 30282057, "dataset_size": 15998067}, {"config_name": "PAWS-X.ko", "features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 540787, "num_examples": 2000}, {"name": "test", "num_bytes": 547978, "num_examples": 1999}, {"name": "train", "num_bytes": 13542657, "num_examples": 49164}], "download_size": 30282057, "dataset_size": 14631422}, {"config_name": "PAWS-X.zh", "features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 459120, "num_examples": 2000}, {"name": "test", "num_bytes": 460638, "num_examples": 2000}, {"name": "train", "num_bytes": 10469712, "num_examples": 49401}], "download_size": 30282057, "dataset_size": 11389470}, {"config_name": "tatoeba.afr", "features": [{"name": "source_sentence", "dtype": "string"}, {"name": "target_sentence", "dtype": "string"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 179651, "num_examples": 1000}], "download_size": 59635, "dataset_size": 179651}, {"config_name": "tatoeba.ara", "features": [{"name": "source_sentence", "dtype": "string"}, {"name": "target_sentence", "dtype": "string"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 192666, "num_examples": 1000}], "download_size": 72650, "dataset_size": 192666}, {"config_name": "tatoeba.ben", "features": [{"name": "source_sentence", "dtype": "string"}, {"name": "target_sentence", "dtype": "string"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 211719, "num_examples": 1000}], "download_size": 91703, "dataset_size": 211719}, {"config_name": "tatoeba.bul", "features": [{"name": "source_sentence", "dtype": "string"}, {"name": "target_sentence", "dtype": "string"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 222295, "num_examples": 1000}], "download_size": 102279, "dataset_size": 222295}, {"config_name": "tatoeba.deu", "features": [{"name": "source_sentence", "dtype": "string"}, {"name": "target_sentence", "dtype": "string"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 225583, "num_examples": 1000}], "download_size": 105567, "dataset_size": 225583}, {"config_name": "tatoeba.cmn", "features": [{"name": "source_sentence", "dtype": "string"}, {"name": "target_sentence", "dtype": "string"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 188947, "num_examples": 1000}], "download_size": 68931, "dataset_size": 188947}, {"config_name": "tatoeba.ell", "features": [{"name": "source_sentence", "dtype": "string"}, {"name": "target_sentence", "dtype": "string"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 198977, "num_examples": 1000}], "download_size": 78961, "dataset_size": 198977}, {"config_name": "tatoeba.est", "features": [{"name": "source_sentence", "dtype": "string"}, {"name": "target_sentence", "dtype": "string"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 179744, "num_examples": 1000}], "download_size": 59728, "dataset_size": 179744}, {"config_name": "tatoeba.eus", "features": [{"name": "source_sentence", "dtype": "string"}, {"name": "target_sentence", "dtype": "string"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 186084, "num_examples": 1000}], "download_size": 66068, "dataset_size": 186084}, {"config_name": "tatoeba.fin", "features": [{"name": "source_sentence", "dtype": "string"}, {"name": "target_sentence", "dtype": "string"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 195685, "num_examples": 1000}], "download_size": 75669, "dataset_size": 195685}, {"config_name": "tatoeba.fra", "features": [{"name": "source_sentence", "dtype": "string"}, {"name": "target_sentence", "dtype": "string"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 200034, "num_examples": 1000}], "download_size": 80018, "dataset_size": 200034}, {"config_name": "tatoeba.heb", "features": [{"name": "source_sentence", "dtype": "string"}, {"name": "target_sentence", "dtype": "string"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 203516, "num_examples": 1000}], "download_size": 83500, "dataset_size": 203516}, {"config_name": "tatoeba.hin", "features": [{"name": "source_sentence", "dtype": "string"}, {"name": "target_sentence", "dtype": "string"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 242574, "num_examples": 1000}], "download_size": 122558, "dataset_size": 242574}, {"config_name": "tatoeba.hun", "features": [{"name": "source_sentence", "dtype": "string"}, {"name": "target_sentence", "dtype": "string"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 188905, "num_examples": 1000}], "download_size": 68889, "dataset_size": 188905}, {"config_name": "tatoeba.ind", "features": [{"name": "source_sentence", "dtype": "string"}, {"name": "target_sentence", "dtype": "string"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 194860, "num_examples": 1000}], "download_size": 74844, "dataset_size": 194860}, {"config_name": "tatoeba.ita", "features": [{"name": "source_sentence", "dtype": "string"}, {"name": "target_sentence", "dtype": "string"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 185849, "num_examples": 1000}], "download_size": 65833, "dataset_size": 185849}, {"config_name": "tatoeba.jav", "features": [{"name": "source_sentence", "dtype": "string"}, {"name": "target_sentence", "dtype": "string"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 38529, "num_examples": 205}], "download_size": 13913, "dataset_size": 38529}, {"config_name": "tatoeba.jpn", "features": [{"name": "source_sentence", "dtype": "string"}, {"name": "target_sentence", "dtype": "string"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 213099, "num_examples": 1000}], "download_size": 93083, "dataset_size": 213099}, {"config_name": "tatoeba.kat", "features": [{"name": "source_sentence", "dtype": "string"}, {"name": "target_sentence", "dtype": "string"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 161696, "num_examples": 746}], "download_size": 72160, "dataset_size": 161696}, {"config_name": "tatoeba.kaz", "features": [{"name": "source_sentence", "dtype": "string"}, {"name": "target_sentence", "dtype": "string"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 116194, "num_examples": 575}], "download_size": 47178, "dataset_size": 116194}, {"config_name": "tatoeba.kor", "features": [{"name": "source_sentence", "dtype": "string"}, {"name": "target_sentence", "dtype": "string"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 199155, "num_examples": 1000}], "download_size": 79139, "dataset_size": 199155}, {"config_name": "tatoeba.mal", "features": [{"name": "source_sentence", "dtype": "string"}, {"name": "target_sentence", "dtype": "string"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 177173, "num_examples": 687}], "download_size": 94717, "dataset_size": 177173}, {"config_name": "tatoeba.mar", "features": [{"name": "source_sentence", "dtype": "string"}, {"name": "target_sentence", "dtype": "string"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 220558, "num_examples": 1000}], "download_size": 100542, "dataset_size": 220558}, {"config_name": "tatoeba.nld", "features": [{"name": "source_sentence", "dtype": "string"}, {"name": "target_sentence", "dtype": "string"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 193279, "num_examples": 1000}], "download_size": 73263, "dataset_size": 193279}, {"config_name": "tatoeba.pes", "features": [{"name": "source_sentence", "dtype": "string"}, {"name": "target_sentence", "dtype": "string"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 213735, "num_examples": 1000}], "download_size": 93719, "dataset_size": 213735}, {"config_name": "tatoeba.por", "features": [{"name": "source_sentence", "dtype": "string"}, {"name": "target_sentence", "dtype": "string"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 195201, "num_examples": 1000}], "download_size": 75185, "dataset_size": 195201}, {"config_name": "tatoeba.rus", "features": [{"name": "source_sentence", "dtype": "string"}, {"name": "target_sentence", "dtype": "string"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 212488, "num_examples": 1000}], "download_size": 92472, "dataset_size": 212488}, {"config_name": "tatoeba.spa", "features": [{"name": "source_sentence", "dtype": "string"}, {"name": "target_sentence", "dtype": "string"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 192282, "num_examples": 1000}], "download_size": 72266, "dataset_size": 192282}, {"config_name": "tatoeba.swh", "features": [{"name": "source_sentence", "dtype": "string"}, {"name": "target_sentence", "dtype": "string"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 67283, "num_examples": 390}], "download_size": 20467, "dataset_size": 67283}, {"config_name": "tatoeba.tam", "features": [{"name": "source_sentence", "dtype": "string"}, {"name": "target_sentence", "dtype": "string"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 76297, "num_examples": 307}], "download_size": 39441, "dataset_size": 76297}, {"config_name": "tatoeba.tel", "features": [{"name": "source_sentence", "dtype": "string"}, {"name": "target_sentence", "dtype": "string"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 53239, "num_examples": 234}], "download_size": 25143, "dataset_size": 53239}, {"config_name": "tatoeba.tgl", "features": [{"name": "source_sentence", "dtype": "string"}, {"name": "target_sentence", "dtype": "string"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 188154, "num_examples": 1000}], "download_size": 68138, "dataset_size": 188154}, {"config_name": "tatoeba.tha", "features": [{"name": "source_sentence", "dtype": "string"}, {"name": "target_sentence", "dtype": "string"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 128974, "num_examples": 548}], "download_size": 63198, "dataset_size": 128974}, {"config_name": "tatoeba.tur", "features": [{"name": "source_sentence", "dtype": "string"}, {"name": "target_sentence", "dtype": "string"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 191901, "num_examples": 1000}], "download_size": 71885, "dataset_size": 191901}, {"config_name": "tatoeba.urd", "features": [{"name": "source_sentence", "dtype": "string"}, {"name": "target_sentence", "dtype": "string"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 208728, "num_examples": 1000}], "download_size": 88712, "dataset_size": 208728}, {"config_name": "tatoeba.vie", "features": [{"name": "source_sentence", "dtype": "string"}, {"name": "target_sentence", "dtype": "string"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 211423, "num_examples": 1000}], "download_size": 91407, "dataset_size": 211423}, {"config_name": "udpos.Afrikaans", "features": [{"name": "tokens", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "ADJ", "1": "ADP", "2": "ADV", "3": "AUX", "4": "CCONJ", "5": "DET", "6": "INTJ", "7": "NOUN", "8": "NUM", "9": "PART", "10": "PRON", "11": "PROPN", "12": "PUNCT", "13": "SCONJ", "14": "SYM", "15": "VERB", "16": "X"}}}}], "splits": [{"name": "validation", "num_bytes": 91302, "num_examples": 194}, {"name": "test", "num_bytes": 174256, "num_examples": 425}, {"name": "train", "num_bytes": 586382, "num_examples": 1315}], "download_size": 355216681, "dataset_size": 851940}, {"config_name": "udpos.Arabic", "features": [{"name": "tokens", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "ADJ", "1": "ADP", "2": "ADV", "3": "AUX", "4": "CCONJ", "5": "DET", "6": "INTJ", "7": "NOUN", "8": "NUM", "9": "PART", "10": "PRON", "11": "PROPN", "12": "PUNCT", "13": "SCONJ", "14": "SYM", "15": "VERB", "16": "X"}}}}], "splits": [{"name": "validation", "num_bytes": 593662, "num_examples": 909}, {"name": "test", "num_bytes": 973834, "num_examples": 1680}, {"name": "train", "num_bytes": 4453694, "num_examples": 6075}], "download_size": 355216681, "dataset_size": 6021190}, {"config_name": "udpos.Basque", "features": [{"name": "tokens", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "ADJ", "1": "ADP", "2": "ADV", "3": "AUX", "4": "CCONJ", "5": "DET", "6": "INTJ", "7": "NOUN", "8": "NUM", "9": "PART", "10": "PRON", "11": "PROPN", "12": "PUNCT", "13": "SCONJ", "14": "SYM", "15": "VERB", "16": "X"}}}}], "splits": [{"name": "validation", "num_bytes": 438683, "num_examples": 1798}, {"name": "test", "num_bytes": 444656, "num_examples": 1799}, {"name": "train", "num_bytes": 1327725, "num_examples": 5396}], "download_size": 355216681, "dataset_size": 2211064}, {"config_name": "udpos.Bulgarian", "features": [{"name": "tokens", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "ADJ", "1": "ADP", "2": "ADV", "3": "AUX", "4": "CCONJ", "5": "DET", "6": "INTJ", "7": "NOUN", "8": "NUM", "9": "PART", "10": "PRON", "11": "PROPN", "12": "PUNCT", "13": "SCONJ", "14": "SYM", "15": "VERB", "16": "X"}}}}], "splits": [{"name": "validation", "num_bytes": 347129, "num_examples": 1115}, {"name": "test", "num_bytes": 339959, "num_examples": 1116}, {"name": "train", "num_bytes": 2689779, "num_examples": 8907}], "download_size": 355216681, "dataset_size": 3376867}, {"config_name": "udpos.Dutch", "features": [{"name": "tokens", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "ADJ", "1": "ADP", "2": "ADV", "3": "AUX", "4": "CCONJ", "5": "DET", "6": "INTJ", "7": "NOUN", "8": "NUM", "9": "PART", "10": "PRON", "11": "PROPN", "12": "PUNCT", "13": "SCONJ", "14": "SYM", "15": "VERB", "16": "X"}}}}], "splits": [{"name": "validation", "num_bytes": 393604, "num_examples": 1394}, {"name": "test", "num_bytes": 397916, "num_examples": 1471}, {"name": "train", "num_bytes": 4518018, "num_examples": 18051}], "download_size": 355216681, "dataset_size": 5309538}, {"config_name": "udpos.English", "features": [{"name": "tokens", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "ADJ", "1": "ADP", "2": "ADV", "3": "AUX", "4": "CCONJ", "5": "DET", "6": "INTJ", "7": "NOUN", "8": "NUM", "9": "PART", "10": "PRON", "11": "PROPN", "12": "PUNCT", "13": "SCONJ", "14": "SYM", "15": "VERB", "16": "X"}}}}], "splits": [{"name": "validation", "num_bytes": 1042052, "num_examples": 3974}, {"name": "test", "num_bytes": 1421160, "num_examples": 5440}, {"name": "train", "num_bytes": 6225545, "num_examples": 21253}], "download_size": 355216681, "dataset_size": 8688757}, {"config_name": "udpos.Estonian", "features": [{"name": "tokens", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "ADJ", "1": "ADP", "2": "ADV", "3": "AUX", "4": "CCONJ", "5": "DET", "6": "INTJ", "7": "NOUN", "8": "NUM", "9": "PART", "10": "PRON", "11": "PROPN", "12": "PUNCT", "13": "SCONJ", "14": "SYM", "15": "VERB", "16": "X"}}}}], "splits": [{"name": "validation", "num_bytes": 814183, "num_examples": 3125}, {"name": "test", "num_bytes": 1065713, "num_examples": 3760}, {"name": "train", "num_bytes": 6614929, "num_examples": 25749}], "download_size": 355216681, "dataset_size": 8494825}, {"config_name": "udpos.Finnish", "features": [{"name": "tokens", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "ADJ", "1": "ADP", "2": "ADV", "3": "AUX", "4": "CCONJ", "5": "DET", "6": "INTJ", "7": "NOUN", "8": "NUM", "9": "PART", "10": "PRON", "11": "PROPN", "12": "PUNCT", "13": "SCONJ", "14": "SYM", "15": "VERB", "16": "X"}}}}], "splits": [{"name": "validation", "num_bytes": 656658, "num_examples": 3239}, {"name": "test", "num_bytes": 1025738, "num_examples": 4422}, {"name": "train", "num_bytes": 5613742, "num_examples": 27198}], "download_size": 355216681, "dataset_size": 7296138}, {"config_name": "udpos.French", "features": [{"name": "tokens", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "ADJ", "1": "ADP", "2": "ADV", "3": "AUX", "4": "CCONJ", "5": "DET", "6": "INTJ", "7": "NOUN", "8": "NUM", "9": "PART", "10": "PRON", "11": "PROPN", "12": "PUNCT", "13": "SCONJ", "14": "SYM", "15": "VERB", "16": "X"}}}}], "splits": [{"name": "validation", "num_bytes": 1294108, "num_examples": 5979}, {"name": "test", "num_bytes": 1731061, "num_examples": 9465}, {"name": "train", "num_bytes": 10118993, "num_examples": 47308}], "download_size": 355216681, "dataset_size": 13144162}, {"config_name": "udpos.German", "features": [{"name": "tokens", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "ADJ", "1": "ADP", "2": "ADV", "3": "AUX", "4": "CCONJ", "5": "DET", "6": "INTJ", "7": "NOUN", "8": "NUM", "9": "PART", "10": "PRON", "11": "PROPN", "12": "PUNCT", "13": "SCONJ", "14": "SYM", "15": "VERB", "16": "X"}}}}], "splits": [{"name": "validation", "num_bytes": 6044862, "num_examples": 19233}, {"name": "test", "num_bytes": 7345899, "num_examples": 22458}, {"name": "train", "num_bytes": 54773981, "num_examples": 166849}], "download_size": 355216681, "dataset_size": 68164742}, {"config_name": "udpos.Greek", "features": [{"name": "tokens", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "ADJ", "1": "ADP", "2": "ADV", "3": "AUX", "4": "CCONJ", "5": "DET", "6": "INTJ", "7": "NOUN", "8": "NUM", "9": "PART", "10": "PRON", "11": "PROPN", "12": "PUNCT", "13": "SCONJ", "14": "SYM", "15": "VERB", "16": "X"}}}}], "splits": [{"name": "validation", "num_bytes": 1062459, "num_examples": 2559}, {"name": "test", "num_bytes": 1028677, "num_examples": 2809}, {"name": "train", "num_bytes": 8932140, "num_examples": 28152}], "download_size": 355216681, "dataset_size": 11023276}, {"config_name": "udpos.Hebrew", "features": [{"name": "tokens", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "ADJ", "1": "ADP", "2": "ADV", "3": "AUX", "4": "CCONJ", "5": "DET", "6": "INTJ", "7": "NOUN", "8": "NUM", "9": "PART", "10": "PRON", "11": "PROPN", "12": "PUNCT", "13": "SCONJ", "14": "SYM", "15": "VERB", "16": "X"}}}}], "splits": [{"name": "validation", "num_bytes": 210025, "num_examples": 484}, {"name": "test", "num_bytes": 223877, "num_examples": 491}, {"name": "train", "num_bytes": 2505703, "num_examples": 5241}], "download_size": 355216681, "dataset_size": 2939605}, {"config_name": "udpos.Hindi", "features": [{"name": "tokens", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "ADJ", "1": "ADP", "2": "ADV", "3": "AUX", "4": "CCONJ", "5": "DET", "6": "INTJ", "7": "NOUN", "8": "NUM", "9": "PART", "10": "PRON", "11": "PROPN", "12": "PUNCT", "13": "SCONJ", "14": "SYM", "15": "VERB", "16": "X"}}}}], "splits": [{"name": "validation", "num_bytes": 839714, "num_examples": 1659}, {"name": "test", "num_bytes": 1400237, "num_examples": 2684}, {"name": "train", "num_bytes": 6690274, "num_examples": 13304}], "download_size": 355216681, "dataset_size": 8930225}, {"config_name": "udpos.Hungarian", "features": [{"name": "tokens", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "ADJ", "1": "ADP", "2": "ADV", "3": "AUX", "4": "CCONJ", "5": "DET", "6": "INTJ", "7": "NOUN", "8": "NUM", "9": "PART", "10": "PRON", "11": "PROPN", "12": "PUNCT", "13": "SCONJ", "14": "SYM", "15": "VERB", "16": "X"}}}}], "splits": [{"name": "validation", "num_bytes": 215891, "num_examples": 441}, {"name": "test", "num_bytes": 193740, "num_examples": 449}, {"name": "train", "num_bytes": 372238, "num_examples": 910}], "download_size": 355216681, "dataset_size": 781869}, {"config_name": "udpos.Indonesian", "features": [{"name": "tokens", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "ADJ", "1": "ADP", "2": "ADV", "3": "AUX", "4": "CCONJ", "5": "DET", "6": "INTJ", "7": "NOUN", "8": "NUM", "9": "PART", "10": "PRON", "11": "PROPN", "12": "PUNCT", "13": "SCONJ", "14": "SYM", "15": "VERB", "16": "X"}}}}], "splits": [{"name": "validation", "num_bytes": 220875, "num_examples": 559}, {"name": "test", "num_bytes": 557113, "num_examples": 1557}, {"name": "train", "num_bytes": 1710690, "num_examples": 4477}], "download_size": 355216681, "dataset_size": 2488678}, {"config_name": "udpos.Italian", "features": [{"name": "tokens", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "ADJ", "1": "ADP", "2": "ADV", "3": "AUX", "4": "CCONJ", "5": "DET", "6": "INTJ", "7": "NOUN", "8": "NUM", "9": "PART", "10": "PRON", "11": "PROPN", "12": "PUNCT", "13": "SCONJ", "14": "SYM", "15": "VERB", "16": "X"}}}}], "splits": [{"name": "validation", "num_bytes": 989008, "num_examples": 2278}, {"name": "test", "num_bytes": 1337881, "num_examples": 3518}, {"name": "train", "num_bytes": 11299329, "num_examples": 29685}], "download_size": 355216681, "dataset_size": 13626218}, {"config_name": "udpos.Japanese", "features": [{"name": "tokens", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "ADJ", "1": "ADP", "2": "ADV", "3": "AUX", "4": "CCONJ", "5": "DET", "6": "INTJ", "7": "NOUN", "8": "NUM", "9": "PART", "10": "PRON", "11": "PROPN", "12": "PUNCT", "13": "SCONJ", "14": "SYM", "15": "VERB", "16": "X"}}}}], "splits": [{"name": "validation", "num_bytes": 200368, "num_examples": 511}, {"name": "test", "num_bytes": 928914, "num_examples": 2372}, {"name": "train", "num_bytes": 2792963, "num_examples": 7125}], "download_size": 355216681, "dataset_size": 3922245}, {"config_name": "udpos.Kazakh", "features": [{"name": "tokens", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "ADJ", "1": "ADP", "2": "ADV", "3": "AUX", "4": "CCONJ", "5": "DET", "6": "INTJ", "7": "NOUN", "8": "NUM", "9": "PART", "10": "PRON", "11": "PROPN", "12": "PUNCT", "13": "SCONJ", "14": "SYM", "15": "VERB", "16": "X"}}}}], "splits": [{"name": "test", "num_bytes": 228936, "num_examples": 1047}, {"name": "train", "num_bytes": 11450, "num_examples": 31}], "download_size": 355216681, "dataset_size": 240386}, {"config_name": "udpos.Korean", "features": [{"name": "tokens", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "ADJ", "1": "ADP", "2": "ADV", "3": "AUX", "4": "CCONJ", "5": "DET", "6": "INTJ", "7": "NOUN", "8": "NUM", "9": "PART", "10": "PRON", "11": "PROPN", "12": "PUNCT", "13": "SCONJ", "14": "SYM", "15": "VERB", "16": "X"}}}}], "splits": [{"name": "validation", "num_bytes": 782599, "num_examples": 3016}, {"name": "test", "num_bytes": 1162551, "num_examples": 4276}, {"name": "train", "num_bytes": 7341303, "num_examples": 27410}], "download_size": 355216681, "dataset_size": 9286453}, {"config_name": "udpos.Chinese", "features": [{"name": "tokens", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "ADJ", "1": "ADP", "2": "ADV", "3": "AUX", "4": "CCONJ", "5": "DET", "6": "INTJ", "7": "NOUN", "8": "NUM", "9": "PART", "10": "PRON", "11": "PROPN", "12": "PUNCT", "13": "SCONJ", "14": "SYM", "15": "VERB", "16": "X"}}}}], "splits": [{"name": "validation", "num_bytes": 594460, "num_examples": 3038}, {"name": "test", "num_bytes": 1236063, "num_examples": 5528}, {"name": "train", "num_bytes": 4218915, "num_examples": 18998}], "download_size": 355216681, "dataset_size": 6049438}, {"config_name": "udpos.Marathi", "features": [{"name": "tokens", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "ADJ", "1": "ADP", "2": "ADV", "3": "AUX", "4": "CCONJ", "5": "DET", "6": "INTJ", "7": "NOUN", "8": "NUM", "9": "PART", "10": "PRON", "11": "PROPN", "12": "PUNCT", "13": "SCONJ", "14": "SYM", "15": "VERB", "16": "X"}}}}], "splits": [{"name": "validation", "num_bytes": 8509, "num_examples": 46}, {"name": "test", "num_bytes": 7883, "num_examples": 47}, {"name": "train", "num_bytes": 59035, "num_examples": 373}], "download_size": 355216681, "dataset_size": 75427}, {"config_name": "udpos.Persian", "features": [{"name": "tokens", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "ADJ", "1": "ADP", "2": "ADV", "3": "AUX", "4": "CCONJ", "5": "DET", "6": "INTJ", "7": "NOUN", "8": "NUM", "9": "PART", "10": "PRON", "11": "PROPN", "12": "PUNCT", "13": "SCONJ", "14": "SYM", "15": "VERB", "16": "X"}}}}], "splits": [{"name": "validation", "num_bytes": 317065, "num_examples": 599}, {"name": "test", "num_bytes": 320695, "num_examples": 600}, {"name": "train", "num_bytes": 2400788, "num_examples": 4798}], "download_size": 355216681, "dataset_size": 3038548}, {"config_name": "udpos.Portuguese", "features": [{"name": "tokens", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "ADJ", "1": "ADP", "2": "ADV", "3": "AUX", "4": "CCONJ", "5": "DET", "6": "INTJ", "7": "NOUN", "8": "NUM", "9": "PART", "10": "PRON", "11": "PROPN", "12": "PUNCT", "13": "SCONJ", "14": "SYM", "15": "VERB", "16": "X"}}}}], "splits": [{"name": "validation", "num_bytes": 712409, "num_examples": 1770}, {"name": "test", "num_bytes": 1082594, "num_examples": 2681}, {"name": "train", "num_bytes": 7669580, "num_examples": 17992}], "download_size": 355216681, "dataset_size": 9464583}, {"config_name": "udpos.Russian", "features": [{"name": "tokens", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "ADJ", "1": "ADP", "2": "ADV", "3": "AUX", "4": "CCONJ", "5": "DET", "6": "INTJ", "7": "NOUN", "8": "NUM", "9": "PART", "10": "PRON", "11": "PROPN", "12": "PUNCT", "13": "SCONJ", "14": "SYM", "15": "VERB", "16": "X"}}}}], "splits": [{"name": "validation", "num_bytes": 3457043, "num_examples": 9960}, {"name": "test", "num_bytes": 4236717, "num_examples": 11336}, {"name": "train", "num_bytes": 24230182, "num_examples": 67435}], "download_size": 355216681, "dataset_size": 31923942}, {"config_name": "udpos.Spanish", "features": [{"name": "tokens", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "ADJ", "1": "ADP", "2": "ADV", "3": "AUX", "4": "CCONJ", "5": "DET", "6": "INTJ", "7": "NOUN", "8": "NUM", "9": "PART", "10": "PRON", "11": "PROPN", "12": "PUNCT", "13": "SCONJ", "14": "SYM", "15": "VERB", "16": "X"}}}}], "splits": [{"name": "validation", "num_bytes": 1498777, "num_examples": 3054}, {"name": "test", "num_bytes": 1476512, "num_examples": 3147}, {"name": "train", "num_bytes": 13858442, "num_examples": 28492}], "download_size": 355216681, "dataset_size": 16833731}, {"config_name": "udpos.Tagalog", "features": [{"name": "tokens", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "ADJ", "1": "ADP", "2": "ADV", "3": "AUX", "4": "CCONJ", "5": "DET", "6": "INTJ", "7": "NOUN", "8": "NUM", "9": "PART", "10": "PRON", "11": "PROPN", "12": "PUNCT", "13": "SCONJ", "14": "SYM", "15": "VERB", "16": "X"}}}}], "splits": [{"name": "test", "num_bytes": 5165, "num_examples": 55}], "download_size": 355216681, "dataset_size": 5165}, {"config_name": "udpos.Tamil", "features": [{"name": "tokens", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "ADJ", "1": "ADP", "2": "ADV", "3": "AUX", "4": "CCONJ", "5": "DET", "6": "INTJ", "7": "NOUN", "8": "NUM", "9": "PART", "10": "PRON", "11": "PROPN", "12": "PUNCT", "13": "SCONJ", "14": "SYM", "15": "VERB", "16": "X"}}}}], "splits": [{"name": "validation", "num_bytes": 40043, "num_examples": 80}, {"name": "test", "num_bytes": 62378, "num_examples": 120}, {"name": "train", "num_bytes": 202608, "num_examples": 400}], "download_size": 355216681, "dataset_size": 305029}, {"config_name": "udpos.Telugu", "features": [{"name": "tokens", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "ADJ", "1": "ADP", "2": "ADV", "3": "AUX", "4": "CCONJ", "5": "DET", "6": "INTJ", "7": "NOUN", "8": "NUM", "9": "PART", "10": "PRON", "11": "PROPN", "12": "PUNCT", "13": "SCONJ", "14": "SYM", "15": "VERB", "16": "X"}}}}], "splits": [{"name": "validation", "num_bytes": 18002, "num_examples": 131}, {"name": "test", "num_bytes": 19587, "num_examples": 146}, {"name": "train", "num_bytes": 138061, "num_examples": 1051}], "download_size": 355216681, "dataset_size": 175650}, {"config_name": "udpos.Thai", "features": [{"name": "tokens", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "ADJ", "1": "ADP", "2": "ADV", "3": "AUX", "4": "CCONJ", "5": "DET", "6": "INTJ", "7": "NOUN", "8": "NUM", "9": "PART", "10": "PRON", "11": "PROPN", "12": "PUNCT", "13": "SCONJ", "14": "SYM", "15": "VERB", "16": "X"}}}}], "splits": [{"name": "test", "num_bytes": 561348, "num_examples": 1000}], "download_size": 355216681, "dataset_size": 561348}, {"config_name": "udpos.Turkish", "features": [{"name": "tokens", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "ADJ", "1": "ADP", "2": "ADV", "3": "AUX", "4": "CCONJ", "5": "DET", "6": "INTJ", "7": "NOUN", "8": "NUM", "9": "PART", "10": "PRON", "11": "PROPN", "12": "PUNCT", "13": "SCONJ", "14": "SYM", "15": "VERB", "16": "X"}}}}], "splits": [{"name": "validation", "num_bytes": 186467, "num_examples": 988}, {"name": "test", "num_bytes": 827394, "num_examples": 4785}, {"name": "train", "num_bytes": 704417, "num_examples": 3664}], "download_size": 355216681, "dataset_size": 1718278}, {"config_name": "udpos.Urdu", "features": [{"name": "tokens", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "ADJ", "1": "ADP", "2": "ADV", "3": "AUX", "4": "CCONJ", "5": "DET", "6": "INTJ", "7": "NOUN", "8": "NUM", "9": "PART", "10": "PRON", "11": "PROPN", "12": "PUNCT", "13": "SCONJ", "14": "SYM", "15": "VERB", "16": "X"}}}}], "splits": [{"name": "validation", "num_bytes": 284273, "num_examples": 552}, {"name": "test", "num_bytes": 288565, "num_examples": 535}, {"name": "train", "num_bytes": 2107374, "num_examples": 4043}], "download_size": 355216681, "dataset_size": 2680212}, {"config_name": "udpos.Vietnamese", "features": [{"name": "tokens", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "ADJ", "1": "ADP", "2": "ADV", "3": "AUX", "4": "CCONJ", "5": "DET", "6": "INTJ", "7": "NOUN", "8": "NUM", "9": "PART", "10": "PRON", "11": "PROPN", "12": "PUNCT", "13": "SCONJ", "14": "SYM", "15": "VERB", "16": "X"}}}}], "splits": [{"name": "validation", "num_bytes": 206200, "num_examples": 800}, {"name": "test", "num_bytes": 214075, "num_examples": 800}, {"name": "train", "num_bytes": 367347, "num_examples": 1400}], "download_size": 355216681, "dataset_size": 787622}, {"config_name": "udpos.Yoruba", "features": [{"name": "tokens", "sequence": "string"}, {"name": "pos_tags", "sequence": {"class_label": {"names": {"0": "ADJ", "1": "ADP", "2": "ADV", "3": "AUX", "4": "CCONJ", "5": "DET", "6": "INTJ", "7": "NOUN", "8": "NUM", "9": "PART", "10": "PRON", "11": "PROPN", "12": "PUNCT", "13": "SCONJ", "14": "SYM", "15": "VERB", "16": "X"}}}}], "splits": [{"name": "test", "num_bytes": 44668, "num_examples": 100}], "download_size": 355216681, "dataset_size": 44668}]}

2024-01-18T11:18:48+00:00

TAGS #task_categories-multiple-choice #task_categories-question-answering #task_categories-token-classification #task_categories-text-classification #task_categories-text-retrieval #task_ids-multiple-choice-qa #task_ids-extractive-qa #task_ids-open-domain-qa #task_ids-natural-language-inference #task_ids-named-entity-recognition #task_ids-part-of-speech #annotations_creators-found #language_creators-found #multilinguality-multilingual #multilinguality-translation #size_categories-n<1K #size_categories-1K<n<10K #size_categories-10K<n<100K #size_categories-100K<n<1M #source_datasets-extended|xnli #source_datasets-extended|paws-x #source_datasets-extended|wikiann #source_datasets-extended|xquad #source_datasets-extended|mlqa #source_datasets-extended|tydiqa #source_datasets-extended|tatoeba #source_datasets-extended|squad #language-Afrikaans #language-Arabic #language-Bulgarian #language-Bengali #language-German #language-Modern Greek (1453-) #language-English #language-Spanish #language-Estonian #language-Basque #language-Persian #language-Finnish #language-French #language-Hebrew #language-Hindi #language-Hungarian #language-Indonesian #language-Italian #language-Japanese #language-Javanese #language-Georgian #language-Kazakh #language-Korean #language-Malayalam #language-Marathi #language-Malay (macrolanguage) #language-Burmese #language-Dutch #language-Portuguese #language-Russian #language-Swahili (macrolanguage) #language-Tamil #language-Telugu #language-Thai #language-Tagalog #language-Turkish #language-Urdu #language-Vietnamese #language-Yoruba #language-Chinese #license-apache-2.0 #license-cc-by-4.0 #license-cc-by-2.0 #license-cc-by-sa-4.0 #license-other #license-cc-by-nc-4.0 #parallel-sentence-retrieval #paraphrase-identification #arxiv-2003.11080 #region-us

Dataset Card for "xtreme" ========================= Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: * Point of Contact: * Size of downloaded dataset files: 15.88 GB * Size of the generated dataset: 1.08 GB * Total amount of disk used: 16.96 GB ### Dataset Summary The Cross-lingual Natural Language Inference (XNLI) corpus is a crowd-sourced collection of 5,000 test and 2,500 dev pairs for the MultiNLI corpus. The pairs are annotated with textual entailment and translated into 14 languages: French, Spanish, German, Greek, Bulgarian, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, Hindi, Swahili and Urdu. This results in 112.5k annotated pairs. Each premise can be associated with the corresponding hypothesis in the 15 languages, summing up to more than 1.5M combinations. The corpus is made to evaluate how to perform inference in any language (including low-resources ones like Swahili or Urdu) when only English NLI data is available at training time. One solution is cross-lingual sentence encoding, for which XNLI is an evaluation benchmark. The Cross-lingual TRansfer Evaluation of Multilingual Encoders (XTREME) benchmark is a benchmark for the evaluation of the cross-lingual generalization ability of pre-trained multilingual models. It covers 40 typologically diverse languages (spanning 12 language families) and includes nine tasks that collectively require reasoning about different levels of syntax and semantics. The languages in XTREME are selected to maximize language diversity, coverage in existing tasks, and availability of training data. Among these are many under-studied languages, such as the Dravidian languages Tamil (spoken in southern India, Sri Lanka, and Singapore), Telugu and Malayalam (spoken mainly in southern India), and the Niger-Congo languages Swahili and Yoruba, spoken in Africa. ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### URL * Size of downloaded dataset files: 75.72 MB * Size of the generated dataset: 9.20 MB * Total amount of disk used: 84.91 MB An example of 'validation' looks as follows. #### URL * Size of downloaded dataset files: 75.72 MB * Size of the generated dataset: 2.55 MB * Total amount of disk used: 78.27 MB An example of 'validation' looks as follows. #### URL * Size of downloaded dataset files: 75.72 MB * Size of the generated dataset: 9.04 MB * Total amount of disk used: 84.76 MB An example of 'validation' looks as follows. #### URL * Size of downloaded dataset files: 75.72 MB * Size of the generated dataset: 3.27 MB * Total amount of disk used: 78.99 MB An example of 'validation' looks as follows. #### URL * Size of downloaded dataset files: 75.72 MB * Size of the generated dataset: 3.32 MB * Total amount of disk used: 79.04 MB An example of 'validation' looks as follows. ### Data Fields The data fields are the same among all splits. #### URL * 'id': a 'string' feature. * 'title': a 'string' feature. * 'context': a 'string' feature. * 'question': a 'string' feature. * 'answers': a dictionary feature containing: + 'answer\_start': a 'int32' feature. + 'text': a 'string' feature. #### URL * 'id': a 'string' feature. * 'title': a 'string' feature. * 'context': a 'string' feature. * 'question': a 'string' feature. * 'answers': a dictionary feature containing: + 'answer\_start': a 'int32' feature. + 'text': a 'string' feature. #### URL * 'id': a 'string' feature. * 'title': a 'string' feature. * 'context': a 'string' feature. * 'question': a 'string' feature. * 'answers': a dictionary feature containing: + 'answer\_start': a 'int32' feature. + 'text': a 'string' feature. #### URL * 'id': a 'string' feature. * 'title': a 'string' feature. * 'context': a 'string' feature. * 'question': a 'string' feature. * 'answers': a dictionary feature containing: + 'answer\_start': a 'int32' feature. + 'text': a 'string' feature. #### URL * 'id': a 'string' feature. * 'title': a 'string' feature. * 'context': a 'string' feature. * 'question': a 'string' feature. * 'answers': a dictionary feature containing: + 'answer\_start': a 'int32' feature. + 'text': a 'string' feature. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @thomwolf, @jplu, @lewtun, @lvwerra, @lhoestq, @patrickvonplaten, @mariamabarham for adding this dataset.

067bd1b85b2a38dd73642c3a42d97b08e340aa18

# Dataset Card for YahooAnswersQa ## 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:** [Add homepage URL here if available (unless it's a GitHub repository)]() - **Repository:** [If the dataset is hosted on github or has a github homepage, add URL here]() - **Paper:** [If the dataset was introduced by a paper or there was a paper written describing the dataset, add URL here (landing page for Arxiv paper preferred)]() - **Leaderboard:** [If the dataset supports an active leaderboard, add link here]() - **Point of Contact:** [If known, name and email of at least one person the reader can contact for questions about the dataset.]() ### 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 [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.

yahoo_answers_qa

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["extended|other-yahoo-webscope-l6"], "task_categories": ["question-answering"], "task_ids": ["open-domain-qa"], "pretty_name": "YahooAnswersQa", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "nbestanswers", "sequence": "string"}, {"name": "main_category", "dtype": "string"}], "config_name": "yahoo_answers_qa", "splits": [{"name": "train", "num_bytes": 138540510, "num_examples": 87362}], "download_size": 49411220, "dataset_size": 138540510}}

2024-01-18T11:18:49+00:00

TAGS #task_categories-question-answering #task_ids-open-domain-qa #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|other-yahoo-webscope-l6 #language-English #license-unknown #region-us

# Dataset Card for YahooAnswersQa ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: [Add homepage URL here if available (unless it's a GitHub repository)]() - Repository: [If the dataset is hosted on github or has a github homepage, add URL here]() - Paper: [If the dataset was introduced by a paper or there was a paper written describing the dataset, add URL here (landing page for Arxiv paper preferred)]() - Leaderboard: [If the dataset supports an active leaderboard, add link here]() - Point of Contact: [If known, name and email of at least one person the reader can contact for questions about the dataset.]() ### Dataset Summary ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @patil-suraj for adding this dataset.

78fccffa043240c80e17a6b1da724f5a1057e8e5

# Dataset Card for "Yahoo Answers Topics" ## 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:** [Add homepage URL here if available (unless it's a GitHub repository)]() - **Repository:** https://github.com/LC-John/Yahoo-Answers-Topic-Classification-Dataset - **Paper:** [If the dataset was introduced by a paper or there was a paper written describing the dataset, add URL here (landing page for Arxiv paper preferred)]() - **Leaderboard:** [If the dataset supports an active leaderboard, add link here]() - **Point of Contact:** [If known, name and email of at least one person the reader can contact for questions about the dataset.]() ### 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 [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.

yahoo_answers_topics

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["extended|other-yahoo-answers-corpus"], "task_categories": ["text-classification"], "task_ids": ["topic-classification"], "pretty_name": "YahooAnswersTopics", "dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "topic", "dtype": {"class_label": {"names": {"0": "Society & Culture", "1": "Science & Mathematics", "2": "Health", "3": "Education & Reference", "4": "Computers & Internet", "5": "Sports", "6": "Business & Finance", "7": "Entertainment & Music", "8": "Family & Relationships", "9": "Politics & Government"}}}}, {"name": "question_title", "dtype": "string"}, {"name": "question_content", "dtype": "string"}, {"name": "best_answer", "dtype": "string"}], "config_name": "yahoo_answers_topics", "splits": [{"name": "train", "num_bytes": 760460695, "num_examples": 1400000}, {"name": "test", "num_bytes": 32661362, "num_examples": 60000}], "download_size": 319476345, "dataset_size": 793122057}, "train-eval-index": [{"config": "yahoo_answers_topics", "task": "text-classification", "task_id": "multi_class_classification", "splits": {"train_split": "train", "eval_split": "test"}, "col_mapping": {"question_content": "text", "topic": "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"}}]}]}

2024-01-18T11:18:50+00:00

TAGS #task_categories-text-classification #task_ids-topic-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-extended|other-yahoo-answers-corpus #language-English #license-unknown #region-us

# Dataset Card for "Yahoo Answers Topics" ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: [Add homepage URL here if available (unless it's a GitHub repository)]() - Repository: URL - Paper: [If the dataset was introduced by a paper or there was a paper written describing the dataset, add URL here (landing page for Arxiv paper preferred)]() - Leaderboard: [If the dataset supports an active leaderboard, add link here]() - Point of Contact: [If known, name and email of at least one person the reader can contact for questions about the dataset.]() ### Dataset Summary ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @patil-suraj for adding this dataset.

c10a301dd13257c3e5c307a0bee8a8826cb397e4

# Dataset Card for "yelp_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://course.fast.ai/datasets](https://course.fast.ai/datasets) - **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:** 166.38 MB - **Size of the generated dataset:** 441.74 MB - **Total amount of disk used:** 608.12 MB ### Dataset Summary Large Yelp Review Dataset. This is a dataset for binary sentiment classification. We provide a set of 560,000 highly polar yelp reviews for training, and 38,000 for testing. ORIGIN The Yelp reviews dataset consists of reviews from Yelp. It is extracted from the Yelp Dataset Challenge 2015 data. For more information, please refer to http://www.yelp.com/dataset_challenge The Yelp reviews polarity dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu) from the above dataset. 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). DESCRIPTION The Yelp reviews polarity dataset is constructed by considering stars 1 and 2 negative, and 3 and 4 positive. For each polarity 280,000 training samples and 19,000 testing samples are take randomly. In total there are 560,000 trainig samples and 38,000 testing samples. Negative polarity is class 1, and positive class 2. The files train.csv and test.csv contain all the training samples as comma-sparated values. There are 2 columns in them, corresponding to class index (1 and 2) and review 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 " ". ### 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:** 166.38 MB - **Size of the generated dataset:** 441.74 MB - **Total amount of disk used:** 608.12 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "label": 0, "text": "\"Unfortunately, the frustration of being Dr. Goldberg's patient is a repeat of the experience I've had with so many other doctor..." } ``` ### Data Fields The data fields are the same among all splits. #### plain_text - `text`: a `string` feature. - `label`: a classification label, with possible values including `1` (0), `2` (1). ### Data Splits | name |train |test | |----------|-----:|----:| |plain_text|560000|38000| ## 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{zhangCharacterlevelConvolutionalNetworks2015, archivePrefix = {arXiv}, eprinttype = {arxiv}, eprint = {1509.01626}, primaryClass = {cs}, title = {Character-Level {{Convolutional Networks}} for {{Text Classification}}}, abstract = {This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered against traditional models such as bag of words, n-grams and their TFIDF variants, and deep learning models such as word-based ConvNets and recurrent neural networks.}, journal = {arXiv:1509.01626 [cs]}, author = {Zhang, Xiang and Zhao, Junbo and LeCun, Yann}, month = sep, year = {2015}, } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@julien-c](https://github.com/julien-c) for adding this dataset.

yelp_polarity

2022-03-02T23:29:22+00:00

{"language": ["en"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "paperswithcode_id": "yelp-review-polarity", "pretty_name": "YelpPolarity", "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "1", "1": "2"}}}}], "config_name": "plain_text", "splits": [{"name": "train", "num_bytes": 413558837, "num_examples": 560000}, {"name": "test", "num_bytes": 27962097, "num_examples": 38000}], "download_size": 166373201, "dataset_size": 441520934}, "train-eval-index": [{"config": "plain_text", "task": "text-classification", "task_id": "binary_classification", "splits": {"train_split": "train", "eval_split": "test"}, "col_mapping": {"text": "text", "label": "target"}, "metrics": [{"type": "accuracy", "name": "Accuracy"}, {"type": "f1", "name": "F1 binary", "args": {"average": "binary"}}, {"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"}}]}]}

2024-01-18T11:18:51+00:00

TAGS #task_categories-text-classification #task_ids-sentiment-classification #language-English #arxiv-1509.01626 #region-us

Dataset Card for "yelp\_polarity" ================================= Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: * Point of Contact: * Size of downloaded dataset files: 166.38 MB * Size of the generated dataset: 441.74 MB * Total amount of disk used: 608.12 MB ### Dataset Summary Large Yelp Review Dataset. This is a dataset for binary sentiment classification. We provide a set of 560,000 highly polar yelp reviews for training, and 38,000 for testing. ORIGIN The Yelp reviews dataset consists of reviews from Yelp. It is extracted from the Yelp Dataset Challenge 2015 data. For more information, please refer to URL The Yelp reviews polarity dataset is constructed by Xiang Zhang (URL@URL) from the above dataset. 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). DESCRIPTION The Yelp reviews polarity dataset is constructed by considering stars 1 and 2 negative, and 3 and 4 positive. For each polarity 280,000 training samples and 19,000 testing samples are take randomly. In total there are 560,000 trainig samples and 38,000 testing samples. Negative polarity is class 1, and positive class 2. The files URL and URL contain all the training samples as comma-sparated values. There are 2 columns in them, corresponding to class index (1 and 2) and review 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 " ". ### Supported Tasks and Leaderboards ### Languages Dataset Structure ----------------- ### Data Instances #### plain\_text * Size of downloaded dataset files: 166.38 MB * Size of the generated dataset: 441.74 MB * Total amount of disk used: 608.12 MB An example of 'train' looks as follows. ### Data Fields The data fields are the same among all splits. #### plain\_text * 'text': a 'string' feature. * 'label': a classification label, with possible values including '1' (0), '2' (1). ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions Thanks to @patrickvonplaten, @lewtun, @mariamabarham, @thomwolf, @julien-c for adding this dataset.

c1f9ee939b7d05667af864ee1cb066393154bf85

--- # 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.

yelp_review_full

2022-03-02T23:29:22+00:00

{"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": {"config_name": "yelp_review_full", "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"}], "splits": [{"name": "train", "num_bytes": 483811554, "num_examples": 650000}, {"name": "test", "num_bytes": 37271188, "num_examples": 50000}], "download_size": 322952369, "dataset_size": 521082742}, "configs": [{"config_name": "yelp_review_full", "data_files": [{"split": "train", "path": "yelp_review_full/train-*"}, {"split": "test", "path": "yelp_review_full/test-*"}], "default": true}], "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"}}]}]}

2024-01-04T17:14:53+00:00

TAGS #task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-other #arxiv-1509.01626 #region-us

--- # Dataset Card for YelpReviewFull ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: Yelp - Repository: Crepe - Paper: Character-level Convolutional Networks for Text Classification - Point of Contact: Xiang Zhang ### 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: ### 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 (URL@URL) 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 #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information You can check the official yelp-dataset-agreement. 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 for adding this dataset.

9ce94e2ad802ef2558cb8acdd0509cebce1c38c5

# Dataset Card for Yoruba BBC News Topic Classification dataset (yoruba_bbc_topics) ## 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:** https://github.com/uds-lsv/transfer-distant-transformer-african - **Paper:** https://www.aclweb.org/anthology/2020.emnlp-main.204/ - **Leaderboard:** - - **Point of Contact:** Michael A. Hedderich and David Adelani {mhedderich, didelani} (at) lsv.uni-saarland.de ### Dataset Summary A news headline topic classification dataset, similar to AG-news, for Yorùbá. The news headlines were collected from [BBC Yoruba](https://www.bbc.com/yoruba). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Yorùbá (ISO 639-1: yo) ## Dataset Structure ### Data Instances An instance consists of a news title sentence and the corresponding topic label as well as publishing information (date and website id). ### Data Fields - `news_title`: A news title. - `label`: The label describing the topic of the news title. Can be one of the following classes: africa, entertainment, health, nigeria, politics, sport or world. - `date`: The publication date (in Yorùbá). - `bbc_url_id`: The identifier of the article in the BBC URL. ### 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 [@michael-aloys](https://github.com/michael-aloys) for adding this dataset.

yoruba_bbc_topics

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["yo"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["topic-classification"], "pretty_name": "Yoruba Bbc News Topic Classification Dataset (YorubaBbcTopics)", "dataset_info": {"features": [{"name": "news_title", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "africa", "1": "entertainment", "2": "health", "3": "nigeria", "4": "politics", "5": "sport", "6": "world"}}}}, {"name": "date", "dtype": "string"}, {"name": "bbc_url_id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 197117, "num_examples": 1340}, {"name": "validation", "num_bytes": 27771, "num_examples": 189}, {"name": "test", "num_bytes": 55652, "num_examples": 379}], "download_size": 265480, "dataset_size": 280540}}

2024-01-18T11:18:52+00:00

TAGS #task_categories-text-classification #task_ids-topic-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Yoruba #license-unknown #region-us

# Dataset Card for Yoruba BBC News Topic Classification dataset (yoruba_bbc_topics) ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: - - Repository: URL - Paper: URL - Leaderboard: - - Point of Contact: Michael A. Hedderich and David Adelani {mhedderich, didelani} (at) URL ### Dataset Summary A news headline topic classification dataset, similar to AG-news, for Yorùbá. The news headlines were collected from BBC Yoruba. ### Supported Tasks and Leaderboards ### Languages Yorùbá (ISO 639-1: yo) ## Dataset Structure ### Data Instances An instance consists of a news title sentence and the corresponding topic label as well as publishing information (date and website id). ### Data Fields - 'news_title': A news title. - 'label': The label describing the topic of the news title. Can be one of the following classes: africa, entertainment, health, nigeria, politics, sport or world. - 'date': The publication date (in Yorùbá). - 'bbc_url_id': The identifier of the article in the BBC URL. ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @michael-aloys for adding this dataset.

5f5c76d9ea6a6a7205e53c842c61af719aa8b8c5

# Dataset Card for Yoruba GV NER 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:** - **Repository:** [Yoruba GV NER](https://github.com/ajesujoba/YorubaTwi-Embedding/tree/master/Yoruba/Yoruba-NER) - **Paper:** https://www.aclweb.org/anthology/2020.lrec-1.335/ - **Leaderboard:** - **Point of Contact:** [David Adelani](mailto:didelani@lsv.uni-saarland.de) ### Dataset Summary The Yoruba GV NER is a named entity recognition (NER) dataset for Yorùbá language based on the [Global Voices news](https://yo.globalvoices.org/) corpus. Global Voices (GV) is a multilingual news platform with articles contributed by journalists, translators, bloggers, and human rights activists from around the world with a coverage of over 50 languages. Most of the texts used in creating the Yoruba GV NER are translations from other languages to Yorùbá. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The language supported is Yorùbá. ## Dataset Structure ### Data Instances A data point consists of sentences seperated by empty line and tab-seperated tokens and tags. {'id': '0', 'ner_tags': [B-LOC, 0, 0, 0, 0], 'tokens': ['Tanzania', 'fi', 'Ajìjàgbara', 'Ọmọ', 'Orílẹ̀-èdèe'] } ### Data Fields - `id`: id of the sample - `tokens`: the tokens of the example text - `ner_tags`: the NER tags of each token The NER tags correspond to this list: ``` "O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-DATE", "I-DATE", ``` The NER tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word. There are four types of phrases: person names (PER), organizations (ORG), locations (LOC) and dates & times (DATE). (O) is used for tokens not considered part of any named entity. ### Data Splits Training (19,421 tokens), validation (2,695 tokens) and test split (5,235 tokens) ## Dataset Creation ### Curation Rationale The data was created to help introduce resources to new language - Yorùbá. [More Information Needed] ### Source Data #### Initial Data Collection and Normalization The dataset is based on the news domain and was crawled from [Global Voices Yorùbá news](https://yo.globalvoices.org/). [More Information Needed] #### Who are the source language producers? The dataset contributed by journalists, translators, bloggers, and human rights activists from around the world. Most of the texts used in creating the Yoruba GV NER are translations from other languages to Yorùbá [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? The data was annotated by Jesujoba Alabi and David Adelani for the paper: [Massive vs. Curated Embeddings for Low-Resourced Languages: the case of Yorùbá and Twi](https://www.aclweb.org/anthology/2020.lrec-1.335/). [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 annotated data sets were developed by students of Saarland University, Saarbrücken, Germany . ### Licensing Information The data is under the [Creative Commons Attribution 3.0 ](https://creativecommons.org/licenses/by/3.0/) ### Citation Information ``` @inproceedings{alabi-etal-2020-massive, title = "Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of {Y}or{\`u}b{\'a} and {T}wi", author = "Alabi, Jesujoba and Amponsah-Kaakyire, Kwabena and Adelani, David and Espa{\~n}a-Bonet, Cristina", 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-1.335", pages = "2754--2762", language = "English", ISBN = "979-10-95546-34-4", } ``` ### Contributions Thanks to [@dadelani](https://github.com/dadelani) for adding this dataset.

yoruba_gv_ner

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["yo"], "license": ["cc-by-3.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "Yoruba GV NER Corpus", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC", "7": "B-DATE", "8": "I-DATE"}}}}], "config_name": "yoruba_gv_ner", "splits": [{"name": "train", "num_bytes": 358885, "num_examples": 817}, {"name": "validation", "num_bytes": 50161, "num_examples": 117}, {"name": "test", "num_bytes": 96518, "num_examples": 237}], "download_size": 254347, "dataset_size": 505564}}

2024-01-18T11:18:53+00:00

TAGS #task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Yoruba #license-cc-by-3.0 #region-us

# Dataset Card for Yoruba GV NER Corpus ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: - Repository: Yoruba GV NER - Paper: URL - Leaderboard: - Point of Contact: David Adelani ### Dataset Summary The Yoruba GV NER is a named entity recognition (NER) dataset for Yorùbá language based on the Global Voices news corpus. Global Voices (GV) is a multilingual news platform with articles contributed by journalists, translators, bloggers, and human rights activists from around the world with a coverage of over 50 languages. Most of the texts used in creating the Yoruba GV NER are translations from other languages to Yorùbá. ### Supported Tasks and Leaderboards ### Languages The language supported is Yorùbá. ## Dataset Structure ### Data Instances A data point consists of sentences seperated by empty line and tab-seperated tokens and tags. {'id': '0', 'ner_tags': [B-LOC, 0, 0, 0, 0], 'tokens': ['Tanzania', 'fi', 'Ajìjàgbara', 'Ọmọ', 'Orílẹ̀-èdèe'] } ### Data Fields - 'id': id of the sample - 'tokens': the tokens of the example text - 'ner_tags': the NER tags of each token The NER tags correspond to this list: The NER tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word. There are four types of phrases: person names (PER), organizations (ORG), locations (LOC) and dates & times (DATE). (O) is used for tokens not considered part of any named entity. ### Data Splits Training (19,421 tokens), validation (2,695 tokens) and test split (5,235 tokens) ## Dataset Creation ### Curation Rationale The data was created to help introduce resources to new language - Yorùbá. ### Source Data #### Initial Data Collection and Normalization The dataset is based on the news domain and was crawled from Global Voices Yorùbá news. #### Who are the source language producers? The dataset contributed by journalists, translators, bloggers, and human rights activists from around the world. Most of the texts used in creating the Yoruba GV NER are translations from other languages to Yorùbá ### Annotations #### Annotation process #### Who are the annotators? The data was annotated by Jesujoba Alabi and David Adelani for the paper: Massive vs. Curated Embeddings for Low-Resourced Languages: the case of Yorùbá and Twi. ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators The annotated data sets were developed by students of Saarland University, Saarbrücken, Germany . ### Licensing Information The data is under the Creative Commons Attribution 3.0 ### Contributions Thanks to @dadelani for adding this dataset.

a9aa4093a83833364a4f12dc6ccd2adb9c13e3b2

# Dataset Card for Yorùbá Text C3 ## 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/ajesujoba/YorubaTwi-Embedding/ - **Paper:** https://aclanthology.org/2020.lrec-1.335/ - **Leaderboard:** - **Point of Contact:** [Jesujoba Alabi](mailto:alabijesujoba@gmail.com) ### Dataset Summary Yorùbá Text C3 was collected from various sources from the web (Bible, JW300, books, news articles, wikipedia, etc) to compare pre-trained word embeddings (Fasttext and BERT) and embeddings and embeddings trained on curated Yorùbá Texts. The dataset consists of clean texts (i.e texts with proper Yorùbá diacritics) like the Bible & JW300 and noisy texts ( with incorrect or absent diacritics) from other online sources like Wikipedia, BBC Yorùbá, and VON Yorùbá ### Supported Tasks and Leaderboards For training word embeddings and language models on Yoruba texts. ### Languages The language supported is Yorùbá. ## Dataset Structure ### Data Instances A data point is a sentence in each line. { 'text': 'lílo àkàbà — ǹjẹ́ o máa ń ṣe àyẹ̀wò wọ̀nyí tó lè dáàbò bò ẹ́' } ### Data Fields - `text`: a `string` feature. a sentence text per line ### Data Splits Contains only the training split. ## Dataset Creation ### Curation Rationale The data was created to help introduce resources to new language - Yorùbá. ### Source Data #### Initial Data Collection and Normalization The dataset comes from various sources of the web like Bible, JW300, books, news articles, wikipedia, etc. See Table 1 in the [paper](https://www.aclweb.org/anthology/2020.lrec-1.335/) for the summary of the dataset and statistics #### Who are the source language producers? [Jehovah Witness](https://www.jw.org/yo/) (JW300) [Yorùbá Bible](http://www.bible.com/) [Yorùbá Wikipedia](dumps.wikimedia.org/yowiki) [BBC Yorùbá](bbc.com/yoruba) [VON Yorùbá](https://von.gov.ng/) [Global Voices Yorùbá]( yo.globalvoices.org) And other sources, see https://www.aclweb.org/anthology/2020.lrec-1.335/ ### 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 The dataset is biased to the religion domain (Christianity) because of the inclusion of JW300 and the Bible. ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The data sets were curated by Jesujoba Alabi and David Adelani, students of Saarland University, Saarbrücken, Germany . ### Licensing Information The data is under the [Creative Commons Attribution-NonCommercial 4.0 ](https://creativecommons.org/licenses/by-nc/4.0/legalcode) ### Citation Information ``` @inproceedings{alabi-etal-2020-massive, title = "Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of {Y}or{\`u}b{\'a} and {T}wi", author = "Alabi, Jesujoba and Amponsah-Kaakyire, Kwabena and Adelani, David and Espa{\~n}a-Bonet, Cristina", 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-1.335", pages = "2754--2762", abstract = "The success of several architectures to learn semantic representations from unannotated text and the availability of these kind of texts in online multilingual resources such as Wikipedia has facilitated the massive and automatic creation of resources for multiple languages. The evaluation of such resources is usually done for the high-resourced languages, where one has a smorgasbord of tasks and test sets to evaluate on. For low-resourced languages, the evaluation is more difficult and normally ignored, with the hope that the impressive capability of deep learning architectures to learn (multilingual) representations in the high-resourced setting holds in the low-resourced setting too. In this paper we focus on two African languages, Yor{\`u}b{\'a} and Twi, and compare the word embeddings obtained in this way, with word embeddings obtained from curated corpora and a language-dependent processing. We analyse the noise in the publicly available corpora, collect high quality and noisy data for the two languages and quantify the improvements that depend not only on the amount of data but on the quality too. We also use different architectures that learn word representations both from surface forms and characters to further exploit all the available information which showed to be important for these languages. For the evaluation, we manually translate the wordsim-353 word pairs dataset from English into Yor{\`u}b{\'a} and Twi. We extend the analysis to contextual word embeddings and evaluate multilingual BERT on a named entity recognition task. For this, we annotate with named entities the Global Voices corpus for Yor{\`u}b{\'a}. As output of the work, we provide corpora, embeddings and the test suits for both languages.", language = "English", ISBN = "979-10-95546-34-4", } ``` ### Contributions Thanks to [@dadelani](https://github.com/dadelani) for adding this dataset.

yoruba_text_c3

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["yo"], "license": ["cc-by-nc-4.0"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-modeling", "masked-language-modeling"], "pretty_name": "Yor\u00f9b\u00e1 Text C3", "dataset_info": [{"config_name": "plain_text", "features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 77094396, "num_examples": 562238}], "download_size": 75407454, "dataset_size": 77094396}, {"config_name": "yoruba_text_c3", "features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 77094396, "num_examples": 562238}], "download_size": 75407454, "dataset_size": 77094396}]}

2023-06-16T14:06:58+00:00

TAGS #task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-Yoruba #license-cc-by-nc-4.0 #region-us

# Dataset Card for Yorùbá Text C3 ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Repository: URL - Paper: URL - Leaderboard: - Point of Contact: Jesujoba Alabi ### Dataset Summary Yorùbá Text C3 was collected from various sources from the web (Bible, JW300, books, news articles, wikipedia, etc) to compare pre-trained word embeddings (Fasttext and BERT) and embeddings and embeddings trained on curated Yorùbá Texts. The dataset consists of clean texts (i.e texts with proper Yorùbá diacritics) like the Bible & JW300 and noisy texts ( with incorrect or absent diacritics) from other online sources like Wikipedia, BBC Yorùbá, and VON Yorùbá ### Supported Tasks and Leaderboards For training word embeddings and language models on Yoruba texts. ### Languages The language supported is Yorùbá. ## Dataset Structure ### Data Instances A data point is a sentence in each line. { 'text': 'lílo àkàbà — ǹjẹ́ o máa ń ṣe àyẹ̀wò wọ̀nyí tó lè dáàbò bò ẹ́' } ### Data Fields - 'text': a 'string' feature. a sentence text per line ### Data Splits Contains only the training split. ## Dataset Creation ### Curation Rationale The data was created to help introduce resources to new language - Yorùbá. ### Source Data #### Initial Data Collection and Normalization The dataset comes from various sources of the web like Bible, JW300, books, news articles, wikipedia, etc. See Table 1 in the paper for the summary of the dataset and statistics #### Who are the source language producers? Jehovah Witness (JW300) Yorùbá Bible Yorùbá Wikipedia BBC Yorùbá VON Yorùbá Global Voices Yorùbá And other sources, see URL ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases The dataset is biased to the religion domain (Christianity) because of the inclusion of JW300 and the Bible. ### Other Known Limitations ## Additional Information ### Dataset Curators The data sets were curated by Jesujoba Alabi and David Adelani, students of Saarland University, Saarbrücken, Germany . ### Licensing Information The data is under the Creative Commons Attribution-NonCommercial 4.0 ### Contributions Thanks to @dadelani for adding this dataset.

3e9d9210f5d748d4b209f7c74c8e62e131d907f3

# Dataset Card for wordsim-353 in Yorùbá (yoruba_wordsim353) ## 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:** https://github.com/ajesujoba/YorubaTwi-Embedding - **Paper:** https://www.aclweb.org/anthology/2020.lrec-1.335/ - **Leaderboard:** - - **Point of Contact:** Jesujoba Alabi ( jesujobaoluwadara.alabi (at) dfki.de ) and David Adelani ( didelani (at) lsv.uni-saarland.de ) ### Dataset Summary A translation of the word pair similarity dataset wordsim-353 to Yorùbá. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Yorùbá (ISO 639-1: yo) ## Dataset Structure ### Data Instances An instance consists of a pair of words as well as their similarity. The dataset contains both the original English words (from wordsim-353) as well as their translation to Yorùbá. ### Data Fields - `english1`: the first word of the pair; the original English word - `english2`: the second word of the pair; the original English word - `yoruba1`: the first word of the pair; translation to Yorùbá - `yoruba2`: the second word of the pair; translation to Yorùbá - `similarity`: similarity rating according to the English dataset ### 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 [@michael-aloys](https://github.com/michael-aloys) for adding this dataset.

yoruba_wordsim353

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced"], "language_creators": ["expert-generated"], "language": ["en", "yo"], "license": ["unknown"], "multilinguality": ["multilingual"], "size_categories": ["n<1K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["text-scoring", "semantic-similarity-scoring"], "pretty_name": "Wordsim-353 In Yor\u00f9b\u00e1 (YorubaWordsim353)", "dataset_info": {"features": [{"name": "english1", "dtype": "string"}, {"name": "english2", "dtype": "string"}, {"name": "yoruba1", "dtype": "string"}, {"name": "yoruba2", "dtype": "string"}, {"name": "similarity", "dtype": "float32"}], "splits": [{"name": "test", "num_bytes": 19299, "num_examples": 353}], "download_size": 17039, "dataset_size": 19299}}

2024-01-18T11:18:55+00:00

TAGS #task_categories-text-classification #task_ids-text-scoring #task_ids-semantic-similarity-scoring #annotations_creators-crowdsourced #language_creators-expert-generated #multilinguality-multilingual #size_categories-n<1K #source_datasets-original #language-English #language-Yoruba #license-unknown #region-us

# Dataset Card for wordsim-353 in Yorùbá (yoruba_wordsim353) ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: - - Repository: URL - Paper: URL - Leaderboard: - - Point of Contact: Jesujoba Alabi ( URL (at) URL ) and David Adelani ( didelani (at) URL ) ### Dataset Summary A translation of the word pair similarity dataset wordsim-353 to Yorùbá. ### Supported Tasks and Leaderboards ### Languages Yorùbá (ISO 639-1: yo) ## Dataset Structure ### Data Instances An instance consists of a pair of words as well as their similarity. The dataset contains both the original English words (from wordsim-353) as well as their translation to Yorùbá. ### Data Fields - 'english1': the first word of the pair; the original English word - 'english2': the second word of the pair; the original English word - 'yoruba1': the first word of the pair; translation to Yorùbá - 'yoruba2': the second word of the pair; translation to Yorùbá - 'similarity': similarity rating according to the English dataset ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @michael-aloys for adding this dataset.

e413f330a48375ccb3be0980d43337089d83771b

# Dataset Card for YouTube Caption Corrections ## 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/2dot71mily/youtube_captions_corrections - **Repository:** https://github.com/2dot71mily/youtube_captions_corrections - **Paper:** [N/A] - **Leaderboard:** [N/A] - **Point of Contact:** Emily McMilin ### Dataset Summary This dataset is built from pairs of YouTube captions where both an auto-generated and a manually-corrected caption are available for a single specified language. It currently only in English, but scripts at repo support other languages. The motivation for creating it was from viewing errors in auto-generated captions at a recent virtual conference, with the hope that there could be some way to help correct those errors. The dataset in the repo at https://github.com/2dot71mily/youtube_captions_corrections records in a non-destructive manner all the differences between an auto-generated and a manually-corrected caption for thousands of videos. The dataset here focuses on the subset of those differences which are mutual and have the same size in token length difference, which means it excludes token insertion or deletion differences between the two captions. Therefore dataset here remains a non-destructive representation of the original auto-generated captions, but excludes some of the differences that are found in the manually-corrected captions. ### Supported Tasks and Leaderboards - `token-classification`: The tokens in `default_seq` are from the auto-generated YouTube captions. If `diff_type` is labeled greater than `0` at a given index, then the associated token in same index in the `default_seq` was found to be different to the token in the manually-corrected YouTube caption, and therefore we assume it is an error. A model can be trained to learn when there are errors in the auto-generated captions. - `slot-filling`: The `correction_seq` is sparsely populated with tokens from the manually-corrected YouTube captions in the locations where there was found to be a difference to the token in the auto-generated YouTube captions. These 'incorrect' tokens in the `default_seq` can be masked in the locations where `diff_type` is labeled greater than `0`, so that a model can be trained to hopefully find a better word to fill in, rather than the 'incorrect' one. End to end, the models could maybe first identify and then replace (with suitable alternatives) errors in YouTube and other auto-generated captions that are lacking manual corrections ### Languages English ## Dataset Structure ### Data Instances If `diff_type` is labeled greater than `0` at a given index, then the associated token in same index in the `default_seq` was found to have a difference to the token in the manually-corrected YouTube caption. The `correction_seq` is sparsely populated with tokens from the manually-corrected YouTube captions at those locations of differences. `diff_type` labels for tokens are as follows: 0: No difference 1: Case based difference, e.g. `hello` vs `Hello` 2: Punctuation difference, e.g. `hello` vs `hello` 3: Case and punctuation difference, e.g. `hello` vs `Hello,` 4: Word difference with same stem, e.g. `thank` vs `thanked` 5: Digit difference, e.g. `2` vs `two` 6: Intra-word punctuation difference, e.g. `autogenerated` vs `auto-generated` 7: Unknown type of difference, e.g. `laughter` vs `draft` 8: Reserved for unspecified difference { 'video_titles': '_QUEXsHfsA0', 'default_seq': ['you', 'see', "it's", 'a', 'laughter', 'but', 'by', 'the', 'time', 'you', 'see', 'this', 'it', "won't", 'be', 'so', 'we', 'have', 'a', 'big'] 'correction_seq': ['', 'see,', '', '', 'draft,', '', '', '', '', '', 'read', 'this,', '', '', 'be.', 'So', '', '', '', ''] 'diff_type': [0, 2, 0, 0, 7, 0, 0, 0, 0, 0, 7, 2, 0, 0, 2, 1, 0, 0, 0, 0] } ### Data Fields - 'video_ids': Unique ID used by YouTube for each video. Can paste into `https://www.youtube.com/watch?v=<{video_ids}` to see video - 'default_seq': Tokenized auto-generated YouTube captions for the video - 'correction_seq': Tokenized manually-corrected YouTube captions only at those locations, where there is a difference between the auto-generated and manually-corrected captions - 'diff_type': A value greater than `0` at every token where there is a difference between the auto-generated and manually-corrected captions ### Data Splits No data splits ## Dataset Creation ### Curation Rationale It was created after viewing errors in auto-generated captions at a recent virtual conference, with the hope that there could be some way to help correct those errors. ### Source Data #### Initial Data Collection and Normalization All captions are requested via `googleapiclient` and `youtube_transcript_api` at the `channel_id` and language granularity, using scripts written at https://github.com/2dot71mily/youtube_captions_corrections. The captions are tokenized on spaces and the manually-corrected sequence has here been reduced to only include differences between it and the auto-generated sequence. #### Who are the source language producers? Auto-generated scripts are from YouTube and the manually-corrected scripts are from creators, and any support they may have (e.g. community or software support) ### Annotations #### Annotation process Scripts at repo, https://github.com/2dot71mily/youtube_captions_corrections take a diff of the two captions and use this to create annotations. #### Who are the annotators? YouTube creators, and any support they may have (e.g. community or software support) ### Personal and Sensitive Information All content publicly available on YouTube ## 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 Emily McMilin ### Licensing Information MIT License ### Citation Information https://github.com/2dot71mily/youtube_captions_corrections ### Contributions Thanks to [@2dot71mily](https://github.com/2dot71mily) for adding this dataset.

youtube_caption_corrections

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated", "machine-generated"], "language_creators": ["machine-generated"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["other", "text-generation", "fill-mask"], "task_ids": ["slot-filling"], "pretty_name": "YouTube Caption Corrections", "tags": ["token-classification-of-text-errors"], "dataset_info": {"features": [{"name": "video_ids", "dtype": "string"}, {"name": "default_seq", "sequence": "string"}, {"name": "correction_seq", "sequence": "string"}, {"name": "diff_type", "sequence": {"class_label": {"names": {"0": "NO_DIFF", "1": "CASE_DIFF", "2": "PUNCUATION_DIFF", "3": "CASE_AND_PUNCUATION_DIFF", "4": "STEM_BASED_DIFF", "5": "DIGIT_DIFF", "6": "INTRAWORD_PUNC_DIFF", "7": "UNKNOWN_TYPE_DIFF", "8": "RESERVED_DIFF"}}}}], "splits": [{"name": "train", "num_bytes": 355978939, "num_examples": 10769}], "download_size": 222479455, "dataset_size": 355978939}}

2024-01-18T11:18:56+00:00

TAGS #task_categories-other #task_categories-text-generation #task_categories-fill-mask #task_ids-slot-filling #annotations_creators-expert-generated #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-mit #token-classification-of-text-errors #region-us

# Dataset Card for YouTube Caption Corrections ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: URL - Paper: [N/A] - Leaderboard: [N/A] - Point of Contact: Emily McMilin ### Dataset Summary This dataset is built from pairs of YouTube captions where both an auto-generated and a manually-corrected caption are available for a single specified language. It currently only in English, but scripts at repo support other languages. The motivation for creating it was from viewing errors in auto-generated captions at a recent virtual conference, with the hope that there could be some way to help correct those errors. The dataset in the repo at URL records in a non-destructive manner all the differences between an auto-generated and a manually-corrected caption for thousands of videos. The dataset here focuses on the subset of those differences which are mutual and have the same size in token length difference, which means it excludes token insertion or deletion differences between the two captions. Therefore dataset here remains a non-destructive representation of the original auto-generated captions, but excludes some of the differences that are found in the manually-corrected captions. ### Supported Tasks and Leaderboards - 'token-classification': The tokens in 'default_seq' are from the auto-generated YouTube captions. If 'diff_type' is labeled greater than '0' at a given index, then the associated token in same index in the 'default_seq' was found to be different to the token in the manually-corrected YouTube caption, and therefore we assume it is an error. A model can be trained to learn when there are errors in the auto-generated captions. - 'slot-filling': The 'correction_seq' is sparsely populated with tokens from the manually-corrected YouTube captions in the locations where there was found to be a difference to the token in the auto-generated YouTube captions. These 'incorrect' tokens in the 'default_seq' can be masked in the locations where 'diff_type' is labeled greater than '0', so that a model can be trained to hopefully find a better word to fill in, rather than the 'incorrect' one. End to end, the models could maybe first identify and then replace (with suitable alternatives) errors in YouTube and other auto-generated captions that are lacking manual corrections ### Languages English ## Dataset Structure ### Data Instances If 'diff_type' is labeled greater than '0' at a given index, then the associated token in same index in the 'default_seq' was found to have a difference to the token in the manually-corrected YouTube caption. The 'correction_seq' is sparsely populated with tokens from the manually-corrected YouTube captions at those locations of differences. 'diff_type' labels for tokens are as follows: 0: No difference 1: Case based difference, e.g. 'hello' vs 'Hello' 2: Punctuation difference, e.g. 'hello' vs 'hello' 3: Case and punctuation difference, e.g. 'hello' vs 'Hello,' 4: Word difference with same stem, e.g. 'thank' vs 'thanked' 5: Digit difference, e.g. '2' vs 'two' 6: Intra-word punctuation difference, e.g. 'autogenerated' vs 'auto-generated' 7: Unknown type of difference, e.g. 'laughter' vs 'draft' 8: Reserved for unspecified difference { 'video_titles': '_QUEXsHfsA0', 'default_seq': ['you', 'see', "it's", 'a', 'laughter', 'but', 'by', 'the', 'time', 'you', 'see', 'this', 'it', "won't", 'be', 'so', 'we', 'have', 'a', 'big'] 'correction_seq': ['', 'see,', '', '', 'draft,', '', '', '', '', '', 'read', 'this,', '', '', 'be.', 'So', '', '', '', ''] 'diff_type': [0, 2, 0, 0, 7, 0, 0, 0, 0, 0, 7, 2, 0, 0, 2, 1, 0, 0, 0, 0] } ### Data Fields - 'video_ids': Unique ID used by YouTube for each video. Can paste into 'URL to see video - 'default_seq': Tokenized auto-generated YouTube captions for the video - 'correction_seq': Tokenized manually-corrected YouTube captions only at those locations, where there is a difference between the auto-generated and manually-corrected captions - 'diff_type': A value greater than '0' at every token where there is a difference between the auto-generated and manually-corrected captions ### Data Splits No data splits ## Dataset Creation ### Curation Rationale It was created after viewing errors in auto-generated captions at a recent virtual conference, with the hope that there could be some way to help correct those errors. ### Source Data #### Initial Data Collection and Normalization All captions are requested via 'googleapiclient' and 'youtube_transcript_api' at the 'channel_id' and language granularity, using scripts written at URL The captions are tokenized on spaces and the manually-corrected sequence has here been reduced to only include differences between it and the auto-generated sequence. #### Who are the source language producers? Auto-generated scripts are from YouTube and the manually-corrected scripts are from creators, and any support they may have (e.g. community or software support) ### Annotations #### Annotation process Scripts at repo, URL take a diff of the two captions and use this to create annotations. #### Who are the annotators? YouTube creators, and any support they may have (e.g. community or software support) ### Personal and Sensitive Information All content publicly available on YouTube ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators Emily McMilin ### Licensing Information MIT License URL ### Contributions Thanks to @2dot71mily for adding this dataset.

a6497d853fdbface91ea06b2e1acce5bb0cba03d

# Dataset Card for "ZEST: ZEroShot learning from Task descriptions" ## 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://allenai.org/data/zest - **Repository:** https://github.com/allenai/zest - **Paper:** https://arxiv.org/abs/2011.08115 - **Leaderboard:** https://leaderboard.allenai.org/zest/submissions/public - **Point of Contact:** ### Dataset Summary ZEST tests whether NLP systems can perform unseen tasks in a zero-shot way, given a natural language description of the task. It is an instantiation of our proposed framework "learning from task descriptions". The tasks include classification, typed entity extraction and relationship extraction, and each task is paired with 20 different annotated (input, output) examples. ZEST's structure allows us to systematically test whether models can generalize in five different ways. ### Supported Tasks and Leaderboards A [leaderboard](https://leaderboard.allenai.org/zest/submissions/public) is included with accepatbility metrics for each of the four generalization types outlined in the paper. The metrics are novel acceptability metrics also proposed by the authors. ### Languages The dataset is in English. ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale To evaluate the ability of a model to generalize to unseen tasks based only on a task description in a zero-shot manner. ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? Mechanical Turk crowdsource workers. ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? Mechanical Turk crowdsource workers. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset The dataset emphasizes a model's ability to generalize to unseen tasks with only a natural language description of the task. The long-term vision of this type of evaluation is to facilitate the creation of models which can perform arbitrary tasks with only a prompt from a non-technical user. This could broaden the frontier of what a user can ask something like a chatbot to do for them, but it is unclear how restrictions would be put in place to prevent users from prompting a system to perform unethical tasks. ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information This dataset is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). ### Citation Information ``` @inproceedings{weller-etal-2020-learning, title = "Learning from Task Descriptions", author = "Weller, Orion and Lourie, Nicholas and Gardner, Matt and Peters, Matthew", 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://www.aclweb.org/anthology/2020.emnlp-main.105", pages = "1361--1375", abstract = "Typically, machine learning systems solve new tasks by training on thousands of examples. In contrast, humans can solve new tasks by reading some instructions, with perhaps an example or two. To take a step toward closing this gap, we introduce a framework for developing NLP systems that solve new tasks after reading their descriptions, synthesizing prior work in this area. We instantiate this frame- work with a new English language dataset, ZEST, structured for task-oriented evaluation on unseen tasks. Formulating task descriptions as questions, we ensure each is general enough to apply to many possible inputs, thus comprehensively evaluating a model{'}s ability to solve each task. Moreover, the dataset{'}s structure tests specific types of systematic generalization. We find that the state-of-the-art T5 model achieves a score of 12% on ZEST, leaving a significant challenge for NLP researchers.", } ``` ### Contributions Thanks to [@joeddav](https://github.com/joeddav) for adding this dataset.

zest

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["question-answering", "token-classification"], "task_ids": ["closed-domain-qa", "extractive-qa"], "paperswithcode_id": "zest", "pretty_name": "ZEST", "tags": ["output-structure", "yes-no-qa"], "dataset_info": {"features": [{"name": "task_id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "generalization_type", "dtype": "string"}, {"name": "derives_from", "sequence": "string"}, {"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "answer", "sequence": "string"}, {"name": "all_answers", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 9588987, "num_examples": 10766}, {"name": "validation", "num_bytes": 2056804, "num_examples": 2280}, {"name": "test", "num_bytes": 9280845, "num_examples": 11980}], "download_size": 5796188, "dataset_size": 20926636}}

2024-01-18T11:18:58+00:00

TAGS #task_categories-question-answering #task_categories-token-classification #task_ids-closed-domain-qa #task_ids-extractive-qa #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-4.0 #output-structure #yes-no-qa #arxiv-2011.08115 #region-us

# Dataset Card for "ZEST: ZEroShot learning from Task descriptions" ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: URL - Paper: URL - Leaderboard: URL - Point of Contact: ### Dataset Summary ZEST tests whether NLP systems can perform unseen tasks in a zero-shot way, given a natural language description of the task. It is an instantiation of our proposed framework "learning from task descriptions". The tasks include classification, typed entity extraction and relationship extraction, and each task is paired with 20 different annotated (input, output) examples. ZEST's structure allows us to systematically test whether models can generalize in five different ways. ### Supported Tasks and Leaderboards A leaderboard is included with accepatbility metrics for each of the four generalization types outlined in the paper. The metrics are novel acceptability metrics also proposed by the authors. ### Languages The dataset is in English. ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale To evaluate the ability of a model to generalize to unseen tasks based only on a task description in a zero-shot manner. ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? Mechanical Turk crowdsource workers. ### Annotations #### Annotation process #### Who are the annotators? Mechanical Turk crowdsource workers. ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset The dataset emphasizes a model's ability to generalize to unseen tasks with only a natural language description of the task. The long-term vision of this type of evaluation is to facilitate the creation of models which can perform arbitrary tasks with only a prompt from a non-technical user. This could broaden the frontier of what a user can ask something like a chatbot to do for them, but it is unclear how restrictions would be put in place to prevent users from prompting a system to perform unethical tasks. ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information This dataset is licensed under CC BY 4.0. ### Contributions Thanks to @joeddav for adding this dataset.

7ebf0b4caa7b2ae39698a889de782c09e6f5ee56

# Dataset Card for SuperLim ## 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 Structure/Creation/Use/Additional Information](#dataset-structurecreationuseadditional-information) - [Dalaj](#dalaj) - [SweAna](#sweana) - [SweDiag](#swediag) - [SweFaq](#swefaq) - [SweFracas](#swefracas) - [SwePar](#swepar) - [SweSat](#swesat) - [SweSim](#swesim) - [SweWgr](#swewgr) - [SweWic](#swewic) - [SweWsc](#swewsc) ## Dataset Description - **Homepage:** [Språkbanken](https://spraakbanken.gu.se/en/resources/superlim) - **Repository:** / - **Paper:** / - **Leaderboard:** / - **Point of Contact:** [Contact Us](mailto:severine.verlinden@ai.se) ### Dataset Summary A standardized suite for evaluation and analysis of Swedish natural language understanding systems. ### Supported Tasks and Leaderboards Work in progress ### Languages Swedish ## Dataset Structure/Creation/Use/Additional Information ### Dalaj [dataset documentation](https://svn.spraakdata.gu.se/sb-arkiv/pub/dalaj/dalaj_documentation.tsv) ### SweAna [dataset documentation](https://svn.spraakdata.gu.se/sb-arkiv/pub/swedish_analogy/analogy_documentation_sheet.tsv) #### SweDiag work in progress ### SweFaq [dataset documentation](https://svn.spraakdata.gu.se/sb-arkiv/pub/faq/faq_documentation_sheet.tsv) ### SweFracas [dataset documentation](https://svn.spraakdata.gu.se/sb-arkiv/pub/swefracas/swefracas_documentation_sheet.tsv) ### SwePar [dataset documentation](https://svn.spraakdata.gu.se/sb-arkiv/pub/sweparaphrase/sweparaphrase_documentation.tsv) ### SweSat [dataset documentation](https://svn.spraakdata.gu.se/sb-arkiv/pub/swesat/swesat-synonyms_documentation_sheet.tsv) ### SweSim [dataset documentation](https://demo.spraakbanken.gu.se/gerlof/SuperSim/supersim-superlim_documentation_sheet.txt) ### SweWgr [dataset documentation](https://demo.spraakbanken.gu.se/gerlof/SweWinogender/swewinogender_documentation_sheet.txt) ### SweWic [dataset documentation](https://demo.spraakbanken.gu.se/gerlof/SweWiC/swewic_documentation_sheet.txt) ### SweWsc [dataset documentation](https://demo.spraakbanken.gu.se/gerlof/SweWinograd/swewinograd_documentation_sheet.txt)

AI-Sweden/SuperLim

2022-03-02T23:29:22+00:00

{"language": ["sv"], "multilinguality": ["monolingual"], "task_categories": ["question-answering", "text-classification", "sequence-modeling", "other"], "pretty_name": "SuperLim"}

2022-10-21T14:25:24+00:00

TAGS #task_categories-question-answering #task_categories-text-classification #task_categories-other #multilinguality-monolingual #language-Swedish #region-us

# Dataset Card for SuperLim ## Table of Contents - Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Structure/Creation/Use/Additional Information - Dalaj - SweAna - SweDiag - SweFaq - SweFracas - SwePar - SweSat - SweSim - SweWgr - SweWic - SweWsc ## Dataset Description - Homepage: Språkbanken - Repository: / - Paper: / - Leaderboard: / - Point of Contact: Contact Us ### Dataset Summary A standardized suite for evaluation and analysis of Swedish natural language understanding systems. ### Supported Tasks and Leaderboards Work in progress ### Languages Swedish ## Dataset Structure/Creation/Use/Additional Information ### Dalaj dataset documentation ### SweAna dataset documentation #### SweDiag work in progress ### SweFaq dataset documentation ### SweFracas dataset documentation ### SwePar dataset documentation ### SweSat dataset documentation ### SweSim dataset documentation ### SweWgr dataset documentation ### SweWic dataset documentation ### SweWsc dataset documentation

d58b8fc80437cfcbc56d5c4e07f8d2e2dd992787

Hello AI-it!

AI-it/korean-hate-speech

2022-03-02T23:29:22+00:00

{}

2021-12-20T03:00:00+00:00

TAGS #region-us

Hello AI-it!

90958ebd524a0e9154c760a463c87910c361a640

# Dataset Card for fanpage ## 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-instances) - [Data Splits](#data-instances) - [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) ## Dataset Description - **Homepage:** [Needs More Information] - **Repository:** [Needs More Information] - **Paper:** [Needs More Information] - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary Fanpage dataset, containing news articles taken from Fanpage. There are two features: - source: Input news article. - target: Summary of the article. ### Supported Tasks and Leaderboards - `abstractive-summarization`, `summarization` ### Languages The text in the dataset is in Italian ## Dataset Structure ### Data Instances [Needs More Information] ### Data Fields [Needs More Information] ### 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 [Needs More Information] ### Citation Information More details and results in [published work](https://www.mdpi.com/2078-2489/13/5/228) ``` @Article{info13050228, AUTHOR = {Landro, Nicola and Gallo, Ignazio and La Grassa, Riccardo and Federici, Edoardo}, TITLE = {Two New Datasets for Italian-Language Abstractive Text Summarization}, JOURNAL = {Information}, VOLUME = {13}, YEAR = {2022}, NUMBER = {5}, ARTICLE-NUMBER = {228}, URL = {https://www.mdpi.com/2078-2489/13/5/228}, ISSN = {2078-2489}, ABSTRACT = {Text summarization aims to produce a short summary containing relevant parts from a given text. Due to the lack of data for abstractive summarization on low-resource languages such as Italian, we propose two new original datasets collected from two Italian news websites with multi-sentence summaries and corresponding articles, and from a dataset obtained by machine translation of a Spanish summarization dataset. These two datasets are currently the only two available in Italian for this task. To evaluate the quality of these two datasets, we used them to train a T5-base model and an mBART model, obtaining good results with both. To better evaluate the results obtained, we also compared the same models trained on automatically translated datasets, and the resulting summaries in the same training language, with the automatically translated summaries, which demonstrated the superiority of the models obtained from the proposed datasets.}, DOI = {10.3390/info13050228} } ```

ARTeLab/fanpage

2022-03-02T23:29:22+00:00

{"language": ["it"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100k"], "source_datasets": ["original"], "task_categories": ["summarization"]}

2022-11-17T02:49:54+00:00

TAGS #task_categories-summarization #multilinguality-monolingual #size_categories-10K<n<100k #source_datasets-original #language-Italian #license-unknown #region-us

# Dataset Card for fanpage ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary Fanpage dataset, containing news articles taken from Fanpage. There are two features: - source: Input news article. - target: Summary of the article. ### Supported Tasks and Leaderboards - 'abstractive-summarization', 'summarization' ### Languages The text in the dataset is in Italian ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information More details and results in published work

9f53a759360f76d70a736c0c887a6711259467c5

# Dataset Card for ilpost ## 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-instances) - [Data Splits](#data-instances) - [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) ## Dataset Description - **Homepage:** [Needs More Information] - **Repository:** [Needs More Information] - **Paper:** [Needs More Information] - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary IlPost dataset, containing news articles taken from IlPost. There are two features: - source: Input news article. - target: Summary of the article. ### Supported Tasks and Leaderboards - `abstractive-summarization`, `summarization` ### Languages The text in the dataset is in Italian ## Dataset Structure ### Data Instances [Needs More Information] ### Data Fields [Needs More Information] ### 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 [Needs More Information] ### Citation Information More details and results in [published work](https://www.mdpi.com/2078-2489/13/5/228) ``` @Article{info13050228, AUTHOR = {Landro, Nicola and Gallo, Ignazio and La Grassa, Riccardo and Federici, Edoardo}, TITLE = {Two New Datasets for Italian-Language Abstractive Text Summarization}, JOURNAL = {Information}, VOLUME = {13}, YEAR = {2022}, NUMBER = {5}, ARTICLE-NUMBER = {228}, URL = {https://www.mdpi.com/2078-2489/13/5/228}, ISSN = {2078-2489}, ABSTRACT = {Text summarization aims to produce a short summary containing relevant parts from a given text. Due to the lack of data for abstractive summarization on low-resource languages such as Italian, we propose two new original datasets collected from two Italian news websites with multi-sentence summaries and corresponding articles, and from a dataset obtained by machine translation of a Spanish summarization dataset. These two datasets are currently the only two available in Italian for this task. To evaluate the quality of these two datasets, we used them to train a T5-base model and an mBART model, obtaining good results with both. To better evaluate the results obtained, we also compared the same models trained on automatically translated datasets, and the resulting summaries in the same training language, with the automatically translated summaries, which demonstrated the superiority of the models obtained from the proposed datasets.}, DOI = {10.3390/info13050228} } ```

ARTeLab/ilpost

2022-03-02T23:29:22+00:00

{"language": ["it"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100k"], "task_categories": ["summarization"]}

2022-11-17T02:50:32+00:00

TAGS #task_categories-summarization #multilinguality-monolingual #size_categories-10K<n<100k #language-Italian #region-us

# Dataset Card for ilpost ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary IlPost dataset, containing news articles taken from IlPost. There are two features: - source: Input news article. - target: Summary of the article. ### Supported Tasks and Leaderboards - 'abstractive-summarization', 'summarization' ### Languages The text in the dataset is in Italian ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information More details and results in published work

7c4b344da4e6d25afa4b65957e65e8b75fdc8259

# Dataset Card for mlsum-it ## 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-instances) - [Data Splits](#data-instances) - [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) ## Dataset Description - **Homepage:** [https://huggingface.co/datasets/mlsum] - **Repository:** [Needs More Information] - **Paper:** [Needs More Information] - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary The MLSum-it dataset is the translated version (Helsinki-NLP/opus-mt-es-it) of the spanish portion of MLSum, containing news articles taken from BBC/mundo. More informations on the official dataset page [HuggingFace page](https://huggingface.co/datasets/mlsum). There are two features: - source: Input news article. - target: Summary of the article. ### Supported Tasks and Leaderboards - `abstractive-summarization`, `summarization` ### Languages The text in the dataset is in Italian ## Dataset Structure ### Data Instances [Needs More Information] ### Data Fields [Needs More Information] ### 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 [Needs More Information] ### Citation Information More details and results in [published work](https://www.mdpi.com/2078-2489/13/5/228) ``` @Article{info13050228, AUTHOR = {Landro, Nicola and Gallo, Ignazio and La Grassa, Riccardo and Federici, Edoardo}, TITLE = {Two New Datasets for Italian-Language Abstractive Text Summarization}, JOURNAL = {Information}, VOLUME = {13}, YEAR = {2022}, NUMBER = {5}, ARTICLE-NUMBER = {228}, URL = {https://www.mdpi.com/2078-2489/13/5/228}, ISSN = {2078-2489}, ABSTRACT = {Text summarization aims to produce a short summary containing relevant parts from a given text. Due to the lack of data for abstractive summarization on low-resource languages such as Italian, we propose two new original datasets collected from two Italian news websites with multi-sentence summaries and corresponding articles, and from a dataset obtained by machine translation of a Spanish summarization dataset. These two datasets are currently the only two available in Italian for this task. To evaluate the quality of these two datasets, we used them to train a T5-base model and an mBART model, obtaining good results with both. To better evaluate the results obtained, we also compared the same models trained on automatically translated datasets, and the resulting summaries in the same training language, with the automatically translated summaries, which demonstrated the superiority of the models obtained from the proposed datasets.}, DOI = {10.3390/info13050228} } ```

ARTeLab/mlsum-it

2022-03-02T23:29:22+00:00

{"language": ["it"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100k"], "task_categories": ["summarization"]}

2022-11-17T02:51:00+00:00

TAGS #task_categories-summarization #multilinguality-monolingual #size_categories-10K<n<100k #language-Italian #region-us

# Dataset Card for mlsum-it ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information ## Dataset Description - Homepage: [URL - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary The MLSum-it dataset is the translated version (Helsinki-NLP/opus-mt-es-it) of the spanish portion of MLSum, containing news articles taken from BBC/mundo. More informations on the official dataset page HuggingFace page. There are two features: - source: Input news article. - target: Summary of the article. ### Supported Tasks and Leaderboards - 'abstractive-summarization', 'summarization' ### Languages The text in the dataset is in Italian ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information More details and results in published work

f37c573d8d99bd0eff5c547282b990db5bc7a20e

this is a datasets about amazon reviews

ASCCCCCCCC/amazon_zh

2022-03-02T23:29:22+00:00

{"license": "apache-2.0"}

2022-02-17T02:16:59+00:00

TAGS #license-apache-2.0 #region-us

this is a datasets about amazon reviews

7af49f85461104c4e7172a55659d09ca423cd160

# Hadith-Data-Sets There are two files of Hadith, the first one for all `hadith With Tashkil and Without Tashkel` from the Nine Books that are 62,169 Hadith. The second one it `Hadith pre-processing` data, which is applyed normalization and removeing stop words and lemmatization on it <!-- ## `All Hadith Books`: All Hadith With Tashkil and Without Tashkel from the Nine Books that are 62,169 Hadith. ## `All Hadith Books_preprocessing`: All Hadith Without Tashkil which is applyed normalization and removeing stop words and lemmatization on it --> ## Number of hadiths in whole books : 62,169 |Book Name |Number Of Hadiiths| | ----------------------- |------------------| |Sahih Bukhari: | 7008| |Sahih Muslim: | 5362| |Sunan al Tirmidhi: | 3891| |Sunan al-Nasai: | 5662| |Sunan Abu Dawud: | 4590| |Sunan Ibn Maja: | 4332| |Musnad Ahmad ibn Hanbal: | 26363| |Maliks Muwatta: | 1594| |Sunan al Darami: | 3367|

Abdo1Kamr/Arabic_Hadith

2022-03-02T23:29:22+00:00

{}

2021-08-21T11:40:44+00:00

TAGS #region-us

Hadith-Data-Sets ================ There are two files of Hadith, the first one for all 'hadith With Tashkil and Without Tashkel' from the Nine Books that are 62,169 Hadith. The second one it 'Hadith pre-processing' data, which is applyed normalization and removeing stop words and lemmatization on it Number of hadiths in whole books : 62,169 -----------------------------------------

7b544c4920a8be268b48b403c188acf0a462051b

# ****Dataset Card for English quotes**** # **I-Dataset Summary** english_quotes is a dataset of all the quotes retrieved from [goodreads quotes](https://www.goodreads.com/quotes). This dataset can be used for multi-label text classification and text generation. The content of each quote is in English and concerns the domain of datasets for NLP and beyond. # **II-Supported Tasks and Leaderboards** - Multi-label text classification : The dataset can be used to train a model for text-classification, which consists of classifying quotes by author as well as by topic (using tags). Success on this task is typically measured by achieving a high or low accuracy. - Text-generation : The dataset can be used to train a model to generate quotes by fine-tuning an existing pretrained model on the corpus composed of all quotes (or quotes by author). # **III-Languages** The texts in the dataset are in English (en). # **IV-Dataset Structure** #### Data Instances A JSON-formatted example of a typical instance in the dataset: ```python {'author': 'Ralph Waldo Emerson', 'quote': '“To be yourself in a world that is constantly trying to make you something else is the greatest accomplishment.”', 'tags': ['accomplishment', 'be-yourself', 'conformity', 'individuality']} ``` #### Data Fields - **author** : The author of the quote. - **quote** : The text of the quote. - **tags**: The tags could be characterized as topics around the quote. #### Data Splits I kept the dataset as one block (train), so it can be shuffled and split by users later using methods of the hugging face dataset library like the (.train_test_split()) method. # **V-Dataset Creation** #### Curation Rationale I want to share my datasets (created by web scraping and additional cleaning treatments) with the HuggingFace community so that they can use them in NLP tasks to advance artificial intelligence. #### Source Data The source of Data is [goodreads](https://www.goodreads.com/?ref=nav_home) site: from [goodreads quotes](https://www.goodreads.com/quotes) #### Initial Data Collection and Normalization The data collection process is web scraping using BeautifulSoup and Requests libraries. The data is slightly modified after the web scraping: removing all quotes with "None" tags, and the tag "attributed-no-source" is removed from all tags, because it has not added value to the topic of the quote. #### Who are the source Data producers ? The data is machine-generated (using web scraping) and subjected to human additional treatment. below, I provide the script I created to scrape the data (as well as my additional treatment): ```python import requests from bs4 import BeautifulSoup import pandas as pd import json from collections import OrderedDict page = requests.get('https://www.goodreads.com/quotes') if page.status_code == 200: pageParsed = BeautifulSoup(page.content, 'html5lib') # Define a function that retrieves information about each HTML quote code in a dictionary form. def extract_data_quote(quote_html): quote = quote_html.find('div',{'class':'quoteText'}).get_text().strip().split('\n')[0] author = quote_html.find('span',{'class':'authorOrTitle'}).get_text().strip() if quote_html.find('div',{'class':'greyText smallText left'}) is not None: tags_list = [tag.get_text() for tag in quote_html.find('div',{'class':'greyText smallText left'}).find_all('a')] tags = list(OrderedDict.fromkeys(tags_list)) if 'attributed-no-source' in tags: tags.remove('attributed-no-source') else: tags = None data = {'quote':quote, 'author':author, 'tags':tags} return data # Define a function that retrieves all the quotes on a single page. def get_quotes_data(page_url): page = requests.get(page_url) if page.status_code == 200: pageParsed = BeautifulSoup(page.content, 'html5lib') quotes_html_page = pageParsed.find_all('div',{'class':'quoteDetails'}) return [extract_data_quote(quote_html) for quote_html in quotes_html_page] # Retrieve data from the first page. data = get_quotes_data('https://www.goodreads.com/quotes') # Retrieve data from all pages. for i in range(2,101): print(i) url = f'https://www.goodreads.com/quotes?page={i}' data_current_page = get_quotes_data(url) if data_current_page is None: continue data = data + data_current_page data_df = pd.DataFrame.from_dict(data) for i, row in data_df.iterrows(): if row['tags'] is None: data_df = data_df.drop(i) # Produce the data in a JSON format. data_df.to_json('C:/Users/Abir/Desktop/quotes.jsonl',orient="records", lines =True,force_ascii=False) # Then I used the familiar process to push it to the Hugging Face hub. ``` #### Annotations Annotations are part of the initial data collection (see the script above). # **VI-Additional Informations** #### Dataset Curators Abir ELTAIEF #### Licensing Information This work is licensed under a Creative Commons Attribution 4.0 International License (all software and libraries used for web scraping are made available under this Creative Commons Attribution license). #### Contributions Thanks to [@Abirate](https://huggingface.co/Abirate) for adding this dataset.

Abirate/english_quotes

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated", "crowdsourced"], "language": ["en"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["multi-label-classification"]}

2022-10-25T07:39:16+00:00

TAGS #task_categories-text-classification #task_ids-multi-label-classification #annotations_creators-expert-generated #language_creators-expert-generated #language_creators-crowdsourced #multilinguality-monolingual #source_datasets-original #language-English #doi-10.57967/hf/1053 #region-us

# Dataset Card for English quotes # I-Dataset Summary english_quotes is a dataset of all the quotes retrieved from goodreads quotes. This dataset can be used for multi-label text classification and text generation. The content of each quote is in English and concerns the domain of datasets for NLP and beyond. # II-Supported Tasks and Leaderboards - Multi-label text classification : The dataset can be used to train a model for text-classification, which consists of classifying quotes by author as well as by topic (using tags). Success on this task is typically measured by achieving a high or low accuracy. - Text-generation : The dataset can be used to train a model to generate quotes by fine-tuning an existing pretrained model on the corpus composed of all quotes (or quotes by author). # III-Languages The texts in the dataset are in English (en). # IV-Dataset Structure #### Data Instances A JSON-formatted example of a typical instance in the dataset: #### Data Fields - author : The author of the quote. - quote : The text of the quote. - tags: The tags could be characterized as topics around the quote. #### Data Splits I kept the dataset as one block (train), so it can be shuffled and split by users later using methods of the hugging face dataset library like the (.train_test_split()) method. # V-Dataset Creation #### Curation Rationale I want to share my datasets (created by web scraping and additional cleaning treatments) with the HuggingFace community so that they can use them in NLP tasks to advance artificial intelligence. #### Source Data The source of Data is goodreads site: from goodreads quotes #### Initial Data Collection and Normalization The data collection process is web scraping using BeautifulSoup and Requests libraries. The data is slightly modified after the web scraping: removing all quotes with "None" tags, and the tag "attributed-no-source" is removed from all tags, because it has not added value to the topic of the quote. #### Who are the source Data producers ? The data is machine-generated (using web scraping) and subjected to human additional treatment. below, I provide the script I created to scrape the data (as well as my additional treatment): #### Annotations Annotations are part of the initial data collection (see the script above). # VI-Additional Informations #### Dataset Curators Abir ELTAIEF #### Licensing Information This work is licensed under a Creative Commons Attribution 4.0 International License (all software and libraries used for web scraping are made available under this Creative Commons Attribution license). #### Contributions Thanks to @Abirate for adding this dataset.

534725e03fec6f560dbe8166e8ae3825314a6290

# ****Dataset Card for French book reviews**** # **I-Dataset Summary** The majority of review datasets are in English. There are datasets in other languages, but not many. Through this work, I would like to enrich the datasets in the French language(my mother tongue with Arabic). The data was retrieved from two French websites: [Babelio](https://www.babelio.com/) and [Critiques Libres](http://www.critiqueslibres.com/) Like Wikipedia, these two French sites are made possible by the contributions of volunteers who use the Internet to share their knowledge and reading experiences. The French book reviews is a dataset of a huge number of reader reviews on French books that ill be constantly updated over time. # **II-Supported Tasks and Leaderboards** - Multi-label text classification : The dataset can be used to train a model for text-classification, which consists of classifying reviews by label value. Success on this task is typically measured by achieving a high or low accuracy. # **III-Languages** The texts in the dataset are in French (fr). # **IV-Dataset Structure** #### Data Instances A JSON-formatted example of a typical instance in the dataset: ```python { "book_title": "La belle histoire des maths", "author": "Michel Rousselet", "reader_review": "C’est un livre impressionnant, qui inspire le respect par la qualité de sa reliure et son contenu. Je le feuillette et je découvre à chaque tour de page un thème distinct magnifiquement illustré. Très beau livre !", "rating": 4.0, "label": 1 } ``` #### Data Fields - **book_title**: The title of the book that received the reader's review, - **author** : The author of the book that received the reader's review, - **reader_review** : The text of the reader's review, - **rating**: A five-star rating system is used to rate the book read, - **label** : A post-processed field indicating if the review is positive (1), neutral(0), or negative(-1) based on the rating field. For more details, see the [Notebook of the Dataset creation](https://github.com/Abirate/Dataset_Creation_Scrapy_Project_French_book_reviews/blob/master/scrapyproject_a_to_z_dataset_book_reviews.ipynb) #### Data Splits I kept the dataset as one block (train), so it can be shuffled and split by users later using methods of the hugging face dataset library like the (.train_test_split()) method. # **V-Dataset Creation** #### Curation Rationale The majority of review datasets are in English. There are datasets in other languages, but not many. Through this work, I would like to enrich the datasets in the French language (French is my mother tongue with Arabic) and slightly contribute to advancing data science and AI, not only for English NLP tasks but for other languages around the world. French is an international language and it is gaining ground. In addition, it is the language of love. The richness of the French language, so appreciated around the world, is largely related to the richness of its culture. The most telling example is French literature, which has many world-famous writers, such as [Gustave Flaubert](https://en.wikipedia.org/wiki/Gustave_Flaubert), [Albert Camus](https://iep.utm.edu/camus/), [Victor Hugo](https://en.wikipedia.org/wiki/Victor_Hugo), [Molière](https://en.wikipedia.org/wiki/Moli%C3%A8re), [Simone de Beauvoir](https://iep.utm.edu/beauvoir/), [Antoine de Saint-Exupéry](https://en.wikipedia.org/wiki/Antoine_de_Saint-Exup%C3%A9ry): the author of "Le Petit Prince" (The Little Prince), which is still among the most translated books in literary history. And one of the world-famous quotes from this book is: "Voici mon secret. Il est très simple: on ne voit bien qu'avec le coeur. L'essentiel est invisible pour les yeux." etc. #### Source Data The source of Data is: two French websites: [Babelio](https://www.babelio.com/) and [Critiques Libres](http://www.critiqueslibres.com/) #### Initial Data Collection and Normalization The data was collected using web scraping (with Scrapy Framework) and subjected to additional data processing. For more details, see this notebook, which details the dataset creation process. [Notebook of the Dataset creation](https://github.com/Abirate/Dataset_Creation_Scrapy_Project_French_book_reviews/blob/master/scrapyproject_a_to_z_dataset_book_reviews.ipynb) **Note**: This dataset will be constantly updated to include the most recent reviews on French books by aggregating the old datasets with the updated ones in order to have a huge dataset over time. #### Who are the source Data producers ? I created the Dataset using web scraping, by building a spider and a crawler to scrape the two french web sites [Babelio](https://www.babelio.com/) and [Critiques Libres](http://www.critiqueslibres.com/) #### Annotations Annotations are part of the initial data collection (see the script above). # **VI-Additional Informations** #### Dataset Curators Abir ELTAIEF #### Licensing Information This work is licensed under [CC0: Public Domain](https://creativecommons.org/publicdomain/zero/1.0/) #### Contributions Thanks to [@Abirate](https://huggingface.co/Abirate) for creating and adding this dataset.

Abirate/french_book_reviews

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated", "crowdsourced"], "language": ["fr"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["multi-label-classification"]}

2022-08-25T18:26:48+00:00

TAGS #task_categories-text-classification #task_ids-multi-label-classification #annotations_creators-expert-generated #language_creators-expert-generated #language_creators-crowdsourced #multilinguality-monolingual #source_datasets-original #language-French #doi-10.57967/hf/1052 #region-us

# Dataset Card for French book reviews # I-Dataset Summary The majority of review datasets are in English. There are datasets in other languages, but not many. Through this work, I would like to enrich the datasets in the French language(my mother tongue with Arabic). The data was retrieved from two French websites: Babelio and Critiques Libres Like Wikipedia, these two French sites are made possible by the contributions of volunteers who use the Internet to share their knowledge and reading experiences. The French book reviews is a dataset of a huge number of reader reviews on French books that ill be constantly updated over time. # II-Supported Tasks and Leaderboards - Multi-label text classification : The dataset can be used to train a model for text-classification, which consists of classifying reviews by label value. Success on this task is typically measured by achieving a high or low accuracy. # III-Languages The texts in the dataset are in French (fr). # IV-Dataset Structure #### Data Instances A JSON-formatted example of a typical instance in the dataset: #### Data Fields - book_title: The title of the book that received the reader's review, - author : The author of the book that received the reader's review, - reader_review : The text of the reader's review, - rating: A five-star rating system is used to rate the book read, - label : A post-processed field indicating if the review is positive (1), neutral(0), or negative(-1) based on the rating field. For more details, see the Notebook of the Dataset creation #### Data Splits I kept the dataset as one block (train), so it can be shuffled and split by users later using methods of the hugging face dataset library like the (.train_test_split()) method. # V-Dataset Creation #### Curation Rationale The majority of review datasets are in English. There are datasets in other languages, but not many. Through this work, I would like to enrich the datasets in the French language (French is my mother tongue with Arabic) and slightly contribute to advancing data science and AI, not only for English NLP tasks but for other languages around the world. French is an international language and it is gaining ground. In addition, it is the language of love. The richness of the French language, so appreciated around the world, is largely related to the richness of its culture. The most telling example is French literature, which has many world-famous writers, such as Gustave Flaubert, Albert Camus, Victor Hugo, Molière, Simone de Beauvoir, Antoine de Saint-Exupéry: the author of "Le Petit Prince" (The Little Prince), which is still among the most translated books in literary history. And one of the world-famous quotes from this book is: "Voici mon secret. Il est très simple: on ne voit bien qu'avec le coeur. L'essentiel est invisible pour les yeux." etc. #### Source Data The source of Data is: two French websites: Babelio and Critiques Libres #### Initial Data Collection and Normalization The data was collected using web scraping (with Scrapy Framework) and subjected to additional data processing. For more details, see this notebook, which details the dataset creation process. Notebook of the Dataset creation Note: This dataset will be constantly updated to include the most recent reviews on French books by aggregating the old datasets with the updated ones in order to have a huge dataset over time. #### Who are the source Data producers ? I created the Dataset using web scraping, by building a spider and a crawler to scrape the two french web sites Babelio and Critiques Libres #### Annotations Annotations are part of the initial data collection (see the script above). # VI-Additional Informations #### Dataset Curators Abir ELTAIEF #### Licensing Information This work is licensed under CC0: Public Domain #### Contributions Thanks to @Abirate for creating and adding this dataset.

08ecce2f116f895feb3f2a5a5b7beeef761ea68f

# CoNaLa Dataset for Code Generation ## Table of content - [Dataset Description](#dataset-description) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) ## Dataset Descritpion This dataset has been processed for Code Generation. CMU CoNaLa, the Code/Natural Language Challenge is a joint project of the Carnegie Mellon University NeuLab and STRUDEL Lab. This dataset was designed to test systems for generating program snippets from natural language. It is avilable at https://conala-corpus.github.io/ , and this is about 13k records from the full corpus of about 600k examples. ### Languages English ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "intent": "convert a list to a dictionary in python", "snippet": "b = dict(zip(a[0::2], a[1::2]))" }, { "intent": "python - sort a list of nested lists", "snippet": "l.sort(key=sum_nested)" } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "intent": "Value(dtype='string', id=None)", "snippet": "Value(dtype='string', id=None)" } ``` ### Dataset Splits This dataset is split into a train, validation and test split. The split sizes are as follow: | Split name | Num samples | | ------------ | ------------------- | | train | 11125 | | valid | 1237 | | test | 500 |

AhmedSSoliman/CoNaLa

2022-03-02T23:29:22+00:00

{"task_categories": ["Code Generation", "Translation", "Text2Text generation"]}

2022-01-22T09:34:19+00:00

TAGS #region-us

CoNaLa Dataset for Code Generation ================================== Table of content ---------------- * Dataset Description + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits Dataset Descritpion ------------------- This dataset has been processed for Code Generation. CMU CoNaLa, the Code/Natural Language Challenge is a joint project of the Carnegie Mellon University NeuLab and STRUDEL Lab. This dataset was designed to test systems for generating program snippets from natural language. It is avilable at URL , and this is about 13k records from the full corpus of about 600k examples. ### Languages English Dataset Structure ----------------- ### Data Instances A sample from this dataset looks as follows: ### Dataset Fields The dataset has the following fields (also called "features"): ### Dataset Splits This dataset is split into a train, validation and test split. The split sizes are as follow:

532717d4f35316fe8b8eef21d07f9cf12c4ab537

## Description **BAAD16** is an **Authorship Attribution dataset for Bengali Literature**. It was collected and analyzed by the authors of [this paper](https://arxiv.org/abs/2001.05316). It was created by scraping text from an online Bangla e-library using custom web crawler and contains literary works of various famous Bangla writers. It contains novels, stories, series, and other works of 16 authors. Each sample document is created with 750 words. The dataset is imbalanced and resembles real-world scenarios more closely, where not all the authors will have a large number of sample texts. The following table gives more details about the dataset. | Author Name | Number of Samples | Word Count | Unique Word | --- | --- | --- | --- | | zahir rayhan | 185 | 138k | 20k |nazrul | 223 | 167k | 33k |manik bandhopaddhay | 469 | 351k | 44k |nihar ronjon gupta | 476 | 357k | 43k |bongkim | 562 | 421k | 62k |tarashonkor | 775 | 581k | 84k |shottojit roy | 849 | 636k | 67k |shordindu | 888 | 666k | 84k |toslima nasrin | 931 | 698k | 76k |shirshendu | 1048 | 786k | 69k |zafar iqbal | 1100 | 825k | 53k |robindronath | 1259 | 944k | 89k |shorotchandra | 1312 | 984k | 78k |shomresh | 1408 | 1056k|69k |shunil gongopaddhay | 1963 | 1472k|109k |humayun ahmed | 4518 | 3388k |161k **Total**| 17,966|13,474,500 | 590,660 **Average**|1,122.875|842,156.25| 71,822.25 ## Citation If you use this dataset, please cite the paper [Authorship Attribution in Bangla literature using Character-level CNN](https://ieeexplore.ieee.org/abstract/document/9038560/). [Archive link](https://arxiv.org/abs/2001.05316). ``` @inproceedings{BAAD16Dataset, title={Authorship Attribution in Bangla literature using Character-level CNN}, author={Khatun, Aisha and Rahman, Anisur and Islam, Md Saiful and others}, booktitle={2019 22nd International Conference on Computer and Information Technology (ICCIT)}, pages={1--5}, year={2019}, organization={IEEE} doi={10.1109/ICCIT48885.2019.9038560} } ``` This dataset is also available in Mendeley: [BAAD16 dataset](https://data.mendeley.com/datasets/6d9jrkgtvv/4). Always make sure to use the latest version of the dataset. Cite the dataset directly by: ``` @misc{BAAD6Dataset, author = {Khatun, Aisha and Rahman, Anisur and Islam, Md. Saiful}, title = {BAAD16: Bangla Authorship Attribution Dataset}, year={2019}, doi = {10.17632/6d9jrkgtvv.4}, howpublished= {\url{https://data.mendeley.com/datasets/6d9jrkgtvv/4}} } ```

Aisha/BAAD16

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found", "crowdsourced", "expert-generated"], "language_creators": ["found", "crowdsourced"], "language": ["bn"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["multi-class-classification"], "pretty_name": "BAAD16: Bangla Authorship Attribution Dataset (16 Authors)"}

2022-10-22T04:31:54+00:00

TAGS #task_categories-text-classification #task_ids-multi-class-classification #annotations_creators-found #annotations_creators-crowdsourced #annotations_creators-expert-generated #language_creators-found #language_creators-crowdsourced #multilinguality-monolingual #source_datasets-original #language-Bengali #license-cc-by-4.0 #arxiv-2001.05316 #region-us

Description ----------- BAAD16 is an Authorship Attribution dataset for Bengali Literature. It was collected and analyzed by the authors of this paper. It was created by scraping text from an online Bangla e-library using custom web crawler and contains literary works of various famous Bangla writers. It contains novels, stories, series, and other works of 16 authors. Each sample document is created with 750 words. The dataset is imbalanced and resembles real-world scenarios more closely, where not all the authors will have a large number of sample texts. The following table gives more details about the dataset. If you use this dataset, please cite the paper Authorship Attribution in Bangla literature using Character-level CNN. Archive link. This dataset is also available in Mendeley: BAAD16 dataset. Always make sure to use the latest version of the dataset. Cite the dataset directly by:

962505e1575d4c2f04745c1c1e906e9eeebeec03

## Description **BAAD6** is an **Authorship Attribution dataset for Bengali Literature**. It was collected and analyzed by Hemayet et al [[1]](https://ieeexplore.ieee.org/document/8631977). The data was obtained from different online posts and blogs. This dataset is balanced among the 6 Authors with 350 sample texts per author. This is a relatively small dataset but is noisy given the sources it was collected from and its cleaning procedure. Nonetheless, it may help evaluate authorship attribution systems as it resembles texts often available on the Internet. Details about the dataset are given in the table below. | Author | Samples | Word count | Unique word | | ------ | ------ | ------ | ------ | |fe|350|357k|53k| | ij | 350 | 391k | 72k | mk | 350 | 377k | 47k | rn | 350 | 231k | 50k | hm | 350 | 555k | 72k | rg | 350 | 391k | 58k **Total** | 2,100 | 2,304,338 | 230,075 **Average** | 350 | 384,056.33 | 59,006.67 ## Citation If you use this dataset, please cite the paper [A Comparative Analysis of Word Embedding Representations in Authorship Attribution of Bengali Literature](https://ieeexplore.ieee.org/document/8631977). ``` @INPROCEEDINGS{BAAD6Dataset, author={Ahmed Chowdhury, Hemayet and Haque Imon, Md. Azizul and Islam, Md. Saiful}, booktitle={2018 21st International Conference of Computer and Information Technology (ICCIT)}, title={A Comparative Analysis of Word Embedding Representations in Authorship Attribution of Bengali Literature}, year={2018}, volume={}, number={}, pages={1-6}, doi={10.1109/ICCITECHN.2018.8631977} } ``` This dataset is also available in Mendeley: [BAAD6 dataset](https://data.mendeley.com/datasets/w9wkd7g43f/5). Always make sure to use the latest version of the dataset. Cite the dataset directly by: ``` @misc{BAAD6Dataset, author = {Ahmed Chowdhury, Hemayet and Haque Imon, Md. Azizul and Khatun, Aisha and Islam, Md. Saiful}, title = {BAAD6: Bangla Authorship Attribution Dataset}, year={2018}, doi = {10.17632/w9wkd7g43f.5}, howpublished= {\url{https://data.mendeley.com/datasets/w9wkd7g43f/5}} } ```

Aisha/BAAD6

2022-03-02T23:29:22+00:00

{"annotations_creators": ["found", "crowdsourced", "expert-generated"], "language_creators": ["found", "crowdsourced"], "language": ["bn"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["unknown"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["multi-class-classification"], "pretty_name": "BAAD6: Bangla Authorship Attribution Dataset (6 Authors)"}

2022-10-22T04:30:28+00:00

TAGS #task_categories-text-classification #task_ids-multi-class-classification #annotations_creators-found #annotations_creators-crowdsourced #annotations_creators-expert-generated #language_creators-found #language_creators-crowdsourced #multilinguality-monolingual #size_categories-unknown #source_datasets-original #language-Bengali #license-cc-by-4.0 #region-us

Description ----------- BAAD6 is an Authorship Attribution dataset for Bengali Literature. It was collected and analyzed by Hemayet et al [[1]](URL The data was obtained from different online posts and blogs. This dataset is balanced among the 6 Authors with 350 sample texts per author. This is a relatively small dataset but is noisy given the sources it was collected from and its cleaning procedure. Nonetheless, it may help evaluate authorship attribution systems as it resembles texts often available on the Internet. Details about the dataset are given in the table below. If you use this dataset, please cite the paper A Comparative Analysis of Word Embedding Representations in Authorship Attribution of Bengali Literature. This dataset is also available in Mendeley: BAAD6 dataset. Always make sure to use the latest version of the dataset. Cite the dataset directly by:

0254a1e4b0489647fa15b018697627891c938996

## Citing this work @inproceedings{peiris2022synthesis, title={{Synthesis and Evaluation of a Domain-specific Large Data Set for Dungeons \& Dragons}}, author={Akila Peiris and Nisansa de Silva}, booktitle={Proceedings of the 36th Pacific Asia Conference on Language, Information and Computation}, pages={to appear}, year={2022} }

Akila/ForgottenRealmsWikiDataset

2022-03-02T23:29:22+00:00

{}

2024-01-28T10:42:13+00:00

TAGS #region-us

## Citing this work @inproceedings{peiris2022synthesis, title={{Synthesis and Evaluation of a Domain-specific Large Data Set for Dungeons \& Dragons}}, author={Akila Peiris and Nisansa de Silva}, booktitle={Proceedings of the 36th Pacific Asia Conference on Language, Information and Computation}, pages={to appear}, year={2022} }

f64cec94f86acd7cc1c13e3822b48788a42ca943

# Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset is comprised of `emoji` and `emotion` subsets of [tweet_eval](https://huggingface.co/datasets/tweet_eval). The motivation is that the original `emoji` subset essentially contains only positive/neutral emojis, while `emotion` subset contains a varied array of emotions. So, the idea was to replace emotion labels with corresponding emojis (sad, angry) in the `emotion` subset and mix it together with the `emoji` subset. ### Supported Tasks and Leaderboards Similar to tweet eval the expected usage is text classification. ### Languages Only English is present in the dataset. ## 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 Refer to [tweet_eval](https://huggingface.co/datasets/tweet_eval). No additional data was added. #### Annotation process Same as tweet eval. #### Who are the annotators? Same as tweet eval. ### Personal and Sensitive Information Same as tweet eval. ## 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]

adorkin/extended_tweet_emojis

2022-03-02T23:29:22+00:00

{"language": ["en"], "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"]}

2023-02-07T12:18:57+00:00

TAGS #task_categories-text-classification #size_categories-10K<n<100K #language-English #region-us

# Dataset Card for Dataset Name ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary This dataset is comprised of 'emoji' and 'emotion' subsets of tweet_eval. The motivation is that the original 'emoji' subset essentially contains only positive/neutral emojis, while 'emotion' subset contains a varied array of emotions. So, the idea was to replace emotion labels with corresponding emojis (sad, angry) in the 'emotion' subset and mix it together with the 'emoji' subset. ### Supported Tasks and Leaderboards Similar to tweet eval the expected usage is text classification. ### Languages Only English is present in the dataset. ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations Refer to tweet_eval. No additional data was added. #### Annotation process Same as tweet eval. #### Who are the annotators? Same as tweet eval. ### Personal and Sensitive Information Same as tweet eval. ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions

9fda7cad48bcda0c9f95c4e2e72c2966671f3f04

dataset from kaggle https://www.kaggle.com/c/amazon-pet-product-reviews-classification

AlexZapolskii/zapolskii-amazon

2022-03-02T23:29:22+00:00

{}

2021-12-22T22:13:57+00:00

TAGS #region-us

dataset from kaggle URL

758333c328d446c1f24ce93320a60feecac3bc72

# NST Danish 16kHz dataset from Sprakbanken Data is from sprakbanken and can be accessed using following [link](https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-19/).

Alvenir/nst-da-16khz

2022-03-02T23:29:22+00:00

{}

2021-11-29T08:58:25+00:00

TAGS #region-us

# NST Danish 16kHz dataset from Sprakbanken Data is from sprakbanken and can be accessed using following link.

a851e86f3029526f5e239beff1da3130fa9802f7

This dataset is from the common voice corpus 7.0 using the Hausa dataset

Arnold/hausa_common_voice

2022-03-02T23:29:22+00:00

{}

2022-02-10T03:28:22+00:00

TAGS #region-us

This dataset is from the common voice corpus 7.0 using the Hausa dataset

704cf00070272d949d97d8688f81809e95ed23d9

# AutoNLP Dataset for project: Scientific_Title_Generator ## Table of content - [Dataset Description](#dataset-description) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) ## Dataset Descritpion This dataset has been automatically processed by AutoNLP for project Scientific_Title_Generator. ### Languages The BCP-47 code for the dataset's language is unk. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "target": "Unification of Fusion Theories, Rules, Filters, Image Fusion and Target\n Tracking Methods (UFT)", "text": " The author has pledged in various papers, conference or seminar\npresentations, and scientific grant applications (between 2004-2015) for the\nunification of fusion theories, combinations of fusion rules, image fusion\nprocedures, filter algorithms, and target tracking methods for more accurate\napplications to our real world problems - since neither fusion theory nor\nfusion rule fully satisfy all needed applications. For each particular\napplication, one selects the most appropriate fusion space and fusion model,\nthen the fusion rules, and the algorithms of implementation. He has worked in\nthe Unification of the Fusion Theories (UFT), which looks like a cooking\nrecipe, better one could say like a logical chart for a computer programmer,\nbut one does not see another method to comprise/unify all things. The\nunification scenario presented herein, which is now in an incipient form,\nshould periodically be updated incorporating new discoveries from the fusion\nand engineering research.\n" }, { "target": "Investigation of Variances in Belief Networks", "text": " The belief network is a well-known graphical structure for representing\nindependences in a joint probability distribution. The methods, which perform\nprobabilistic inference in belief networks, often treat the conditional\nprobabilities which are stored in the network as certain values. However, if\none takes either a subjectivistic or a limiting frequency approach to\nprobability, one can never be certain of probability values. An algorithm\nshould not only be capable of reporting the probabilities of the alternatives\nof remaining nodes when other nodes are instantiated; it should also be capable\nof reporting the uncertainty in these probabilities relative to the uncertainty\nin the probabilities which are stored in the network. In this paper a method\nfor determining the variances in inferred probabilities is obtained under the\nassumption that a posterior distribution on the uncertainty variables can be\napproximated by the prior distribution. It is shown that this assumption is\nplausible if their is a reasonable amount of confidence in the probabilities\nwhich are stored in the network. Furthermore in this paper, a surprising upper\nbound for the prior variances in the probabilities of the alternatives of all\nnodes is obtained in the case where the probability distributions of the\nprobabilities of the alternatives are beta distributions. It is shown that the\nprior variance in the probability at an alternative of a node is bounded above\nby the largest variance in an element of the conditional probability\ndistribution for that node.\n" } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "target": "Value(dtype='string', id=None)", "text": "Value(dtype='string', id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: | Split name | Num samples | | ------------ | ------------------- | | train | 5784 | | valid | 1446 |

AryanLala/autonlp-data-Scientific_Title_Generator

2022-03-02T23:29:22+00:00

{"task_categories": ["conditional-text-generation"]}

2021-11-20T18:00:56+00:00

TAGS #region-us

AutoNLP Dataset for project: Scientific\_Title\_Generator ========================================================= Table of content ---------------- * Dataset Description + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits Dataset Descritpion ------------------- This dataset has been automatically processed by AutoNLP for project Scientific\_Title\_Generator. ### Languages The BCP-47 code for the dataset's language is unk. Dataset Structure ----------------- ### Data Instances A sample from this dataset looks as follows: ### Dataset Fields The dataset has the following fields (also called "features"): ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow:

04c0b447da176d358a7662f5036ad50c9b17770d

fungi_diagnostic_chars_comparison_japanese 大菌輪「識別形質まとめ」データセット 最終更新日：2024/1/21（R3-11355まで） ==== ### Languages Japanese This dataset is available in Japanese only. # 概要 Atsushi Nakajima（中島淳志）が個人で運営しているWebサイト[大菌輪](http://mycoscouter.coolblog.jp/daikinrin/) では、数千件以上の菌類分類学論文を「論文3行まとめ」という形で要約および索引付け（インデキシング）した情報を提供しています。 その一環として、ある菌と別の菌の「共通する」あるいは「異なる」識別形質 (diagnostic characters) に関する記述を人手で抽出しています。 本データセットは、抽出された識別形質の一覧に、「色/color」、「形状/shape」などのカテゴリを半自動的に付与して集積したものです。 「論文3行まとめ」は毎日更新していますが、本データセットの更新はおおむね1ヶ月に一度とする予定です。 ## 関連データセット 「論文3行まとめ」 [Atsushi/fungi_indexed_mycological_papers_japanese](https://huggingface.co/datasets/Atsushi/fungi_indexed_mycological_papers_japanese) 「Trait Circusデータセット」（統制形質） [Atsushi/fungi_trait_circus_database](https://huggingface.co/datasets/Atsushi/fungi_trait_circus_database) ## 各カラムの説明 * R3ID … 大菌輪「論文3行まとめ」のIDです。 * No … 各識別文を一意のIDで区別するために、各R3IDにおいてナンバリングしたものです。 * comparison_source … 比較元の分類群（学名）です。 * comparison_target … 比較先の分類群（学名）です。 * sentence … 識別文です。全て日本語です。 * label …半自動的に付与されたカテゴリです（人手で修正していますが、ダブルチェックは行っていないので誤分類もあると思います）。以下の25のカテゴリが存在します。 * サイズ/size * 分子系統解析/molecular_phylogenetic_analysis * 形状/shape * 色/color * 地理的分布/geographical_distribution * 生息環境/habitat * 表面性状/surface_characteristics * 構造/structure * 有無/presence * 形態全般/general_morphology * 位置/position * 二次代謝産物/secondary_metabolite * 呈色反応/chemical_reaction * 数量/amount * 発達/development * 生理学的形質/physiological_characters * 分類/classification * 資化・発酵能/assimilation_and_fermentation * 質感/texture * 味・臭い/taste_and_smell * 病害・病原性関連/disease_and_pathogenecity * 全般/general_characters * 耐性・感受性/resistance_and_susceptibility * 栄養摂取様式/nutrition_style * 未分類/unclassified * common_or_different … 共通する形質は「1」、異なる形質は「0」です。 * data_source … 各情報の 出典（文献）のURLです。

Atsushi/fungi_diagnostic_chars_comparison_japanese

2022-03-02T23:29:22+00:00

{"annotations_creators": ["other"], "language": ["ja"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["multi-class-classification"]}

2024-01-21T04:55:20+00:00

TAGS #task_categories-text-classification #task_ids-multi-class-classification #annotations_creators-other #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-Japanese #license-cc-by-4.0 #region-us

fungi_diagnostic_chars_comparison_japanese 大菌輪「識別形質まとめ」データセット 最終更新日：2024/1/21（R3-11355まで） ==== ### Languages Japanese This dataset is available in Japanese only. # 概要 Atsushi Nakajima（中島淳志）が個人で運営しているWebサイト大菌輪 では、数千件以上の菌類分類学論文を「論文3行まとめ」という形で要約および索引付け（インデキシング）した情報を提供しています。 その一環として、ある菌と別の菌の「共通する」あるいは「異なる」識別形質 (diagnostic characters) に関する記述を人手で抽出しています。 本データセットは、抽出された識別形質の一覧に、「色/color」、「形状/shape」などのカテゴリを半自動的に付与して集積したものです。 「論文3行まとめ」は毎日更新していますが、本データセットの更新はおおむね1ヶ月に一度とする予定です。 ## 関連データセット 「論文3行まとめ」 Atsushi/fungi_indexed_mycological_papers_japanese 「Trait Circusデータセット」（統制形質） Atsushi/fungi_trait_circus_database ## 各カラムの説明 * R3ID … 大菌輪「論文3行まとめ」のIDです。 * No … 各識別文を一意のIDで区別するために、各R3IDにおいてナンバリングしたものです。 * comparison_source … 比較元の分類群（学名）です。 * comparison_target … 比較先の分類群（学名）です。 * sentence … 識別文です。全て日本語です。 * label …半自動的に付与されたカテゴリです（人手で修正していますが、ダブルチェックは行っていないので誤分類もあると思います）。以下の25のカテゴリが存在します。 * サイズ/size * 分子系統解析/molecular_phylogenetic_analysis * 形状/shape * 色/color * 地理的分布/geographical_distribution * 生息環境/habitat * 表面性状/surface_characteristics * 構造/structure * 有無/presence * 形態全般/general_morphology * 位置/position * 二次代謝産物/secondary_metabolite * 呈色反応/chemical_reaction * 数量/amount * 発達/development * 生理学的形質/physiological_characters * 分類/classification * 資化・発酵能/assimilation_and_fermentation * 質感/texture * 味・臭い/taste_and_smell * 病害・病原性関連/disease_and_pathogenecity * 全般/general_characters * 耐性・感受性/resistance_and_susceptibility * 栄養摂取様式/nutrition_style * 未分類/unclassified * common_or_different … 共通する形質は「1」、異なる形質は「0」です。 * data_source … 各情報の 出典（文献）のURLです。

e187a03ea89752bec2f459a4ebb77c8c92dc4146

fungi_indexed_mycological_papers_japanese 大菌輪「論文3行まとめ」データセット 最終更新日：2024/1/21（R3-11355まで） ==== ### Languages Japanese This dataset is available in Japanese only. # 概要 Atsushi Nakajima（中島淳志）が個人で運営しているWebサイト[大菌輪](http://mycoscouter.coolblog.jp/daikinrin/) では、数千件以上の菌類分類学論文を「論文3行まとめ」という形で要約および索引付け（インデキシング）した情報を提供しています。 本データセットは、「論文3行まとめ」のコンテンツに含まれる各論文の3行抄録、タグ（索引）、掲載種一覧、比較種一覧をまとめたものです。 「論文3行まとめ」は毎日更新していますが、本データセットの更新はおおむね1ヶ月に一度とする予定です。 また、本データセットを可視化したWebアプリを[Observableで公開](https://tinyurl.com/2tvryz8u)しています。 ## 関連データセット 「識別形質まとめ」 [Atsushi/fungi_diagnostic_chars_comparison_japanese](https://huggingface.co/datasets/Atsushi/fungi_diagnostic_chars_comparison_japanese) 「Trait Circusデータセット」（統制形質） [Atsushi/fungi_trait_circus_database](https://huggingface.co/datasets/Atsushi/fungi_trait_circus_database) ## 各カラムの説明 * R3ID … 大菌輪「論文3行まとめ」のIDです。 * ja_title_provisional_translate（仮訳和文題名） … 作成者が翻訳したタイトルです。一部、日本語の原題があるものはそれをそのまま使用しています。 * original_title（原文題名） * published_year（出版年） * journal_title（雑誌名） * source（文献リンク） … 各情報の 出典（文献）のURLです。 * daikinrin_url … 大菌輪「論文3行まとめ」のURLです。 * tags … 作成者が論文を全文読んだ上で独自に付与した索引です。カンマ+半角空白区切りです。形態形質、宿主/基質、実験器具/実験手法/試薬、地理的分布、生理/生化学などを幅広く索引しています。 * R3summary_1 … 3行抄録の「1行目」です。 * R3summary_2 … 3行抄録の「2行目」です。 * R3summary_3 … 3行抄録の「3行目」です。 * species_reported（報告種一覧） … 当該論文内で掲載された種の一覧です。「半角空白+半角スラッシュ+半角空白」区切りです。記号の意味は以下の通りです。 * ★＝新種（新亜種・新品種・新変種） * ■＝ 新産種 * ▲＝新組み合わせ * ◆＝新学名 * ●＝新階級 * （無印）＝その他 * species_compared（比較種一覧） … いずれかの報告種と論文中で何らかの比較がなされた種の一覧です。「半角空白+半角スラッシュ+半角空白」区切りです。詳細は「識別形質まとめ」データセット（[Atsushi/fungi_diagnostic_chars_comparison_japanese](https://huggingface.co/datasets/Atsushi/fungi_diagnostic_chars_comparison_japanese)）を参照してください。 * taxon_reported（分類群一覧） … 報告種に対応する上位分類群をまとめたものです。カンマ+半角空白区切りです。MycoBankの情報を基に付与していますが、最新でない可能性があります。

Atsushi/fungi_indexed_mycological_papers_japanese

2022-03-02T23:29:22+00:00

{"annotations_creators": ["other"], "language": ["ja"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"]}

2024-01-21T04:55:02+00:00

TAGS #annotations_creators-other #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Japanese #license-cc-by-4.0 #region-us

fungi_indexed_mycological_papers_japanese 大菌輪「論文3行まとめ」データセット 最終更新日：2024/1/21（R3-11355まで） ==== ### Languages Japanese This dataset is available in Japanese only. # 概要 Atsushi Nakajima（中島淳志）が個人で運営しているWebサイト大菌輪 では、数千件以上の菌類分類学論文を「論文3行まとめ」という形で要約および索引付け（インデキシング）した情報を提供しています。 本データセットは、「論文3行まとめ」のコンテンツに含まれる各論文の3行抄録、タグ（索引）、掲載種一覧、比較種一覧をまとめたものです。 「論文3行まとめ」は毎日更新していますが、本データセットの更新はおおむね1ヶ月に一度とする予定です。 また、本データセットを可視化したWebアプリをObservableで公開しています。 ## 関連データセット 「識別形質まとめ」 Atsushi/fungi_diagnostic_chars_comparison_japanese 「Trait Circusデータセット」（統制形質） Atsushi/fungi_trait_circus_database ## 各カラムの説明 * R3ID … 大菌輪「論文3行まとめ」のIDです。 * ja_title_provisional_translate（仮訳和文題名） … 作成者が翻訳したタイトルです。一部、日本語の原題があるものはそれをそのまま使用しています。 * original_title（原文題名） * published_year（出版年） * journal_title（雑誌名） * source（文献リンク） … 各情報の 出典（文献）のURLです。 * daikinrin_url … 大菌輪「論文3行まとめ」のURLです。 * tags … 作成者が論文を全文読んだ上で独自に付与した索引です。カンマ+半角空白区切りです。形態形質、宿主/基質、実験器具/実験手法/試薬、地理的分布、生理/生化学などを幅広く索引しています。 * R3summary_1 … 3行抄録の「1行目」です。 * R3summary_2 … 3行抄録の「2行目」です。 * R3summary_3 … 3行抄録の「3行目」です。 * species_reported（報告種一覧） … 当該論文内で掲載された種の一覧です。「半角空白+半角スラッシュ+半角空白」区切りです。記号の意味は以下の通りです。 * ＝新種（新亜種・新品種・新変種） * ■＝ 新産種 * ▲＝新組み合わせ * ◆＝新学名 * ●＝新階級 * （無印）＝その他 * species_compared（比較種一覧） … いずれかの報告種と論文中で何らかの比較がなされた種の一覧です。「半角空白+半角スラッシュ+半角空白」区切りです。詳細は「識別形質まとめ」データセット（Atsushi/fungi_diagnostic_chars_comparison_japanese）を参照してください。 * taxon_reported（分類群一覧） … 報告種に対応する上位分類群をまとめたものです。カンマ+半角空白区切りです。MycoBankの情報を基に付与していますが、最新でない可能性があります。

c8e5049ab833801f41d6a835200b7e39c42916db

fungi_trait_circus_database 大菌輪「Trait Circus」データセット（統制形質） 最終更新日：2023/12/29 ==== ### Languages Japanese and English Please do not use this dataset for academic purposes for the time being. (casual use only) 当面の間仮公開とします。学術目的での使用はご遠慮ください。 # 概要 Atsushi Nakajima（中島淳志）が個人で運営しているWebサイト[大菌輪](http://mycoscouter.coolblog.jp/daikinrin/) では、菌類の記載文を自然言語処理の手法を利用して半自動的に処理し、菌類の形態、生態などに関する様々な「形質 (traits)」データを抽出して、集計や解析の便宜を図るために、あらかじめ設定された「統制語 (controlled term)」の形でまとめています。 抽出手法については「ニッチェ・ライフ」誌の[こちらの記事](https://media.niche-life.com/series/008/Niche008_06.pdf)（査読なし）で報告しています。 自動抽出という性質上、ある程度の誤りが含まれる可能性があることをご承知おきください。 2023/12/29に全ての掲載内容を見直し、ほぼ一から収集し直したデータに差し替えました。 統制語は「要素 (element）」「属性（attribute）」「値（value）」の3つ組からなります。 例えば「傘_色_黒」はそれぞれ「傘」「色」「黒」の要素/属性/値を持っています。一部の統制語では要素と属性が同一となっています（「生息環境」など） 参考までに、データ数上位3件は「要素」で「子実体」「傘」「胞子」、「属性」で「色」「形状」「表面性状」、「値」で「褐」「平滑」「黄」です。 また、菌類分類学の学習および同定支援の目的で、そのデータを基にしたインタラクティブな可視化Webアプリ「[Trait Circus](https://tinyurl.com/nrhcfksu)」を提供しています。 本データセットは、そのWebアプリの生データに相当し、容量の都合等でWebアプリに反映されていない情報も含まれています。 ## 関連データセット 「論文3行まとめ」 [Atsushi/fungi_indexed_mycological_papers_japanese](https://huggingface.co/datasets/Atsushi/fungi_indexed_mycological_papers_japanese) 「識別形質まとめ」 [Atsushi/fungi_diagnostic_chars_comparison_japanese](https://huggingface.co/datasets/Atsushi/fungi_diagnostic_chars_comparison_japanese) ## 各カラムの説明 * source … 各情報の出典のURLです。多くは学術文献またはMycoBankの記載文データベースを参照しています。 * hit_term … 抽出された形質の出典中における表現です。 * current_name … その形質を有する菌の現行学名です。MycoBankを参照していますが、最新の情報ではない可能性があります。 * original_name … その形質を有する菌の出典で使用されていた学名です。（2023/12/29版から追加） * element_j … 「要素」の日本語表記です。 * attribute_j … 「属性」の日本語表記です。 * value_j … 「値」の日本語表記です。 * element … 「要素」の英語表記です。 * attribute … 「属性」の英語表記です。 * value … 「値」の英語表記です。

Atsushi/fungi_trait_circus_database

2022-03-02T23:29:22+00:00

{"annotations_creators": ["other"], "language": ["en", "ja"], "license": ["cc-by-4.0"], "multilinguality": ["multilingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"]}

2023-12-29T06:22:46+00:00

TAGS #annotations_creators-other #multilinguality-multilingual #size_categories-100K<n<1M #source_datasets-original #language-English #language-Japanese #license-cc-by-4.0 #region-us

fungi_trait_circus_database 大菌輪「Trait Circus」データセット（統制形質） 最終更新日：2023/12/29 ==== ### Languages Japanese and English Please do not use this dataset for academic purposes for the time being. (casual use only) 当面の間仮公開とします。学術目的での使用はご遠慮ください。 # 概要 Atsushi Nakajima（中島淳志）が個人で運営しているWebサイト大菌輪 では、菌類の記載文を自然言語処理の手法を利用して半自動的に処理し、菌類の形態、生態などに関する様々な「形質 (traits)」データを抽出して、集計や解析の便宜を図るために、あらかじめ設定された「統制語 (controlled term)」の形でまとめています。 抽出手法については「ニッチェ・ライフ」誌のこちらの記事（査読なし）で報告しています。 自動抽出という性質上、ある程度の誤りが含まれる可能性があることをご承知おきください。 2023/12/29に全ての掲載内容を見直し、ほぼ一から収集し直したデータに差し替えました。 統制語は「要素 (element）」「属性（attribute）」「値（value）」の3つ組からなります。 例えば「傘_色_黒」はそれぞれ「傘」「色」「黒」の要素/属性/値を持っています。一部の統制語では要素と属性が同一となっています（「生息環境」など） 参考までに、データ数上位3件は「要素」で「子実体」「傘」「胞子」、「属性」で「色」「形状」「表面性状」、「値」で「褐」「平滑」「黄」です。 また、菌類分類学の学習および同定支援の目的で、そのデータを基にしたインタラクティブな可視化Webアプリ「Trait Circus」を提供しています。 本データセットは、そのWebアプリの生データに相当し、容量の都合等でWebアプリに反映されていない情報も含まれています。 ## 関連データセット 「論文3行まとめ」 Atsushi/fungi_indexed_mycological_papers_japanese 「識別形質まとめ」 Atsushi/fungi_diagnostic_chars_comparison_japanese ## 各カラムの説明 * source … 各情報の出典のURLです。多くは学術文献またはMycoBankの記載文データベースを参照しています。 * hit_term … 抽出された形質の出典中における表現です。 * current_name … その形質を有する菌の現行学名です。MycoBankを参照していますが、最新の情報ではない可能性があります。 * original_name … その形質を有する菌の出典で使用されていた学名です。（2023/12/29版から追加） * element_j … 「要素」の日本語表記です。 * attribute_j … 「属性」の日本語表記です。 * value_j … 「値」の日本語表記です。 * element … 「要素」の英語表記です。 * attribute … 「属性」の英語表記です。 * value … 「値」の英語表記です。

5b70713c4cca19c341b62b01811c834ee60a33f2

cc-by-nc-sa-4.0--- annotations_creators: - machine-generated language_creators: - machine-generated language: - en multilinguality: - monolingual size_categories: - unknown source_datasets: - original task_categories: - text-retrieval - text-generation task_ids: [] pretty_name: rebel-dataset tags: - relation-extraction - conditional-text-generation --- # Dataset Card for REBEL dataset ## Table of Contents - [Dataset Card for REBEL dataset](#dataset-card-for-rebel) - [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/Babelscape/rebel](https://github.com/Babelscape/rebel) - **Paper:** [https://github.com/Babelscape/rebel/blob/main/docs/EMNLP_2021_REBEL__Camera_Ready_.pdf](https://github.com/Babelscape/rebel/blob/main/docs/EMNLP_2021_REBEL__Camera_Ready_.pdf) - **Point of Contact:** [huguetcabot@babelscape.com](huguetcabot@babelscape.com) ### Dataset Summary Dataset created for [REBEL](https://huggingface.co/Babelscape/rebel-large) dataset from interlinking Wikidata and Wikipedia for Relation Extraction, filtered using NLI. ### Supported Tasks and Leaderboards - `text-retrieval-other-relation-extraction`: The dataset can be used to train a model for Relation Extraction, which consists in extracting triplets from raw text, made of subject, object and relation type. Success on this task is typically measured by achieving a *high* [F1](https://huggingface.co/metrics/F1). The [BART](https://huggingface.co/transformers/model_doc/bart.html)) model currently achieves the following score: 74 Micro F1 and 51 Macro F1 for the 220 most frequent relation types. ### Languages The dataset is in English, from the English Wikipedia. ## Dataset Structure ### Data Instances REBEL - `Size of downloaded dataset files`: 1490.02 MB - `Size of the generated dataset`: 1199.27 MB - `Total amount of disk used`: 2689.29 MB ``` { 'id': 'Q82442-1', 'title': 'Arsène Lupin, Gentleman Burglar', 'context': 'Arsène Lupin , Gentleman Burglar is the first collection of stories by Maurice Leblanc recounting the adventures of Arsène Lupin , released on 10 June 1907 .', 'triplets': '<triplet> Arsène Lupin, Gentleman Burglar <subj> Maurice Leblanc <obj> author <triplet> Arsène Lupin <subj> Maurice Leblanc <obj> creator' } ``` The original data is in jsonl format and contains much more information. It is divided by Wikipedia articles instead of by sentence, and contains metadata about Wikidata entities, their boundaries in the text, how it was annotated, etc. For more information check the [paper repository](https://huggingface.co/Babelscape/rebel-large) and how it was generated using the Relation Extraction dataset pipeline, [cRocoDiLe](https://github.com/Babelscape/crocodile). ### Data Fields List and describe the fields present in the dataset. Mention their data type, and whether they are used as input or output in any of the tasks the dataset currently supports. If the data has span indices, describe their attributes, such as whether they are at the character level or word level, whether they are contiguous or not, etc. If the datasets contains example IDs, state whether they have an inherent meaning, such as a mapping to other datasets or pointing to relationships between data points. - `id`: ID of the instance. It contains a unique id matching to a Wikipedia page and a number separated by a hyphen indicating which sentence of the Wikipedia article it is. - `title`: Title of the Wikipedia page the sentence comes from. - `context`: Text from Wikipedia articles that serves as context for the Relation Extraction task. - `triplets`: Linearized version of the triplets present in the text, split by the use of special tokens. For more info on this linearization check the [paper](https://github.com/Babelscape/rebel/blob/main/docs/EMNLP_2021_REBEL__Camera_Ready_.pdf). ### Data Splits Test and Validation splits are each 5% of the original data. Provide the sizes of each split. As appropriate, provide any descriptive statistics for the features, such as average length. For example: | | Tain | Valid | Test | | ----- | ------ | ----- | ---- | | Input Sentences | 3,120,296 | 172,860 | 173,601 | | Input Sentences (top 220 relation types as used in original paper) | 784,202 | 43,341 | 43,506 | | Number of Triplets (top 220 relation types as used in original paper) | 878,555 | 48,514 | 48,852 | ## Dataset Creation ### Curation Rationale This dataset was created to enable the training of a BART based model as pre-training phase for Relation Extraction as seen in the paper [REBEL: Relation Extraction By End-to-end Language generation](https://github.com/Babelscape/rebel/blob/main/docs/EMNLP_2021_REBEL__Camera_Ready_.pdf). ### Source Data Data comes from Wikipedia text before the table of contents, as well as Wikidata for the triplets annotation. #### Initial Data Collection and Normalization For the data collection, the dataset extraction pipeline [cRocoDiLe: Automati**c** **R**elati**o**n Extra**c**ti**o**n **D**ataset w**i**th N**L**I filt**e**ring](https://github.com/Babelscape/crocodile) insipired by [T-REx Pipeline](https://github.com/hadyelsahar/RE-NLG-Dataset) more details found at: [T-REx Website](https://hadyelsahar.github.io/t-rex/). The starting point is a Wikipedia dump as well as a Wikidata one. After the triplets are extracted, an NLI system was used to filter out those not entailed by the text. #### Who are the source language producers? Any Wikipedia and Wikidata contributor. ### Annotations #### Annotation process The dataset extraction pipeline [cRocoDiLe: Automati**c** **R**elati**o**n Extra**c**ti**o**n **D**ataset w**i**th N**L**I filt**e**ring](https://github.com/Babelscape/crocodile). #### Who are the annotators? Automatic annottations ### Personal and Sensitive Information All text is from Wikipedia, any Personal or Sensitive Information there may be present in this dataset. ## Considerations for Using the Data ### Social Impact of Dataset The dataset serves as a pre-training step for Relation Extraction models. It is distantly annotated, hence it should only be used as such. A model trained solely on this dataset may produce allucinations coming from the silver nature of the dataset. ### Discussion of Biases Since the dataset was automatically created from Wikipedia and Wikidata, it may reflect the biases withing those sources. For Wikipedia text, see for example [Dinan et al 2020 on biases in Wikipedia (esp. Table 1)](https://arxiv.org/abs/2005.00614), or [Blodgett et al 2020](https://www.aclweb.org/anthology/2020.acl-main.485/) for a more general discussion of the topic. For Wikidata, there are class imbalances, also resulting from Wikipedia. ### Other Known Limitations Not for now ## Additional Information ### Dataset Curators Pere-Lluis Huguet Cabot - Babelscape and Sapienza University of Rome, Italy Roberto Navigli - Sapienza University of Rome, Italy ### Licensing Information Contents of this repository are restricted to only non-commercial research purposes under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright of the dataset contents belongs to the original copyright holders. ### Citation Information Provide the [BibTex](http://www.bibtex.org/)-formatted reference for the dataset. For example: ``` @inproceedings{huguet-cabot-navigli-2021-rebel, title = "REBEL: Relation Extraction By End-to-end Language generation", author = "Huguet Cabot, Pere-Llu{\'\i}s and Navigli, Roberto", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021", month = nov, year = "2021", address = "Online and in the Barceló Bávaro Convention Centre, Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://github.com/Babelscape/rebel/blob/main/docs/EMNLP_2021_REBEL__Camera_Ready_.pdf", } ``` ### Contributions Thanks to [@littlepea13](https://github.com/LittlePea13) for adding this dataset.

Babelscape/rebel-dataset

2022-03-02T23:29:22+00:00

{"annotations_creators": ["machine-generated"], "language_creators": ["machine-generated"], "language": ["en"], "license": "cc-by-sa-4.0", "multilinguality": ["monolingual"], "size_categories": ["unknown"], "source_datasets": ["original"], "task_categories": ["text-retrieval", "text-generation"], "task_ids": [], "pretty_name": "rebel-dataset", "tags": ["relation-extraction", "conditional-text-generation"]}

2023-06-15T11:12:59+00:00

TAGS #task_categories-text-retrieval #task_categories-text-generation #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-unknown #source_datasets-original #language-English #license-cc-by-sa-4.0 #relation-extraction #conditional-text-generation #arxiv-2005.00614 #region-us

cc-by-nc-sa-4.0--- annotations\_creators: * machine-generated language\_creators: * machine-generated language: * en multilinguality: * monolingual size\_categories: * unknown source\_datasets: * original task\_categories: * text-retrieval * text-generation task\_ids: [] pretty\_name: rebel-dataset tags: * relation-extraction * conditional-text-generation --- Dataset Card for REBEL dataset ============================== Table of Contents ----------------- * Dataset Card for REBEL dataset + Table of Contents + Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages + Dataset Structure - Data Instances - Data Fields - Data Splits + Dataset Creation - Curation Rationale - Source Data * Initial Data Collection and Normalization * Who are the source language producers? - Annotations * Annotation process * Who are the annotators? - Personal and Sensitive Information + Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations + Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions Dataset Description ------------------- * Repository: URL * Paper: URL * Point of Contact: huguetcabot@URL ### Dataset Summary Dataset created for REBEL dataset from interlinking Wikidata and Wikipedia for Relation Extraction, filtered using NLI. ### Supported Tasks and Leaderboards * 'text-retrieval-other-relation-extraction': The dataset can be used to train a model for Relation Extraction, which consists in extracting triplets from raw text, made of subject, object and relation type. Success on this task is typically measured by achieving a *high* F1. The BART) model currently achieves the following score: 74 Micro F1 and 51 Macro F1 for the 220 most frequent relation types. ### Languages The dataset is in English, from the English Wikipedia. Dataset Structure ----------------- ### Data Instances REBEL * 'Size of downloaded dataset files': 1490.02 MB * 'Size of the generated dataset': 1199.27 MB * 'Total amount of disk used': 2689.29 MB The original data is in jsonl format and contains much more information. It is divided by Wikipedia articles instead of by sentence, and contains metadata about Wikidata entities, their boundaries in the text, how it was annotated, etc. For more information check the paper repository and how it was generated using the Relation Extraction dataset pipeline, cRocoDiLe. ### Data Fields List and describe the fields present in the dataset. Mention their data type, and whether they are used as input or output in any of the tasks the dataset currently supports. If the data has span indices, describe their attributes, such as whether they are at the character level or word level, whether they are contiguous or not, etc. If the datasets contains example IDs, state whether they have an inherent meaning, such as a mapping to other datasets or pointing to relationships between data points. * 'id': ID of the instance. It contains a unique id matching to a Wikipedia page and a number separated by a hyphen indicating which sentence of the Wikipedia article it is. * 'title': Title of the Wikipedia page the sentence comes from. * 'context': Text from Wikipedia articles that serves as context for the Relation Extraction task. * 'triplets': Linearized version of the triplets present in the text, split by the use of special tokens. For more info on this linearization check the paper. ### Data Splits Test and Validation splits are each 5% of the original data. Provide the sizes of each split. As appropriate, provide any descriptive statistics for the features, such as average length. For example: Dataset Creation ---------------- ### Curation Rationale This dataset was created to enable the training of a BART based model as pre-training phase for Relation Extraction as seen in the paper REBEL: Relation Extraction By End-to-end Language generation. ### Source Data Data comes from Wikipedia text before the table of contents, as well as Wikidata for the triplets annotation. #### Initial Data Collection and Normalization For the data collection, the dataset extraction pipeline cRocoDiLe: Automatic Relation Extraction Dataset with NLI filtering insipired by T-REx Pipeline more details found at: T-REx Website. The starting point is a Wikipedia dump as well as a Wikidata one. After the triplets are extracted, an NLI system was used to filter out those not entailed by the text. #### Who are the source language producers? Any Wikipedia and Wikidata contributor. ### Annotations #### Annotation process The dataset extraction pipeline cRocoDiLe: Automatic Relation Extraction Dataset with NLI filtering. #### Who are the annotators? Automatic annottations ### Personal and Sensitive Information All text is from Wikipedia, any Personal or Sensitive Information there may be present in this dataset. Considerations for Using the Data --------------------------------- ### Social Impact of Dataset The dataset serves as a pre-training step for Relation Extraction models. It is distantly annotated, hence it should only be used as such. A model trained solely on this dataset may produce allucinations coming from the silver nature of the dataset. ### Discussion of Biases Since the dataset was automatically created from Wikipedia and Wikidata, it may reflect the biases withing those sources. For Wikipedia text, see for example Dinan et al 2020 on biases in Wikipedia (esp. Table 1), or Blodgett et al 2020 for a more general discussion of the topic. For Wikidata, there are class imbalances, also resulting from Wikipedia. ### Other Known Limitations Not for now Additional Information ---------------------- ### Dataset Curators Pere-Lluis Huguet Cabot - Babelscape and Sapienza University of Rome, Italy Roberto Navigli - Sapienza University of Rome, Italy ### Licensing Information Contents of this repository are restricted to only non-commercial research purposes under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). Copyright of the dataset contents belongs to the original copyright holders. Provide the BibTex-formatted reference for the dataset. For example: ### Contributions Thanks to @littlepea13 for adding this dataset.

74c9b9dca034bb1606a6769457983904bd976803

## Table of Contents - [Description](#description) - [Dataset Structure](#dataset-structure) - [Additional Information](#additional-information) ## Dataset Card for WikiNEuRal dataset ## Dataset Description - **Summary:** Training data for NER in 9 languages. - **Repository:** [https://github.com/Babelscape/wikineural](https://github.com/Babelscape/wikineural) - **Paper:** [https://aclanthology.org/wikineural](https://aclanthology.org/2021.findings-emnlp.215/) - **Point of Contact:** [tedeschi@babelscape.com](tedeschi@babelscape.com) ## Description - **Summary:** In a nutshell, WikiNEuRal consists in a novel technique which builds upon a multilingual lexical knowledge base (i.e., [BabelNet](https://babelnet.org/)) and transformer-based architectures (i.e., [BERT](https://arxiv.org/abs/1810.04805)) to produce high-quality annotations for multilingual NER. It shows consistent improvements of up to 6 span-based F1-score points against state-of-the-art alternative data production methods on common benchmarks for NER. We used this methodology to automatically generate training data for NER in 9 languages. - **Repository:** [https://github.com/Babelscape/wikineural](https://github.com/Babelscape/wikineural) - **Paper:** [https://aclanthology.org/wikineural](https://aclanthology.org/2021.findings-emnlp.215/) - **Point of Contact:** [tedeschi@babelscape.com](tedeschi@babelscape.com) ## Dataset Structure The data fields are the same among all splits. - `tokens`: a `list` of `string` features. - `ner_tags`: a `list` of classification labels (`int`). Full tagset with indices: ```python {'O': 0, 'B-PER': 1, 'I-PER': 2, 'B-ORG': 3, 'I-ORG': 4, 'B-LOC': 5, 'I-LOC': 6, 'B-MISC': 7, 'I-MISC': 8} ``` - `lang`: a `string` feature. Full list of language: Dutch (nl), English (en), French (fr), German (de), Italian (it), Polish (pl), Portugues (pt), Russian (ru), Spanish (es). ## Dataset Statistics The table below shows the number of sentences, number of tokens and number of instances per class, for each of the 9 languages. | Dataset Version | Sentences | Tokens | PER | ORG | LOC | MISC | OTHER | | :------------- | -------------: | -------------: | -------------: | -------------: | -------------: | -------------: | -------------: | | WikiNEuRal EN | 116k | 2.73M | 51k | 31k | 67k | 45k | 2.40M | | WikiNEuRal ES | 95k | 2.33M | 43k | 17k | 68k | 25k | 2.04M | | WikiNEuRal NL | 107k | 1.91M | 46k | 22k | 61k | 24k | 1.64M | | WikiNEuRal DE | 124k | 2.19M | 60k | 32k | 59k | 25k | 1.87M | | WikiNEuRal RU | 123k | 2.39M | 40k | 26k | 89k | 25k | 2.13M | | WikiNEuRal IT | 111k | 2.99M | 67k | 22k | 97k | 26k | 2.62M | | WikiNEuRal FR | 127k | 3.24M | 76k | 25k | 101k | 29k | 2.83M | | WikiNEuRal PL | 141k | 2.29M | 59k | 34k | 118k | 22k | 1.91M | | WikiNEuRal PT | 106k | 2.53M | 44k | 17k | 112k | 25k | 2.20M | ## Additional Information - **Licensing Information**: Contents of this repository are restricted to only non-commercial research purposes under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright of the dataset contents belongs to the original copyright holders. - **Citation Information**: Please consider citing our work if you use data and/or code from this repository. ```bibtex @inproceedings{tedeschi-etal-2021-wikineural-combined, title = "{W}iki{NE}u{R}al: {C}ombined Neural and Knowledge-based Silver Data Creation for Multilingual {NER}", author = "Tedeschi, Simone and Maiorca, Valentino and Campolungo, Niccol{\`o} and Cecconi, Francesco and Navigli, Roberto", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021", month = nov, year = "2021", address = "Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-emnlp.215", pages = "2521--2533", abstract = "Multilingual Named Entity Recognition (NER) is a key intermediate task which is needed in many areas of NLP. In this paper, we address the well-known issue of data scarcity in NER, especially relevant when moving to a multilingual scenario, and go beyond current approaches to the creation of multilingual silver data for the task. We exploit the texts of Wikipedia and introduce a new methodology based on the effective combination of knowledge-based approaches and neural models, together with a novel domain adaptation technique, to produce high-quality training corpora for NER. We evaluate our datasets extensively on standard benchmarks for NER, yielding substantial improvements up to 6 span-based F1-score points over previous state-of-the-art systems for data creation.", } ``` - **Contributions**: Thanks to [@sted97](https://github.com/sted97) for adding this dataset.

Babelscape/wikineural

2022-03-02T23:29:22+00:00

{"annotations_creators": ["machine-generated"], "language_creators": ["machine-generated"], "language": ["de", "en", "es", "fr", "it", "nl", "pl", "pt", "ru"], "license": ["cc-by-nc-sa-4.0"], "multilinguality": ["multilingual"], "source_datasets": ["original"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "wikineural-dataset", "tags": ["structure-prediction"]}

2022-11-13T07:52:46+00:00

TAGS #task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-multilingual #source_datasets-original #language-German #language-English #language-Spanish #language-French #language-Italian #language-Dutch #language-Polish #language-Portuguese #language-Russian #license-cc-by-nc-sa-4.0 #structure-prediction #arxiv-1810.04805 #region-us

Table of Contents ----------------- * Description * Dataset Structure * Additional Information Dataset Card for WikiNEuRal dataset ----------------------------------- Dataset Description ------------------- * Summary: Training data for NER in 9 languages. * Repository: URL * Paper: URL * Point of Contact: tedeschi@URL Description ----------- * Summary: In a nutshell, WikiNEuRal consists in a novel technique which builds upon a multilingual lexical knowledge base (i.e., BabelNet) and transformer-based architectures (i.e., BERT) to produce high-quality annotations for multilingual NER. It shows consistent improvements of up to 6 span-based F1-score points against state-of-the-art alternative data production methods on common benchmarks for NER. We used this methodology to automatically generate training data for NER in 9 languages. * Repository: URL * Paper: URL * Point of Contact: tedeschi@URL Dataset Structure ----------------- The data fields are the same among all splits. * 'tokens': a 'list' of 'string' features. * 'ner\_tags': a 'list' of classification labels ('int'). Full tagset with indices: * 'lang': a 'string' feature. Full list of language: Dutch (nl), English (en), French (fr), German (de), Italian (it), Polish (pl), Portugues (pt), Russian (ru), Spanish (es). Dataset Statistics ------------------ The table below shows the number of sentences, number of tokens and number of instances per class, for each of the 9 languages. Additional Information ---------------------- * Licensing Information: Contents of this repository are restricted to only non-commercial research purposes under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). Copyright of the dataset contents belongs to the original copyright holders. * Citation Information: Please consider citing our work if you use data and/or code from this repository. * Contributions: Thanks to @sted97 for adding this dataset.

a388264362bb51412653f3da7b18f37fd24fab30

# ParlaTextCorpus Spoken text corpus for Catalan. Derived and cleaned from three sources. OpenSubtitles, Tv3Parla and Festcat.

Baybars/parla_text_corpus

2022-03-02T23:29:22+00:00

{"annotations_creators": ["no-annotation"], "language_creators": ["various"], "language": ["ca"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["100k<n<1M"], "source_datasets": ["found"], "task_categories": ["sequence-modeling"], "task_ids": ["language-modeling"], "pretty_name": "ParlaTextCorpus", "tags": ["robust-speech-event"]}

2022-10-21T14:29:15+00:00

TAGS #task_ids-language-modeling #annotations_creators-no-annotation #language_creators-various #multilinguality-monolingual #size_categories-100k<n<1M #source_datasets-found #language-Catalan #license-cc-by-4.0 #robust-speech-event #region-us

# ParlaTextCorpus Spoken text corpus for Catalan. Derived and cleaned from three sources. OpenSubtitles, Tv3Parla and Festcat.

31cce8af65eb851c49dca96b280b957a6e745424

# Dataset Card for BEIR Benchmark ## 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/UKPLab/beir - **Repository:** https://github.com/UKPLab/beir - **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ - **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns - **Point of Contact:** nandan.thakur@uwaterloo.ca ### Dataset Summary BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks: - Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact) - Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/) - Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) - News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html) - Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data) - Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) - Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs) - Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html) - Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/) All these datasets have been preprocessed and can be used for your experiments. ```python ``` ### Supported Tasks and Leaderboards The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia. The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/). ### Languages All tasks are in English (`en`). ## Dataset Structure All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format: - `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}` - `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}` - `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1` ### Data Instances A high level example of any beir dataset: ```python corpus = { "doc1" : { "title": "Albert Einstein", "text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \ one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \ its influence on the philosophy of science. He is best known to the general public for his mass–energy \ equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \ Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \ of the photoelectric effect', a pivotal step in the development of quantum theory." }, "doc2" : { "title": "", # Keep title an empty string if not present "text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \ malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\ with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)." }, } queries = { "q1" : "Who developed the mass-energy equivalence formula?", "q2" : "Which beer is brewed with a large proportion of wheat?" } qrels = { "q1" : {"doc1": 1}, "q2" : {"doc2": 1}, } ``` ### Data Fields Examples from all configurations have the following features: ### Corpus - `corpus`: a `dict` feature representing the document title and passage text, made up of: - `_id`: a `string` feature representing the unique document id - `title`: a `string` feature, denoting the title of the document. - `text`: a `string` feature, denoting the text of the document. ### Queries - `queries`: a `dict` feature representing the query, made up of: - `_id`: a `string` feature representing the unique query id - `text`: a `string` feature, denoting the text of the query. ### Qrels - `qrels`: a `dict` feature representing the query document relevance judgements, made up of: - `_id`: a `string` feature representing the query id - `_id`: a `string` feature, denoting the document id. - `score`: a `int32` feature, denoting the relevance judgement between query and document. ### Data Splits | Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 | | -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:| | MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` | | TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` | | NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` | | BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) | | NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` | | HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` | | FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` | | Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) | | TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) | | ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` | | Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` | | CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` | | Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` | | DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` | | SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` | | FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` | | Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` | | SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` | | Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) | ## 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 [Needs More Information] ### Citation Information Cite as: ``` @inproceedings{ thakur2021beir, title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models}, author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych}, booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)}, year={2021}, url={https://openreview.net/forum?id=wCu6T5xFjeJ} } ``` ### Contributions Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.

BeIR/beir-corpus

2022-03-02T23:29:22+00:00

{"annotations_creators": [], "language_creators": [], "language": ["en"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": {"msmarco": ["1M<n<10M"], "trec-covid": ["100k<n<1M"], "nfcorpus": ["1K<n<10K"], "nq": ["1M<n<10M"], "hotpotqa": ["1M<n<10M"], "fiqa": ["10K<n<100K"], "arguana": ["1K<n<10K"], "touche-2020": ["100K<n<1M"], "cqadupstack": ["100K<n<1M"], "quora": ["100K<n<1M"], "dbpedia": ["1M<n<10M"], "scidocs": ["10K<n<100K"], "fever": ["1M<n<10M"], "climate-fever": ["1M<n<10M"], "scifact": ["1K<n<10K"]}, "source_datasets": [], "task_categories": ["text-retrieval", "zero-shot-retrieval", "information-retrieval", "zero-shot-information-retrieval"], "task_ids": ["passage-retrieval", "entity-linking-retrieval", "fact-checking-retrieval", "tweet-retrieval", "citation-prediction-retrieval", "duplication-question-retrieval", "argument-retrieval", "news-retrieval", "biomedical-information-retrieval", "question-answering-retrieval"], "paperswithcode_id": "beir", "pretty_name": "BEIR Benchmark"}

2022-10-21T14:30:07+00:00

TAGS #task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us

Dataset Card for BEIR Benchmark =============================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: URL * Paper: URL * Leaderboard: URL * Point of Contact: URL@URL ### Dataset Summary BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks: * Fact-checking: FEVER, Climate-FEVER, SciFact * Question-Answering: NQ, HotpotQA, FiQA-2018 * Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus * News Retrieval: TREC-NEWS, Robust04 * Argument Retrieval: Touche-2020, ArguAna * Duplicate Question Retrieval: Quora, CqaDupstack * Citation-Prediction: SCIDOCS * Tweet Retrieval: Signal-1M * Entity Retrieval: DBPedia All these datasets have been preprocessed and can be used for your experiments. ### Supported Tasks and Leaderboards The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia. The current best performing models can be found here. ### Languages All tasks are in English ('en'). Dataset Structure ----------------- All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format: * 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{"\_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}' * 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\_id' with unique query identifier and 'text' with query text. For example: '{"\_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}' * 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1' ### Data Instances A high level example of any beir dataset: ### Data Fields Examples from all configurations have the following features: ### Corpus * 'corpus': a 'dict' feature representing the document title and passage text, made up of: + '\_id': a 'string' feature representing the unique document id - 'title': a 'string' feature, denoting the title of the document. - 'text': a 'string' feature, denoting the text of the document. ### Queries * 'queries': a 'dict' feature representing the query, made up of: + '\_id': a 'string' feature representing the unique query id + 'text': a 'string' feature, denoting the text of the query. ### Qrels * 'qrels': a 'dict' feature representing the query document relevance judgements, made up of: + '\_id': a 'string' feature representing the query id - '\_id': a 'string' feature, denoting the document id. - 'score': a 'int32' feature, denoting the relevance judgement between query and document. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information Cite as: ### Contributions Thanks to @Nthakur20 for adding this dataset.

78edba255941a9f67828b606e79a08e497c9298f

# Dataset Card for BEIR Benchmark ## 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/UKPLab/beir - **Repository:** https://github.com/UKPLab/beir - **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ - **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns - **Point of Contact:** nandan.thakur@uwaterloo.ca ### Dataset Summary BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks: - Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact) - Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/) - Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) - News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html) - Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data) - Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) - Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs) - Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html) - Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/) All these datasets have been preprocessed and can be used for your experiments. ```python ``` ### Supported Tasks and Leaderboards The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia. The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/). ### Languages All tasks are in English (`en`). ## Dataset Structure All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format: - `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}` - `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}` - `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1` ### Data Instances A high level example of any beir dataset: ```python corpus = { "doc1" : { "title": "Albert Einstein", "text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \ one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \ its influence on the philosophy of science. He is best known to the general public for his mass–energy \ equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \ Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \ of the photoelectric effect', a pivotal step in the development of quantum theory." }, "doc2" : { "title": "", # Keep title an empty string if not present "text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \ malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\ with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)." }, } queries = { "q1" : "Who developed the mass-energy equivalence formula?", "q2" : "Which beer is brewed with a large proportion of wheat?" } qrels = { "q1" : {"doc1": 1}, "q2" : {"doc2": 1}, } ``` ### Data Fields Examples from all configurations have the following features: ### Corpus - `corpus`: a `dict` feature representing the document title and passage text, made up of: - `_id`: a `string` feature representing the unique document id - `title`: a `string` feature, denoting the title of the document. - `text`: a `string` feature, denoting the text of the document. ### Queries - `queries`: a `dict` feature representing the query, made up of: - `_id`: a `string` feature representing the unique query id - `text`: a `string` feature, denoting the text of the query. ### Qrels - `qrels`: a `dict` feature representing the query document relevance judgements, made up of: - `_id`: a `string` feature representing the query id - `_id`: a `string` feature, denoting the document id. - `score`: a `int32` feature, denoting the relevance judgement between query and document. ### Data Splits | Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 | | -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:| | MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` | | TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` | | NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` | | BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) | | NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` | | HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` | | FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` | | Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) | | TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) | | ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` | | Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` | | CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` | | Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` | | DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` | | SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` | | FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` | | Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` | | SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` | | Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) | ## 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 [Needs More Information] ### Citation Information Cite as: ``` @inproceedings{ thakur2021beir, title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models}, author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych}, booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)}, year={2021}, url={https://openreview.net/forum?id=wCu6T5xFjeJ} } ``` ### Contributions Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.

BeIR/beir

2022-03-02T23:29:22+00:00

{"annotations_creators": [], "language_creators": [], "language": ["en"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": {"msmarco": ["1M<n<10M"], "trec-covid": ["100k<n<1M"], "nfcorpus": ["1K<n<10K"], "nq": ["1M<n<10M"], "hotpotqa": ["1M<n<10M"], "fiqa": ["10K<n<100K"], "arguana": ["1K<n<10K"], "touche-2020": ["100K<n<1M"], "cqadupstack": ["100K<n<1M"], "quora": ["100K<n<1M"], "dbpedia": ["1M<n<10M"], "scidocs": ["10K<n<100K"], "fever": ["1M<n<10M"], "climate-fever": ["1M<n<10M"], "scifact": ["1K<n<10K"]}, "source_datasets": [], "task_categories": ["text-retrieval", "zero-shot-retrieval", "information-retrieval", "zero-shot-information-retrieval"], "task_ids": ["passage-retrieval", "entity-linking-retrieval", "fact-checking-retrieval", "tweet-retrieval", "citation-prediction-retrieval", "duplication-question-retrieval", "argument-retrieval", "news-retrieval", "biomedical-information-retrieval", "question-answering-retrieval"], "paperswithcode_id": "beir", "pretty_name": "BEIR Benchmark"}

2022-10-21T14:30:43+00:00

TAGS #task_categories-text-retrieval #task_ids-entity-linking-retrieval #task_ids-fact-checking-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us

Dataset Card for BEIR Benchmark =============================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: URL * Paper: URL * Leaderboard: URL * Point of Contact: URL@URL ### Dataset Summary BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks: * Fact-checking: FEVER, Climate-FEVER, SciFact * Question-Answering: NQ, HotpotQA, FiQA-2018 * Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus * News Retrieval: TREC-NEWS, Robust04 * Argument Retrieval: Touche-2020, ArguAna * Duplicate Question Retrieval: Quora, CqaDupstack * Citation-Prediction: SCIDOCS * Tweet Retrieval: Signal-1M * Entity Retrieval: DBPedia All these datasets have been preprocessed and can be used for your experiments. ### Supported Tasks and Leaderboards The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia. The current best performing models can be found here. ### Languages All tasks are in English ('en'). Dataset Structure ----------------- All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format: * 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{"\_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}' * 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\_id' with unique query identifier and 'text' with query text. For example: '{"\_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}' * 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1' ### Data Instances A high level example of any beir dataset: ### Data Fields Examples from all configurations have the following features: ### Corpus * 'corpus': a 'dict' feature representing the document title and passage text, made up of: + '\_id': a 'string' feature representing the unique document id - 'title': a 'string' feature, denoting the title of the document. - 'text': a 'string' feature, denoting the text of the document. ### Queries * 'queries': a 'dict' feature representing the query, made up of: + '\_id': a 'string' feature representing the unique query id + 'text': a 'string' feature, denoting the text of the query. ### Qrels * 'qrels': a 'dict' feature representing the query document relevance judgements, made up of: + '\_id': a 'string' feature representing the query id - '\_id': a 'string' feature, denoting the document id. - 'score': a 'int32' feature, denoting the relevance judgement between query and document. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information Cite as: ### Contributions Thanks to @Nthakur20 for adding this dataset.

f80e65ea3922bfab04afe8f4a07a8ab16bd81553

# Dataset Card for [Needs More Information] ## 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-instances) - [Data Splits](#data-instances) - [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) ## Dataset Description - **Homepage:** [Web interface of the Pangloss Collection, which hosts the data sets](https://pangloss.cnrs.fr/) - **Repository:** [GithHub repository of the Pangloss Collection, which hosts the data sets](https://github.com/CNRS-LACITO/Pangloss/) - **Paper:** [A paper about the Pangloss Collection, including a presentation of the Document Type Definition](https://halshs.archives-ouvertes.fr/halshs-01003734) [A paper in French about the deposit in Zenodo](https://halshs.archives-ouvertes.fr/halshs-03475436) - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Benjamin Galliot](mailto:b.g01lyon@gmail.com) ### Dataset Summary Two audio corpora of minority languages of China (Japhug and Na), with transcriptions, proposed as reference data sets for experiments in Natural Language Processing. The data, collected and transcribed in the course of immersion fieldwork, amount to a total of about 1,900 minutes in Japhug and 200 minutes in Na. By making them available in an easily accessible and usable form, we hope to facilitate the development and deployment of state-of-the-art NLP tools for the full range of human languages. There is an associated tool for assembling datasets from the Pangloss Collection (an open archive) in a way that ensures full reproducibility of experiments conducted on these data. The Document Type Definition for the XML files is available here: http://cocoon.huma-num.fr/schemas/Archive.dtd ### Supported Tasks and Leaderboards [Needs More Information] ### Languages Japhug (ISO 639-3 code: jya, Glottolog language code: japh1234) and Yongning Na (ISO 639-3 code: nru, Glottolog language code: yong1288) are two minority languages of China. The documents in the dataset have a transcription in the endangered language. Some of the documents have translations into French, English, and Chinese. ## Dataset Structure ### Data Instances A typical data row includes the path, audio, sentence, document type and several translations (depending on the sub-corpus). ` { "path": "cocoon-db3cf0e1-30bb-3225-b012-019252bb4f4d_C1/Tone_BodyPartsOfAnimals_12_F4_2008_withEGG_069.wav", "audio": "{'path': 'na/cocoon-db3cf0e1-30bb-3225-b012-019252bb4f4d_C1/Tone_BodyPartsOfAnimals_12_F4_2008_withEGG_069.wav', 'array': array([0.00018311, 0.00015259, 0.00021362, ..., 0.00030518, 0.00030518, 0.00054932], dtype=float32), 'sampling_rate': 16000}", "sentence": "ʈʂʰɯ˧ | ɖɤ˧mi˧-ɬi˧pi˩ ɲi˩", "doctype": "WORDLIST", "translation:zh": "狐狸的耳朵", "translation:fr": "oreilles de renard", "translation:en": "fox's ears", } ` ### Data Fields path: the path to the audio file;; audio: a dictionary containing the path to the audio file, the audio array and the sampling rate; sentence: the sentence the native has pronunced; doctype: the document type (a text or a word list); translation:XX: the translation of the sentence in the language XX. ### Data Splits The train, test and validation splits have all been reviewed and were splitted randomly (ratio 8:1:1) at sentence level (after the extraction from various files). ## 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 The dataset was collected in immersion fieldwork for language documentation. It contributes to the documentation and study of the world's languages by providing documents of connected, spontaneous speech recorded in their cultural context and transcribed in consultation with native speakers. The impacts concern research, and society at large: a guiding principle of the Pangloss Collection, which hosts the data sets, is that a close association between documentation and research is highly profitable to both. A range of possibilities for uses exist, for the scientific and speaker communities and for the general public. ### Discussion of Biases The corpora are single-speaker and hence clearly do not reflect the sociolinguistic and dialectal diversity of the languages. No claim is made that the language variety described constitutes a 'standard'. ### Other Known Limitations The translations are entirely hand-made by experts working on these languages; the amount and type of translations available varies from document to document, as not all documents have translations and not all translated documents have the same translation languages (Chinese, French, English...). ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information [Needs More Information]

Lacito/pangloss

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["jya", "nru"], "license": "cc-by-nc-sa-4.0", "multilinguality": ["multilingual", "translation"], "size_categories": {"yong1288": ["10K<n<100K"], "japh1234": ["10K<n<100K"]}, "source_datasets": ["original"], "task_categories": ["automatic-speech-recognition"], "task_ids": ["speech-recognition"], "pretty_name": "Pangloss", "language_bcp47": ["x-japh1234", "x-yong1288"], "language_details": "jya consists of japh1234 (Glottolog code); nru consists of yong1288 (Glottolog code)"}

2022-09-06T17:02:34+00:00

TAGS #task_categories-automatic-speech-recognition #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-multilingual #multilinguality-translation #source_datasets-original #language-Jiarong #language-Narua #license-cc-by-nc-sa-4.0 #region-us

# Dataset Card for ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information ## Dataset Description - Homepage: Web interface of the Pangloss Collection, which hosts the data sets - Repository: GithHub repository of the Pangloss Collection, which hosts the data sets - Paper: A paper about the Pangloss Collection, including a presentation of the Document Type Definition A paper in French about the deposit in Zenodo - Leaderboard: - Point of Contact: Benjamin Galliot ### Dataset Summary Two audio corpora of minority languages of China (Japhug and Na), with transcriptions, proposed as reference data sets for experiments in Natural Language Processing. The data, collected and transcribed in the course of immersion fieldwork, amount to a total of about 1,900 minutes in Japhug and 200 minutes in Na. By making them available in an easily accessible and usable form, we hope to facilitate the development and deployment of state-of-the-art NLP tools for the full range of human languages. There is an associated tool for assembling datasets from the Pangloss Collection (an open archive) in a way that ensures full reproducibility of experiments conducted on these data. The Document Type Definition for the XML files is available here: URL ### Supported Tasks and Leaderboards ### Languages Japhug (ISO 639-3 code: jya, Glottolog language code: japh1234) and Yongning Na (ISO 639-3 code: nru, Glottolog language code: yong1288) are two minority languages of China. The documents in the dataset have a transcription in the endangered language. Some of the documents have translations into French, English, and Chinese. ## Dataset Structure ### Data Instances A typical data row includes the path, audio, sentence, document type and several translations (depending on the sub-corpus). ' { "path": "cocoon-db3cf0e1-30bb-3225-b012-019252bb4f4d_C1/Tone_BodyPartsOfAnimals_12_F4_2008_withEGG_069.wav", "audio": "{'path': 'na/cocoon-db3cf0e1-30bb-3225-b012-019252bb4f4d_C1/Tone_BodyPartsOfAnimals_12_F4_2008_withEGG_069.wav', 'array': array([0.00018311, 0.00015259, 0.00021362, ..., 0.00030518, 0.00030518, 0.00054932], dtype=float32), 'sampling_rate': 16000}", "sentence": "ʈʂʰɯ˧ | ɖɤ˧mi˧-ɬi˧pi˩ ɲi˩", "doctype": "WORDLIST", "translation:zh": "狐狸的耳朵", "translation:fr": "oreilles de renard", "translation:en": "fox's ears", } ' ### Data Fields path: the path to the audio file;; audio: a dictionary containing the path to the audio file, the audio array and the sampling rate; sentence: the sentence the native has pronunced; doctype: the document type (a text or a word list); translation:XX: the translation of the sentence in the language XX. ### Data Splits The train, test and validation splits have all been reviewed and were splitted randomly (ratio 8:1:1) at sentence level (after the extraction from various files). ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset The dataset was collected in immersion fieldwork for language documentation. It contributes to the documentation and study of the world's languages by providing documents of connected, spontaneous speech recorded in their cultural context and transcribed in consultation with native speakers. The impacts concern research, and society at large: a guiding principle of the Pangloss Collection, which hosts the data sets, is that a close association between documentation and research is highly profitable to both. A range of possibilities for uses exist, for the scientific and speaker communities and for the general public. ### Discussion of Biases The corpora are single-speaker and hence clearly do not reflect the sociolinguistic and dialectal diversity of the languages. No claim is made that the language variety described constitutes a 'standard'. ### Other Known Limitations The translations are entirely hand-made by experts working on these languages; the amount and type of translations available varies from document to document, as not all documents have translations and not all translated documents have the same translation languages (Chinese, French, English...). ## Additional Information ### Dataset Curators ### Licensing Information

66bfd05a6720c6eb0d274779cec9b0632622c682

# Dataset Card for EThOS PhD metadata ## Table of Contents - [Dataset Card for blbooksgenre](#dataset-card-for-EThOS PhD metadata) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Supervised tasks](#supervised-tasks) - [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://bl.iro.bl.uk/concern/datasets/c815b271-09be-4123-8156-405094429198?locale=en - **Repository:** https://doi.org/10.23636/ybpt-nh33 - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The data in this collection comprises the bibliographic metadata for all UK doctoral theses listed in EThOS, the UK's national thesis service. We estimate the data covers around 98% of all PhDs ever awarded by UK Higher Education institutions, dating back to 1787. Thesis metadata from every PhD-awarding university in the UK is included. You can investigate and re-use this unique collection of UK universities' PhD thesis data to analyse trends in postgraduate research, make connections between researchers, apply large data analysis, improve citation of theses and many more applications. [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] #### Supervised tasks [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure [More Information Needed] ### Data Instances An example data instance: ```python {'Abstract': ' ', 'Author': 'Loizou, Panos A.', 'Author ISNI': 'https://isni.org/isni/0000000136122593', 'DOI': ' ', 'Date': datetime.datetime(1989, 1, 1, 0, 0), 'EThOS URL': 'https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.232781', 'Funder(s)': ' ', 'IR URL': ' ', 'Institution': 'University of Manchester', 'Institution ISNI': 'https://isni.org/isni/0000000121662407', 'ORCID': ' ', 'Qualification': 'Thesis (Ph.D.)', 'Subject Discipline': 0, 'Supervisor(s)': ' ', 'Title': 'Computation and measurement of turbulent flow through idealized turbine blade passages'} ``` ### Data Fields [More Information Needed] ### Data Splits This dataset contains a single split `train`. ## Dataset Creation [More Information Needed] ### 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 [More Information Needed] ### 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 books are licensed under the [CC BY 4.0 Attribution](https://creativecommons.org/licenses/by/4.0/) license. ### Citation Information

TheBritishLibrary/EThOS-PhD-metadata

2022-03-02T23:29:22+00:00

{"annotations_creators": [], "language_creators": [], "language": ["en"], "license": [], "multilinguality": ["monolingual"], "size_categories": [], "source_datasets": [], "task_categories": ["text-classification", "fill-mask"], "task_ids": ["multi-label-classification", "masked-language-modeling"], "pretty_name": "EThOS PhD metadata", "tags": []}

2022-07-23T20:14:57+00:00

TAGS #task_categories-text-classification #task_categories-fill-mask #task_ids-multi-label-classification #task_ids-masked-language-modeling #multilinguality-monolingual #language-English #region-us

# Dataset Card for EThOS PhD metadata ## Table of Contents - Dataset Card for blbooksgenre - Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Supervised tasks - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Initial Data Collection and Normalization - Who are the source language producers? - Annotations - Annotation process - Who are the annotators? - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage:: URL - Repository: URL - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary The data in this collection comprises the bibliographic metadata for all UK doctoral theses listed in EThOS, the UK's national thesis service. We estimate the data covers around 98% of all PhDs ever awarded by UK Higher Education institutions, dating back to 1787. Thesis metadata from every PhD-awarding university in the UK is included. You can investigate and re-use this unique collection of UK universities' PhD thesis data to analyse trends in postgraduate research, make connections between researchers, apply large data analysis, improve citation of theses and many more applications. ### Supported Tasks and Leaderboards #### Supervised tasks ### Languages ## Dataset Structure ### Data Instances An example data instance: ### Data Fields ### Data Splits This dataset contains a single split 'train'. ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information The books are licensed under the CC BY 4.0 Attribution license.

f002ce337df0b0f1fb96f87edd17b2ebe4a73963

welcoe to cager data set

CAGER/rick

2022-03-02T23:29:22+00:00

{}

2021-07-09T01:05:44+00:00

TAGS #region-us

welcoe to cager data set

36dcd9cf86cfc635866ec63d58ef5ee25885ac4d

# Arabic Wiki Dataset ## Dataset Summary This dataset is extracted using [`wikiextractor`](https://github.com/attardi/wikiextractor) tool, from [Wikipedia Arabic pages](https://dumps.wikimedia.org/arwiki/). ## Supported Tasks and Leaderboards Intended to train **Arabic** language models on MSA (Modern Standard Arabic). ## Dataset Structure The dataset is structured into 2 folders: - `arwiki_20211213_txt`: dataset is divided into subfolders each of which contains no more than 100 documents. - `arwiki_20211213_txt_single`: all documents merged together in a single txt file. ## Dataset Statistics #### Extracts from **December 13, 2021**: | documents | vocabulary | words | | --- | --- | --- | | 1,136,455 | 5,446,560 | 175,566,016 | ## Usage Load all dataset from the single txt file: ```python load_dataset('CALM/arwiki', data_files='arwiki_2021_txt_single/arwiki_20211213.txt') # OR with stream load_dataset('CALM/arwiki', data_files='arwiki_2021_txt_single/arwiki_20211213.txt', streaming=True) ``` Load a smaller subset from the individual txt files: ```python load_dataset('CALM/arwiki', data_files='arwiki_2021_txt/AA/arwiki_20211213_1208.txt') # OR with stream load_dataset('CALM/arwiki', data_files='arwiki_2021_txt/AA/arwiki_20211213_1208.txt', streaming=True) ```

CALM/arwiki

2022-03-02T23:29:22+00:00

{"language": ["ar"], "license": ["unknown"], "multilinguality": ["monolingual"], "pretty_name": "Wikipedia Arabic dumps dataset."}

2022-08-01T15:37:23+00:00

TAGS #multilinguality-monolingual #language-Arabic #license-unknown #region-us

Arabic Wiki Dataset =================== Dataset Summary --------------- This dataset is extracted using 'wikiextractor' tool, from Wikipedia Arabic pages. Supported Tasks and Leaderboards -------------------------------- Intended to train Arabic language models on MSA (Modern Standard Arabic). Dataset Structure ----------------- The dataset is structured into 2 folders: * 'arwiki\_20211213\_txt': dataset is divided into subfolders each of which contains no more than 100 documents. * 'arwiki\_20211213\_txt\_single': all documents merged together in a single txt file. Dataset Statistics ------------------ #### Extracts from December 13, 2021: documents: 1,136,455, vocabulary: 5,446,560, words: 175,566,016 Usage ----- Load all dataset from the single txt file: Load a smaller subset from the individual txt files:

b966160952fc5fe9cb036f098dd44f135d5c6f20

# Dataset Card for ASCEND ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Usage](#usage) - [Dataset Structure](#dataset-structure) - [Data Splits](#data-instances) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - **Homepage:** [Needs More Information] - **Repository:** [Needs More Information] - **Paper:** https://arxiv.org/abs/2112.06223 - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary ASCEND (A Spontaneous Chinese-English Dataset) introduces a high-quality resource of spontaneous multi-turn conversational dialogue Chinese-English code-switching corpus collected in Hong Kong. ASCEND consists of 10.62 hours of spontaneous speech with a total of ~12.3K utterances. The corpus is split into 3 sets: training, validation, and test with a ratio of 8:1:1 while maintaining a balanced gender proportion on each set. ### Supported Tasks and Leaderboards Code-switching ### Languages Chinese and English ## Usage To obtain the full dataset (complete with train, validation, and test set), simply run this: ``` import datasets dataset = datasets.load_dataset("CAiRE/ASCEND") ``` ## Dataset Structure A typical data point comprises the path to the audio file, the loaded audio array, and its transcription. Additional fields include datapoint id, duration, language, speaker id, session id, and topic. ``` { 'id': '00644', 'path': '.cache/huggingface/datasets/downloads/extracted/f0b33b5266cd9452ee310eef3577cf7adb7f29aa54dbff74b9a8ee406a55d614/waves/ses2_spk3_L13101_189.900_5.490.wav', 'audio': { 'path': '.cache/huggingface/datasets/downloads/extracted/f0b33b5266cd9452ee310eef3577cf7adb7f29aa54dbff74b9a8ee406a55d614/waves/ses2_spk3_L13101_189.900_5.490.wav', 'array': array([-6.1035156e-05, -1.8310547e-04, 3.0517578e-05, ..., 0.0000000e+00, -3.0517578e-05, 0.0000000e+00 ], dtype = float32), 'sampling_rate': 16000 }, 'transcription': '因为你不可能邀你的female friends去说走我们去play basketball', 'duration': 5.489999771118164, 'language': 'mixed', 'original_speaker_id': 3, 'session_id': 2, 'topic': 'sports' } ``` ### Data Splits Number of utterances: 9,869 train, 1,130 validation, and 1,315 test. ## Additional Information For comprehensive explanations, please check [our paper](https://arxiv.org/pdf/2112.06223.pdf). ### Licensing Information Creative Common Attribution Share-Alike 4.0 International (CC-BY-SA 4.0) ### Citation Information If you use our dataset, please cite us: ``` @inproceedings{lovenia2022ascend, title={ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversation}, author={Lovenia, Holy and Cahyawijaya, Samuel and Winata, Genta Indra and Xu, Peng and Yan, Xu and Liu, Zihan and Frieske, Rita and Yu, Tiezheng and Dai, Wenliang and Barezi, Elham J and others}, booktitle={Proceedings of the 13th Language Resources and Evaluation Conference (LREC)}, year={2022} ```

CAiRE/ASCEND

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["crowdsourced"], "language": ["en", "zh"], "license": ["cc-by-sa-4.0"], "multilinguality": ["multilingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["automatic-speech-recognition"], "task_ids": [], "pretty_name": "ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversation", "tags": ["speech-recognition", "code-switching"]}

2022-10-24T11:43:58+00:00

TAGS #task_categories-automatic-speech-recognition #annotations_creators-expert-generated #language_creators-crowdsourced #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-original #language-English #language-Chinese #license-cc-by-sa-4.0 #speech-recognition #code-switching #arxiv-2112.06223 #region-us

# Dataset Card for ASCEND ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks - Languages - Usage - Dataset Structure - Data Splits - Additional Information - Licensing Information - Citation Information ## Dataset Description - Homepage: - Repository: - Paper: URL - Leaderboard: - Point of Contact: ### Dataset Summary ASCEND (A Spontaneous Chinese-English Dataset) introduces a high-quality resource of spontaneous multi-turn conversational dialogue Chinese-English code-switching corpus collected in Hong Kong. ASCEND consists of 10.62 hours of spontaneous speech with a total of ~12.3K utterances. The corpus is split into 3 sets: training, validation, and test with a ratio of 8:1:1 while maintaining a balanced gender proportion on each set. ### Supported Tasks and Leaderboards Code-switching ### Languages Chinese and English ## Usage To obtain the full dataset (complete with train, validation, and test set), simply run this: ## Dataset Structure A typical data point comprises the path to the audio file, the loaded audio array, and its transcription. Additional fields include datapoint id, duration, language, speaker id, session id, and topic. ### Data Splits Number of utterances: 9,869 train, 1,130 validation, and 1,315 test. ## Additional Information For comprehensive explanations, please check our paper. ### Licensing Information Creative Common Attribution Share-Alike 4.0 International (CC-BY-SA 4.0) If you use our dataset, please cite us:

563ad7cd6591c8a51a807ce073645e349ce92fa8

<h1>KerasBERT</h1> <ul> <li>All Data</li> <li>Keras API Docs</li> <li>Keras Developer Guides</li> <li>Keras Code Examples</li> </ul> Please cite KerasBERT: Modeling the Keras Language, Connor Shorten and Taghi M. Khoshgoftaar. https://ieeexplore.ieee.org/abstract/document/9679980.

CShorten/KerasBERT

2022-03-02T23:29:22+00:00

{}

2022-06-28T10:51:07+00:00

TAGS #region-us

<h1>KerasBERT</h1> <ul> <li>All Data</li> <li>Keras API Docs</li> <li>Keras Developer Guides</li> <li>Keras Code Examples</li> </ul> Please cite KerasBERT: Modeling the Keras Language, Connor Shorten and Taghi M. Khoshgoftaar. URL

d0be437cfd0d5d653ffd582514f55cb1f039ba16

Repo to share original and anonymized speech of vpc2020

Champion/vpc2020_clear_anon_speech

2022-03-02T23:29:22+00:00

{}

2021-10-12T13:19:45+00:00

TAGS #region-us

Repo to share original and anonymized speech of vpc2020

f496e251dd03afb88f8a19b8bdb47b6ae41a2408

A translation dataset between english and traditional chinese train : 101497 rows val : 1000 rows test : 1000 rows

Chun/dataset

2022-03-02T23:29:22+00:00

{}

2021-08-24T07:16:33+00:00

TAGS #region-us

A translation dataset between english and traditional chinese train : 101497 rows val : 1000 rows test : 1000 rows

9cca1f325b9f97066a2306b17e24f7d24ac9d42a

# Dataset Card for Code Clippy Data ## 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://the-eye.eu/public/AI/training_data/code_clippy_data/ - **Repository:** https://github.com/ncoop57/gpt-code-clippy - **Paper:** [Not yet :)] - **Leaderboard:** [Not yet :)] - **Point of Contact:** [Nathan Cooper](mailto@nacooper01@email.wm.edu) ### Dataset Summary This dataset was generated by selecting GitHub repositories from a large collection of repositories. These repositories were collected from https://seart-ghs.si.usi.ch/ and Github portion of [The Pile](https://github.com/EleutherAI/github-downloader) (performed on July 7th, 2021). The goal of this dataset is to provide a training set for pretraining large language models on code data for helping software engineering researchers better understand their impacts on software related tasks such as autocompletion of code. The dataset is split into train, validation, and test splits. There is a version containing duplicates (209GBs compressed) and ones where exact duplicates (132GBs compressed) are removed. Contains mostly JavaScript and Python code, but other programming languages are included as well to various degrees. ### Supported Tasks and Leaderboards - `language-modeling`: The dataset can be used to train a model for language modeling for modeling programming languages, which consists of pretraining/finetuning a model to predict missing tokens, either causally or masked, given some context. Success on this task is typically measured by achieving a *low* perplexity score. ### Languages Multiple programming languages are included in the dataset. ## Dataset Structure ### Data Instances ``` { "id": datasets.Value("int64"), "text": datasets.Value("string"), "repo_name": datasets.Value("string"), "stars": datasets.Value("string"), "repo_language": datasets.Value("string"), "file_name": datasets.Value("string"), "mime_type": datasets.Value("string") } ``` ### Data Fields - `id`: A unique identifier for the data instance. - `text`: The text of the code. - `repo_name`: The name of the repository. - `stars`: The number of stars the repository has. - `repo_language`: The programming language of the repository. - `file_name`: The name of the file. - `mime_type`: The MIME type of the file. ### Data Splits | Size in GBs | Tain | Valid | Test | | ----- | ------ | ----- | ---- | | Duplicate | 194 | 9 | 6.3 | | Deduplicate | 126 | 3.3 | 3.1 | ## Dataset Creation ### Curation Rationale To have a code dataset that is large enough to properly train a large language model on. ### Source Data #### Initial Data Collection and Normalization - [The Pile](https://github.com/EleutherAI/github-downloader) - [Seart-GHS](https://seart-ghs.si.usi.ch/) Repositories were collected from both sources and the helper script from https://github.com/EleutherAI/github-downloader was used to download the repositories. Files were scrapped from the downloaded repositories, but ignored files that had certain extensions associated with binary or other non-textual/autogenerated content, and the output was converted into the [LM_Dataformat](https://pypi.org/project/lm-dataformat/) format. #### Who are the source language producers? Software developers. ### Annotations #### Annotation process No annotation was performed. #### Who are the annotators? N/A ### Personal and Sensitive Information Since this data was collected from public repositories, there exists potential for personal and sensitive information to be included in the data through developers accidentally or on purpose uploading their secret keys, passwords, API keys, emails, etc. ## Considerations for Using the Data ### Social Impact of Dataset The paper ["Evaluating Large Language Models Trained on Code"](https://arxiv.org/abs/2107.03374) from OpenAI has a good discussion on what the impact of a large language model trained on code could be. Therefore, some parts of their discuss are highlighted here as it pertains to this dataset and models that may be trained from it. **As well as some differences in views from the paper, particularly around legal implications**. 1. **Over-reliance:** A language model trained on large datasets such as this one for the task of autogenerating code may generate plausible solutions that may appear correct, but are not necessarily the correct solution. Not properly evaluating the generated code may cause have negative consequences such as the introduction of bugs, or the introduction of security vulnerabilities. Therefore, it is important that users are aware of the limitations and potential negative consequences of using a language model trained on this dataset. 2. **Economic and labor market impacts:** Large language models trained on large code datasets such as this one that are capable of generating high-quality code have the potential to automate part of the software development process. This may negatively impact software developers. However, as discussed in the paper, as shown in the Summary Report of software developers from [O*NET OnLine](https://www.onetonline.org/link/summary/15-1252.00), developers don't just write software. 3. **Security implications:** No filtering or checking of vulnerabilities or buggy code was performed. This means that the dataset may contain code that may be malicious or contain vulnerabilities. Therefore, any model trained on this dataset may generate vulnerable, buggy, or malicious code. In safety critical software, this could lead to software that may work improperly and could result in serious consequences depending on the software. Additionally, a model trained on this dataset may be used to generate malicious code on purpose in order to perform ransomware or other such attacks. 4. **Legal implications:** No filtering was performed on licensed code. This means that the dataset may contain restrictive licensed code. As discussed in the paper, public Github repositories may fall under "fair use." However, there has been little to no previous cases of such usages of licensed publicly available code. Therefore, any model trained on this dataset may be required to obey license terms that align with the software it was trained on such as GPL-3.0, which is why we purposefully put this dataset under the GPL-3.0 license. It is unclear the legal ramifications of using a language model trained on this dataset. ### Discussion of Biases The programming languages most represented in this dataset are those of Javascript and Python. Therefore, other, still popular languages such as C and C++, are less represented and therefore model performance for these languages will be less comparatively. Additionally, this dataset only contains public repositories and so may not be representative of code written by private developers. No filtering was performed for potential racist, offensive, or otherwise inappropriate content. Therefore there may be such content in the dataset that will be reflected in models trained on it. ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Nathan Coooper, Artashes Arutiunian, Santiago Hincapié-Potes, Ben Trevett, Arun Raja, Erfan Hossami, Mrinal Mathur, and contributors! ### Licensing Information This repository is under the GPL-3.0 license. ### Citation Information ``` @misc{cooper-2021-code-clippy-data, author = {Nathan Coooper, Artashes Arutiunian, Santiago Hincapié-Potes, Ben Trevett, Arun Raja, Erfan Hossami, Mrinal Mathur, and contributors}, title = {{Code Clippy Data: A large dataset of code data from Github for research into code language models}}, month = jul, year = 2021, version = {1.0}, publisher = {GitHub}, url = {https://github.com/ncoop57/gpt-code-clippy} } ``` ### Contributions Thanks to [@ncoop57](https://github.com/ncoop57), [@arampacha](https://github.com/arampacha), [@shpotes](https://github.com/shpotes), [@bentrevett](https://github.com/bentrevett), [@arunraja-hub](https://github.com/arunraja-hub), [@taisazero](https://github.com/taisazero), [@Mrinal18](https://github.com/Mrinal18), and contributors for adding this dataset.

CodedotAI/code_clippy

2022-03-02T23:29:22+00:00

{"annotations_creators": ["no-annotation"], "language_creators": ["crowdsourced"], "language": ["code"], "license": ["gpl-3.0"], "multilinguality": ["multilingual"], "size_categories": ["unknown"], "source_datasets": ["original"], "task_categories": ["text-generation"], "task_ids": ["language-modeling"], "pretty_name": "Code Clippy"}

2022-11-17T19:54:28+00:00

TAGS #task_categories-text-generation #task_ids-language-modeling #annotations_creators-no-annotation #language_creators-crowdsourced #multilinguality-multilingual #size_categories-unknown #source_datasets-original #language-code #license-gpl-3.0 #arxiv-2107.03374 #region-us

Dataset Card for Code Clippy Data ================================= Table of Contents ----------------- * Table of Contents * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: URL * Paper: [Not yet :)] * Leaderboard: [Not yet :)] * Point of Contact: Nathan Cooper ### Dataset Summary This dataset was generated by selecting GitHub repositories from a large collection of repositories. These repositories were collected from URL and Github portion of The Pile (performed on July 7th, 2021). The goal of this dataset is to provide a training set for pretraining large language models on code data for helping software engineering researchers better understand their impacts on software related tasks such as autocompletion of code. The dataset is split into train, validation, and test splits. There is a version containing duplicates (209GBs compressed) and ones where exact duplicates (132GBs compressed) are removed. Contains mostly JavaScript and Python code, but other programming languages are included as well to various degrees. ### Supported Tasks and Leaderboards * 'language-modeling': The dataset can be used to train a model for language modeling for modeling programming languages, which consists of pretraining/finetuning a model to predict missing tokens, either causally or masked, given some context. Success on this task is typically measured by achieving a *low* perplexity score. ### Languages Multiple programming languages are included in the dataset. Dataset Structure ----------------- ### Data Instances ### Data Fields * 'id': A unique identifier for the data instance. * 'text': The text of the code. * 'repo\_name': The name of the repository. * 'stars': The number of stars the repository has. * 'repo\_language': The programming language of the repository. * 'file\_name': The name of the file. * 'mime\_type': The MIME type of the file. ### Data Splits Dataset Creation ---------------- ### Curation Rationale To have a code dataset that is large enough to properly train a large language model on. ### Source Data #### Initial Data Collection and Normalization * The Pile * Seart-GHS Repositories were collected from both sources and the helper script from URL was used to download the repositories. Files were scrapped from the downloaded repositories, but ignored files that had certain extensions associated with binary or other non-textual/autogenerated content, and the output was converted into the LM\_Dataformat format. #### Who are the source language producers? Software developers. ### Annotations #### Annotation process No annotation was performed. #### Who are the annotators? N/A ### Personal and Sensitive Information Since this data was collected from public repositories, there exists potential for personal and sensitive information to be included in the data through developers accidentally or on purpose uploading their secret keys, passwords, API keys, emails, etc. Considerations for Using the Data --------------------------------- ### Social Impact of Dataset The paper "Evaluating Large Language Models Trained on Code" from OpenAI has a good discussion on what the impact of a large language model trained on code could be. Therefore, some parts of their discuss are highlighted here as it pertains to this dataset and models that may be trained from it. As well as some differences in views from the paper, particularly around legal implications. 1. Over-reliance: A language model trained on large datasets such as this one for the task of autogenerating code may generate plausible solutions that may appear correct, but are not necessarily the correct solution. Not properly evaluating the generated code may cause have negative consequences such as the introduction of bugs, or the introduction of security vulnerabilities. Therefore, it is important that users are aware of the limitations and potential negative consequences of using a language model trained on this dataset. 2. Economic and labor market impacts: Large language models trained on large code datasets such as this one that are capable of generating high-quality code have the potential to automate part of the software development process. This may negatively impact software developers. However, as discussed in the paper, as shown in the Summary Report of software developers from O\*NET OnLine, developers don't just write software. 3. Security implications: No filtering or checking of vulnerabilities or buggy code was performed. This means that the dataset may contain code that may be malicious or contain vulnerabilities. Therefore, any model trained on this dataset may generate vulnerable, buggy, or malicious code. In safety critical software, this could lead to software that may work improperly and could result in serious consequences depending on the software. Additionally, a model trained on this dataset may be used to generate malicious code on purpose in order to perform ransomware or other such attacks. 4. Legal implications: No filtering was performed on licensed code. This means that the dataset may contain restrictive licensed code. As discussed in the paper, public Github repositories may fall under "fair use." However, there has been little to no previous cases of such usages of licensed publicly available code. Therefore, any model trained on this dataset may be required to obey license terms that align with the software it was trained on such as GPL-3.0, which is why we purposefully put this dataset under the GPL-3.0 license. It is unclear the legal ramifications of using a language model trained on this dataset. ### Discussion of Biases The programming languages most represented in this dataset are those of Javascript and Python. Therefore, other, still popular languages such as C and C++, are less represented and therefore model performance for these languages will be less comparatively. Additionally, this dataset only contains public repositories and so may not be representative of code written by private developers. No filtering was performed for potential racist, offensive, or otherwise inappropriate content. Therefore there may be such content in the dataset that will be reflected in models trained on it. ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators Nathan Coooper, Artashes Arutiunian, Santiago Hincapié-Potes, Ben Trevett, Arun Raja, Erfan Hossami, Mrinal Mathur, and contributors! ### Licensing Information This repository is under the GPL-3.0 license. ### Contributions Thanks to @ncoop57, @arampacha, @shpotes, @bentrevett, @arunraja-hub, @taisazero, @Mrinal18, and contributors for adding this dataset.

cf9f33dec640f45228f8f3f5e6a7899a37f5f83e

# Code Clippy Github Dataset ## Dataset Description The Code Clippy dataset consists of various public codebases from GitHub in 22 programming languages with 23 extensions totaling about 16 TB of data when uncompressed. The dataset was created from the public GitHub dataset on Google BigQuery. ### How to use it This dataset is pretty large please use the streaming parameter from the `datasets` library as seen below: ```python from datasets import load_dataset ds = load_dataset("CodedotAI/code_clippy_github", streaming=True) ``` ## Data Structure ### Data Instances ```python { 'code_text': " a = mc^2", 'repo_name': 'NotEinstein', 'file_path': 'root/users/einstein.py', 'language': 'Python', 'license': 'isc', 'size': 2 } ``` ### Data Fields |Field|Type|Description| |---|---|---| |code_text|string|string of the source code contained in the code file| |repo_name|string|name of the GitHub repository| |file_path|string|path of the code file within the repository | |language|string|programming language used in the file inferred by the file extension| |license|string|license of GitHub repository| |size|int|size of source file in bytes| ### Data Splits Only a train split is provided in this dataset. ## Languages The dataset contains 22 programming languages with over 23 extensions: ```python { "C": [".c"], "C#": [".cs"], "C++": [".cpp"], "CSS": [".css"], "Dart" : [".dart"], "GO": [".go"], "HTML":[".html"], "Java": [".java"], "JavaScript": [".js"], "Jupyter Notebooks (Python)": [".ipynb"], "Kotlin" : [".kt"], "Lisp" : [".lisp"], "Matlab" : [".m"], "PHP": [".php"], "Perl": [".pl"], "Python": [".py"], "R" : [".r"], "Ruby": [".rb"], "Rust": [".rs"], "SQL": [".sql"], "Shell": [".sh"], "Swift" : [".swift"], "TypeScript": [".ts"], } ``` ## Licenses Each example is also annotated with the license of the associated repository. There are in total 15 licenses: ```python [ 'mit', 'apache-2.0', 'gpl-2.0', 'gpl-3.0', 'bsd-3-clause', 'bsd-2-clause', 'unlicense', 'apacheagpl-3.0', 'lgpl-3.0', 'cc0-1.0', 'epl-1.0', 'lgpl-2.1', 'mpl-2.0', 'isc', 'artistic-2.0' ] ``` ## Dataset Statistics The dataset is about ~ 18 TB uncompressed. We are currently working on processing it and applying further filtering. ## Dataset Creation The dataset was created in two steps: 1. Files with the extensions given in the list above were retrieved from the GitHub dataset on BigQuery using the following query: ```sql SELECT f.id, f.repo_name, f.path, content.copies, content.size, content.content, lic.license FROM `bigquery-public-data.github_repos.files` AS f JOIN `bigquery-public-data.github_repos.contents` as content ON f.id = content.id JOIN `bigquery-public-data.github_repos.licenses` AS lic ON f.repo_name = lic.repo_name WHERE NOT content.binary AND ( (f.path LIKE '%.py') OR (f.path LIKE '%.java') OR (f.path LIKE '%.js') OR (f.path LIKE '%.html') OR (f.path LIKE '%.lisp') OR (f.path LIKE '%.sh') OR (f.path LIKE '%.r') OR (f.path LIKE '%.pl') OR (f.path LIKE '%.css') OR (f.path LIKE '%.sql') OR (f.path LIKE '%.c') OR (f.path LIKE '%.cpp') OR (f.path LIKE '%.ts') OR (f.path LIKE '%.cs') OR (f.path LIKE '%.go') OR (f.path LIKE '%.rs') OR (f.path LIKE '%.swift') OR (f.path LIKE '%.php') OR (f.path LIKE '%.dart') OR (f.path LIKE '%.kt') OR (f.path LIKE '%.m') OR (f.path LIKE '%.rb') OR (f.path LIKE '%.ipynb') ) -- make sure we dont go above 1 megabyte AND (content.size BETWEEN 1024 AND 1000000) ``` 2. Currently, our CodedotAI team is working on adding additional filters and cleaning this dataset. ### Personal and Sensitive Information Since this data was collected from public repositories, there exists potential for personal and sensitive information to be included in the data through developers accidentally or on purpose uploading their secret keys, passwords, API keys, emails, etc. ## Considerations for Using the Data ### Social Impact of Dataset The paper ["Evaluating Large Language Models Trained on Code"](https://arxiv.org/abs/2107.03374) from OpenAI has a good discussion on what the impact of a large language model trained on code could be. Therefore, some parts of their discussion are highlighted here as it pertains to this dataset and models that may be trained from it. **As well as some differences in views from the paper, particularly around legal implications**. 1. **Over-reliance:** A language model trained on large datasets such as this one for the task of autogenerating code may generate plausible solutions that may appear correct, but are not necessarily the correct solution. Not properly evaluating the generated code may cause have negative consequences such as the introduction of bugs, or the introduction of security vulnerabilities. Therefore, it is important that users are aware of the limitations and potential negative consequences of using a language model trained on this dataset. 2. **Economic and labor market impacts:** Large language models trained on large code datasets such as this one that are capable of generating high-quality code have the potential to automate part of the software development process. This may negatively impact software developers. However, as discussed in the paper, as shown in the Summary Report of software developers from [O*NET OnLine](https://www.onetonline.org/link/summary/15-1252.00), developers don't just write software. 3. **Security implications:** No filtering or checking of vulnerabilities or buggy code was performed. This means that the dataset may contain code that may be malicious or contain vulnerabilities. Therefore, any model trained on this dataset may generate vulnerable, buggy, or malicious code. In safety-critical software, this could lead to software that may work improperly and could result in serious consequences depending on the software. Additionally, a model trained on this dataset may be used to generate malicious code on purpose in order to perform ransomware or other such attacks. 4. **Legal implications:** No filtering was performed on licensed code. This means that the dataset may contain restrictive licensed code. As discussed in the paper, public Github repositories may fall under "fair use." However, there have been little to no previous cases of such usages of licensed publicly available code. Therefore, any model trained on this dataset may be required to obey license terms that align with the software it was trained on such as GPL-3.0, which is why we purposefully put this dataset under the GPL-3.0 license. It is unclear the legal ramifications of using a language model trained on this dataset. ### v1.0 - The query was executed on _February 1, 2022, 12:15:59 AM EST_ ## Acknowledgements This project would not have been possible without compute generously provided by Google through the [TPU Research Cloud](https://sites.research.google/trc/about/). We would also like to thank [Dr. Razvan Bunescu](https://webpages.charlotte.edu/rbunescu/) and [The College of Computing and Informatics at UNC Charlotte](https://cci.charlotte.edu/) for their generous contributions to this project, specifically in funding the BigQuery and Google Cloud Storage costs. We would also like to thank the [codeparrot team at Hugging face](https://huggingface.co/codeparrot) for open sourcing their documentation on [github-code](https://huggingface.co/datasets/codeparrot/github-code) which we used for the readme in this dataset. For another similar dataset to this please check github-code!

CodedotAI/code_clippy_github

2022-03-02T23:29:22+00:00

{"annotations_creators": [], "language_creators": ["crowdsourced", "expert-generated"], "language": ["code"], "license": ["mit"], "multilinguality": ["multilingual"], "size_categories": ["unknown"], "source_datasets": [], "task_categories": ["sequence-modeling"], "task_ids": ["language-modeling"], "pretty_name": "code-clippy-github-code"}

2022-08-05T01:57:36+00:00

TAGS #task_ids-language-modeling #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-multilingual #size_categories-unknown #language-code #license-mit #arxiv-2107.03374 #region-us

Code Clippy Github Dataset ========================== Dataset Description ------------------- The Code Clippy dataset consists of various public codebases from GitHub in 22 programming languages with 23 extensions totaling about 16 TB of data when uncompressed. The dataset was created from the public GitHub dataset on Google BigQuery. ### How to use it This dataset is pretty large please use the streaming parameter from the 'datasets' library as seen below: Data Structure -------------- ### Data Instances ### Data Fields Field: code\_text, Type: string, Description: string of the source code contained in the code file Field: repo\_name, Type: string, Description: name of the GitHub repository Field: file\_path, Type: string, Description: path of the code file within the repository Field: language, Type: string, Description: programming language used in the file inferred by the file extension Field: license, Type: string, Description: license of GitHub repository Field: size, Type: int, Description: size of source file in bytes ### Data Splits Only a train split is provided in this dataset. Languages --------- The dataset contains 22 programming languages with over 23 extensions: Licenses -------- Each example is also annotated with the license of the associated repository. There are in total 15 licenses: Dataset Statistics ------------------ The dataset is about ~ 18 TB uncompressed. We are currently working on processing it and applying further filtering. Dataset Creation ---------------- The dataset was created in two steps: 1. Files with the extensions given in the list above were retrieved from the GitHub dataset on BigQuery using the following query: 2. Currently, our CodedotAI team is working on adding additional filters and cleaning this dataset. ### Personal and Sensitive Information Since this data was collected from public repositories, there exists potential for personal and sensitive information to be included in the data through developers accidentally or on purpose uploading their secret keys, passwords, API keys, emails, etc. Considerations for Using the Data --------------------------------- ### Social Impact of Dataset The paper "Evaluating Large Language Models Trained on Code" from OpenAI has a good discussion on what the impact of a large language model trained on code could be. Therefore, some parts of their discussion are highlighted here as it pertains to this dataset and models that may be trained from it. As well as some differences in views from the paper, particularly around legal implications. 1. Over-reliance: A language model trained on large datasets such as this one for the task of autogenerating code may generate plausible solutions that may appear correct, but are not necessarily the correct solution. Not properly evaluating the generated code may cause have negative consequences such as the introduction of bugs, or the introduction of security vulnerabilities. Therefore, it is important that users are aware of the limitations and potential negative consequences of using a language model trained on this dataset. 2. Economic and labor market impacts: Large language models trained on large code datasets such as this one that are capable of generating high-quality code have the potential to automate part of the software development process. This may negatively impact software developers. However, as discussed in the paper, as shown in the Summary Report of software developers from O\*NET OnLine, developers don't just write software. 3. Security implications: No filtering or checking of vulnerabilities or buggy code was performed. This means that the dataset may contain code that may be malicious or contain vulnerabilities. Therefore, any model trained on this dataset may generate vulnerable, buggy, or malicious code. In safety-critical software, this could lead to software that may work improperly and could result in serious consequences depending on the software. Additionally, a model trained on this dataset may be used to generate malicious code on purpose in order to perform ransomware or other such attacks. 4. Legal implications: No filtering was performed on licensed code. This means that the dataset may contain restrictive licensed code. As discussed in the paper, public Github repositories may fall under "fair use." However, there have been little to no previous cases of such usages of licensed publicly available code. Therefore, any model trained on this dataset may be required to obey license terms that align with the software it was trained on such as GPL-3.0, which is why we purposefully put this dataset under the GPL-3.0 license. It is unclear the legal ramifications of using a language model trained on this dataset. ### v1.0 * The query was executed on *February 1, 2022, 12:15:59 AM EST* Acknowledgements ---------------- This project would not have been possible without compute generously provided by Google through the TPU Research Cloud. We would also like to thank Dr. Razvan Bunescu and The College of Computing and Informatics at UNC Charlotte for their generous contributions to this project, specifically in funding the BigQuery and Google Cloud Storage costs. We would also like to thank the codeparrot team at Hugging face for open sourcing their documentation on github-code which we used for the readme in this dataset. For another similar dataset to this please check github-code!

41d4a7c0dceb496a6a7441a80bb28e430cae670e

## Wow fishing bobber object detection dataset Hello, here you will find a link to a csv i scraped using the scraper found at the same link. it contains paragraphs of text found on a flat earth conspiracy website #TODO: turn it into an actualy huggingface dataset)

Cropinky/flatearther

2022-03-02T23:29:22+00:00

{}

2021-06-30T21:37:54+00:00

TAGS #region-us

## Wow fishing bobber object detection dataset Hello, here you will find a link to a csv i scraped using the scraper found at the same link. it contains paragraphs of text found on a flat earth conspiracy website #TODO: turn it into an actualy huggingface dataset)

63ce731b2049c2d02e1c47a3ef15400c7e08d2e5

## Rap lyrics dataset this is the repo containing the dataset we made for the hugging face community week, in order to download more songs you need to request and get(it's very simple and fast) your genius API key which ou put in the genius.py file<br/> #TODO: turn it into an actual huggingface dataset

Cropinky/rap_lyrics_english

2022-03-02T23:29:22+00:00

{}

2021-07-21T02:07:36+00:00

TAGS #region-us

## Rap lyrics dataset this is the repo containing the dataset we made for the hugging face community week, in order to download more songs you need to request and get(it's very simple and fast) your genius API key which ou put in the URL file<br/> #TODO: turn it into an actual huggingface dataset

49a1de0829240746a613c3e9a6d7a98e6527827f

## Wow fishing bobber object detection dataset Hello, in this zip you will find 160 annotated images each containing 1 fishing bobber from World of warcraft. I think this is an easy object detection datset, my yolov3 network was trained on it for 2000 iterations, it achieved a loss of 0.05. It was working flawlessly as it classified the newly generated images (also from wailing caverns zone) I haven't even tested it how it would work outside or on other fishing locations in the game, pozz.

Cropinky/wow_fishing_bobber

2022-03-02T23:29:22+00:00

{}

2021-06-30T21:14:04+00:00

TAGS #region-us

## Wow fishing bobber object detection dataset Hello, in this zip you will find 160 annotated images each containing 1 fishing bobber from World of warcraft. I think this is an easy object detection datset, my yolov3 network was trained on it for 2000 iterations, it achieved a loss of 0.05. It was working flawlessly as it classified the newly generated images (also from wailing caverns zone) I haven't even tested it how it would work outside or on other fishing locations in the game, pozz.

81c2d1f3fc945fbc417173ce18aadf781efecd53

词性标注训练集

Cyberfish/pos_tagger

2022-03-02T23:29:22+00:00

{}

2021-08-20T01:32:01+00:00

TAGS #region-us

词性标注训练集

577a560fa4ee1bf8d32480eefa9e14f416022016

文本纠错的相关数据

Cyberfish/text_error_correction

2022-03-02T23:29:22+00:00

{}

2021-08-19T12:07:16+00:00

TAGS #region-us

文本纠错的相关数据

7582bab94af11d09843132c0aa36571f148fa304

# 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.

CyranoB/polarity

2022-03-02T23:29:22+00:00

{"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"}

2022-10-25T07:54:09+00:00

TAGS #task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-English #license-apache-2.0 #arxiv-1509.01626 #region-us

# Dataset Card for Amazon Review Polarity ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: URL - Paper: URL - Leaderboard: - Point of Contact: Xiang Zhang ### 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: ### 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 (URL@URL). 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 #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information Apache License 2.0 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 for adding this dataset.

20b0e6081892e78179356fada741b7afa381443d

# Dataset Card for AngryTweets ## 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) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Paper:** https://aclanthology.org/2021.nodalida-main.53/ - **Direct Download**: https://danlp-downloads.alexandra.dk/datasets/game_tweets.zip ### Dataset Summary This dataset consists of anonymised Danish Twitter data that has been annotated for sentiment analysis through crowd-sourcing. All credits go to the authors of the following paper, who created the dataset: [Pauli, Amalie Brogaard, et al. "DaNLP: An open-source toolkit for Danish Natural Language Processing." Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa). 2021](https://aclanthology.org/2021.nodalida-main.53/) ### Supported Tasks and Leaderboards This dataset is suitable for sentiment analysis. ### Languages This dataset is in Danish. ## Dataset Structure ### Data Instances Every entry in the dataset has a tweet and an associated label. ### Data Fields An entry in the dataset consists of the following fields: - `text` (`str`): The tweet content. - `label` (`str`): The label of the `text`. Can be "positiv", "neutral" or "negativ" for positive, neutral and negative sentiment, respectively. ### Data Splits A `train` and `test` split is available, with the test split being 30% of the dataset, randomly sampled in a stratified fashion. There are 2,437 tweets in the training split and 1,047 in the test split. ## Additional Information ### Dataset Curators The collection and annotation of the dataset is solely due to the authors of [the original paper](https://aclanthology.org/2021.nodalida-main.53/): Amalie Brogaard Pauli, Maria Barrett, Ophélie Lacroix and Rasmus Hvingelby. The tweets have been anonymised by [@saattrupdan](https://github.com/saattrupdan). ### Licensing Information The dataset is released under the CC BY 4.0 license. ### Citation Information ``` @inproceedings{pauli2021danlp, title={DaNLP: An open-source toolkit for Danish Natural Language Processing}, author={Pauli, Amalie Brogaard and Barrett, Maria and Lacroix, Oph{\'e}lie and Hvingelby, Rasmus}, booktitle={Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)}, pages={460--466}, year={2021} } ``` ### Contributions Thanks to [@saattrupdan](https://github.com/saattrupdan) for adding this dataset to the Hugging Face Hub.

DDSC/angry-tweets

2022-03-02T23:29:22+00:00

{"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["da"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "pretty_name": "AngryTweets"}

2023-07-19T23:34:34+00:00

TAGS #task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Danish #license-cc-by-4.0 #region-us

# Dataset Card for AngryTweets ## Table of Contents - Table of Contents - Dataset Description - Dataset Summary - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Paper: URL - Direct Download: URL ### Dataset Summary This dataset consists of anonymised Danish Twitter data that has been annotated for sentiment analysis through crowd-sourcing. All credits go to the authors of the following paper, who created the dataset: Pauli, Amalie Brogaard, et al. "DaNLP: An open-source toolkit for Danish Natural Language Processing." Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa). 2021 ### Supported Tasks and Leaderboards This dataset is suitable for sentiment analysis. ### Languages This dataset is in Danish. ## Dataset Structure ### Data Instances Every entry in the dataset has a tweet and an associated label. ### Data Fields An entry in the dataset consists of the following fields: - 'text' ('str'): The tweet content. - 'label' ('str'): The label of the 'text'. Can be "positiv", "neutral" or "negativ" for positive, neutral and negative sentiment, respectively. ### Data Splits A 'train' and 'test' split is available, with the test split being 30% of the dataset, randomly sampled in a stratified fashion. There are 2,437 tweets in the training split and 1,047 in the test split. ## Additional Information ### Dataset Curators The collection and annotation of the dataset is solely due to the authors of the original paper: Amalie Brogaard Pauli, Maria Barrett, Ophélie Lacroix and Rasmus Hvingelby. The tweets have been anonymised by @saattrupdan. ### Licensing Information The dataset is released under the CC BY 4.0 license. ### Contributions Thanks to @saattrupdan for adding this dataset to the Hugging Face Hub.

59d12749a3c91a186063c7d729ec392fda94681c

# Dataset Card for DKHate ## 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) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://stromberg.ai/publication/offensivelanguageandhatespeechdetectionfordanish/](https://stromberg.ai/publication/offensivelanguageandhatespeechdetectionfordanish/) - **Repository:** [https://github.com/StrombergNLP/dkhate](https://github.com/StrombergNLP/dkhate) - **Paper:** [https://https://aclanthology.org/2020.lrec-1.430/](aclanthology.org/2020.lrec-1.430/), [https://arxiv.org/abs/1908.04531](https://arxiv.org/abs/1908.04531) - **Direct Download**: [https://figshare.com/articles/dataset/Danish_Hate_Speech_Abusive_Language_data/12220805](https://figshare.com/articles/dataset/Danish_Hate_Speech_Abusive_Language_data/12220805) - **Point of Contact:** [Leon Derczynski](mailto:leod@itu.dk) ### Dataset Summary This dataset consists of anonymised Danish Twitter data that has been annotated for hate speech. All credits go to the authors of the following paper, who created the dataset: [Offensive Language and Hate Speech Detection for Danish](https://aclanthology.org/2020.lrec-1.430) (Sigurbergsson & Derczynski, LREC 2020) ### Supported Tasks and Leaderboards This dataset is suitable for hate speech detection. * PwC leaderboard for Task A: [Hate Speech Detection on DKhate](https://paperswithcode.com/sota/hate-speech-detection-on-dkhate) ### Languages This dataset is in Danish. ## Dataset Structure ### Data Instances Every entry in the dataset has a tweet and an associated label. ### Data Fields An entry in the dataset consists of the following fields: - `text` (`str`): The tweet content. - `label` (`str`): The label of the `text`. Can be either "OFF" or "NOT", being offensive and not offensive, respectively. ### Data Splits A `train` and `test` split is available, which are identical to the original splits. There are 2,960 tweets in the training split and 329 in the test split. ## Additional Information ### Dataset Curators The curation of the dataset is solely due to the authors of [the original paper](https://aclanthology.org/2020.lrec-1.430/): Gudbjartur Ingi Sigurbergsson and Leon Derczynski. ### Licensing Information The dataset is released under the CC BY 4.0 license. ### Citation Information ``` @inproceedings{sigurbergsson2020offensive, title={Offensive Language and Hate Speech Detection for Danish}, author={Sigurbergsson, Gudbjartur Ingi and Derczynski, Leon}, booktitle={Proceedings of the 12th Language Resources and Evaluation Conference}, pages={3498--3508}, year={2020} } ``` ### Contributions Thanks to [@saattrupdan](https://github.com/saattrupdan) for adding this dataset to the Hugging Face Hub.

DDSC/dkhate

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["da"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["hate-speech-detection"], "paperswithcode_id": "dkhate", "pretty_name": "DKHate", "extra_gated_prompt": "Content warning: This dataset contains harmful text (abusive language, hate speech)."}

2023-05-17T05:19:43+00:00

TAGS #task_categories-text-classification #task_ids-hate-speech-detection #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Danish #license-cc-by-4.0 #arxiv-1908.04531 #region-us

# Dataset Card for DKHate ## Table of Contents - Table of Contents - Dataset Description - Dataset Summary - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: URL - Paper: https://URL URL - Direct Download: URL - Point of Contact: Leon Derczynski ### Dataset Summary This dataset consists of anonymised Danish Twitter data that has been annotated for hate speech. All credits go to the authors of the following paper, who created the dataset: Offensive Language and Hate Speech Detection for Danish (Sigurbergsson & Derczynski, LREC 2020) ### Supported Tasks and Leaderboards This dataset is suitable for hate speech detection. * PwC leaderboard for Task A: Hate Speech Detection on DKhate ### Languages This dataset is in Danish. ## Dataset Structure ### Data Instances Every entry in the dataset has a tweet and an associated label. ### Data Fields An entry in the dataset consists of the following fields: - 'text' ('str'): The tweet content. - 'label' ('str'): The label of the 'text'. Can be either "OFF" or "NOT", being offensive and not offensive, respectively. ### Data Splits A 'train' and 'test' split is available, which are identical to the original splits. There are 2,960 tweets in the training split and 329 in the test split. ## Additional Information ### Dataset Curators The curation of the dataset is solely due to the authors of the original paper: Gudbjartur Ingi Sigurbergsson and Leon Derczynski. ### Licensing Information The dataset is released under the CC BY 4.0 license. ### Contributions Thanks to @saattrupdan for adding this dataset to the Hugging Face Hub.

705be67c4566ea3ae65476b5a9fa44361d3a2287

# Dataset Card for DKHate ## 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) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Direct Download**: http://danlp-downloads.alexandra.dk/datasets/europarl.sentiment2.zip ### Dataset Summary This dataset consists of Danish data from the European Parliament that has been annotated for sentiment analysis by the [Alexandra Institute](https://github.com/alexandrainst) - all credits go to them. ### Supported Tasks and Leaderboards This dataset is suitable for sentiment analysis. ### Languages This dataset is in Danish. ## Dataset Structure ### Data Instances Every entry in the dataset has a document and an associated label. ### Data Fields An entry in the dataset consists of the following fields: - `text` (`str`): The text content. - `label` (`str`): The label of the `text`. Can be "positiv", "neutral" or "negativ" for positive, neutral and negative sentiment, respectively. ### Data Splits A `train` and `test` split is available, with the test split being 30% of the dataset, randomly sampled in a stratified fashion. There are 669 documents in the training split and 288 in the test split. ## Additional Information ### Dataset Curators The collection and annotation of the dataset is solely due to the [Alexandra Institute](https://github.com/alexandrainst). ### Licensing Information The dataset is released under the CC BY 4.0 license. ### Citation Information ``` @misc{europarl, title={EuroParl}, author={Alexandra Institute}, year={2020}, note={\url{https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#europarl-sentiment2}} } ``` ### Contributions Thanks to [@saattrupdan](https://github.com/saattrupdan) for adding this dataset to the Hugging Face Hub.

DDSC/europarl

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["da"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["n<1K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "pretty_name": "TwitterSent"}

2022-07-01T14:42:03+00:00

TAGS #task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-Danish #license-cc-by-4.0 #region-us

# Dataset Card for DKHate ## Table of Contents - Table of Contents - Dataset Description - Dataset Summary - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Direct Download: URL ### Dataset Summary This dataset consists of Danish data from the European Parliament that has been annotated for sentiment analysis by the Alexandra Institute - all credits go to them. ### Supported Tasks and Leaderboards This dataset is suitable for sentiment analysis. ### Languages This dataset is in Danish. ## Dataset Structure ### Data Instances Every entry in the dataset has a document and an associated label. ### Data Fields An entry in the dataset consists of the following fields: - 'text' ('str'): The text content. - 'label' ('str'): The label of the 'text'. Can be "positiv", "neutral" or "negativ" for positive, neutral and negative sentiment, respectively. ### Data Splits A 'train' and 'test' split is available, with the test split being 30% of the dataset, randomly sampled in a stratified fashion. There are 669 documents in the training split and 288 in the test split. ## Additional Information ### Dataset Curators The collection and annotation of the dataset is solely due to the Alexandra Institute. ### Licensing Information The dataset is released under the CC BY 4.0 license. ### Contributions Thanks to @saattrupdan for adding this dataset to the Hugging Face Hub.

de7ba3406ee55ea2cc52a0a41408fa6aede6d3c6

# Dataset Card for LCC ## 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) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository**: https://github.com/fnielsen/lcc-sentiment - **Direct Download part 1**: https://raw.githubusercontent.com/fnielsen/lcc-sentiment/master/dan_mixed_2014_10K-sentences.csv - **Direct Download part 2**: https://raw.githubusercontent.com/fnielsen/lcc-sentiment/master/dan_newscrawl_2011_10K-sentences.csv ### Dataset Summary This dataset consists of Danish data from [the Leipzig Collection](https://www.aclweb.org/anthology/L06-1396/) that has been annotated for sentiment analysis by Finn Årup Nielsen. ### Supported Tasks and Leaderboards This dataset is suitable for sentiment analysis. ### Languages This dataset is in Danish. ## Dataset Structure ### Data Instances Every entry in the dataset has a document and an associated label. ### Data Fields An entry in the dataset consists of the following fields: - `text` (`str`): The text content. - `label` (`str`): The label of the `text`. Can be "positiv", "neutral" or "negativ" for positive, neutral and negative sentiment, respectively. ### Data Splits A `train` and `test` split is available, with the test split being 30% of the dataset, randomly sampled in a stratified fashion. There are 349 documents in the training split and 150 in the test split. ## Additional Information ### Dataset Curators The collection and annotation of the dataset is solely due to the Finn Årup Nielsen. It was originally annotated as a score between -5 and +5, but the labels in this version have been converted to a negative, neutral and positive label. ### Licensing Information The dataset is released under the CC BY 4.0 license. ### Citation Information ``` @misc{lcc, title={LCC}, author={Finn Årup Nielsen}, year={2016}, note={\url{https://github.com/fnielsen/lcc-sentiment}} } ``` ### Contributions Thanks to [@saattrupdan](https://github.com/saattrupdan) for adding this dataset to the Hugging Face Hub.

DDSC/lcc

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["da"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["n<1K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "pretty_name": "TwitterSent"}

2023-07-20T18:43:29+00:00

TAGS #task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-Danish #license-cc-by-4.0 #region-us

# Dataset Card for LCC ## Table of Contents - Table of Contents - Dataset Description - Dataset Summary - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Repository: URL - Direct Download part 1: URL - Direct Download part 2: URL ### Dataset Summary This dataset consists of Danish data from the Leipzig Collection that has been annotated for sentiment analysis by Finn Årup Nielsen. ### Supported Tasks and Leaderboards This dataset is suitable for sentiment analysis. ### Languages This dataset is in Danish. ## Dataset Structure ### Data Instances Every entry in the dataset has a document and an associated label. ### Data Fields An entry in the dataset consists of the following fields: - 'text' ('str'): The text content. - 'label' ('str'): The label of the 'text'. Can be "positiv", "neutral" or "negativ" for positive, neutral and negative sentiment, respectively. ### Data Splits A 'train' and 'test' split is available, with the test split being 30% of the dataset, randomly sampled in a stratified fashion. There are 349 documents in the training split and 150 in the test split. ## Additional Information ### Dataset Curators The collection and annotation of the dataset is solely due to the Finn Årup Nielsen. It was originally annotated as a score between -5 and +5, but the labels in this version have been converted to a negative, neutral and positive label. ### Licensing Information The dataset is released under the CC BY 4.0 license. ### Contributions Thanks to @saattrupdan for adding this dataset to the Hugging Face Hub.

bc1302039dd6176aa5ce42342a25549c36587dc8

# Dataset Card for SQuAD-da ## 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) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Contributions](#contributions) ### Dataset Summary This dataset consists of 1,908,887 Danish posts from Reddit. These are from [this Reddit dump](https://files.pushshift.io/reddit/) and have been filtered using [this script](https://github.com/NBAiLab/notram/blob/master/corpus_generation_scripts/lang_detect_reddit.py), which uses FastText to detect the Danish posts. ### Supported Tasks and Leaderboards This dataset is suitable for language modelling. ### Languages This dataset is in Danish. ## Dataset Structure ### Data Instances Every entry in the dataset contains short Reddit comments in Danish, along with a unique ID. ### Data Fields An entry in the dataset consists of the following fields: - `id` (`str`): A unique identifier. - `text` (`str`): A short Reddit comment. ## Additional Information ### Licensing Information The dataset is released under the MIT license. ### Contributions Thanks to [@saattrupdan](https://github.com/saattrupdan) for adding this dataset to the Hugging Face Hub.

DDSC/reddit-da

2022-03-02T23:29:22+00:00

{"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["da"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["text-generation"], "task_ids": ["language-modeling"], "pretty_name": "Reddit-da"}

2022-10-27T10:00:42+00:00

TAGS #task_categories-text-generation #task_ids-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-Danish #license-mit #region-us

# Dataset Card for SQuAD-da ## Table of Contents - Table of Contents - Dataset Description - Dataset Summary - Languages - Dataset Structure - Data Instances - Data Fields - Additional Information - Dataset Curators - Licensing Information - Contributions ### Dataset Summary This dataset consists of 1,908,887 Danish posts from Reddit. These are from this Reddit dump and have been filtered using this script, which uses FastText to detect the Danish posts. ### Supported Tasks and Leaderboards This dataset is suitable for language modelling. ### Languages This dataset is in Danish. ## Dataset Structure ### Data Instances Every entry in the dataset contains short Reddit comments in Danish, along with a unique ID. ### Data Fields An entry in the dataset consists of the following fields: - 'id' ('str'): A unique identifier. - 'text' ('str'): A short Reddit comment. ## Additional Information ### Licensing Information The dataset is released under the MIT license. ### Contributions Thanks to @saattrupdan for adding this dataset to the Hugging Face Hub.

589ba98416d19168b59c26f992b4337815e55964

# Dataset Card for DKHate ## 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) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Direct Download**: https://danlp.alexandra.dk/304bd159d5de/datasets/twitter.sentiment.zip ### Dataset Summary This dataset consists of anonymised Danish Twitter data that has been annotated for sentiment analysis by the [Alexandra Institute](https://github.com/alexandrainst) - all credits go to them. ### Supported Tasks and Leaderboards This dataset is suitable for sentiment analysis. ### Languages This dataset is in Danish. ## Dataset Structure ### Data Instances Every entry in the dataset has a tweet and an associated label. ### Data Fields An entry in the dataset consists of the following fields: - `text` (`str`): The tweet content. - `label` (`str`): The label of the `text`. Can be "positiv", "neutral" or "negativ" for positive, neutral and negative sentiment, respectively. ### Data Splits A `train` and `test` split is available, being identical to the original splits. There are 1,007 tweets in the training split and 431 in the test split. ## Additional Information ### Dataset Curators The collection and annotation of the dataset is solely due to the [Alexandra Institute](https://github.com/alexandrainst). The tweets have been anonymised by [@saattrupdan](https://github.com/saattrupdan). ### Licensing Information The dataset is released under the CC BY 4.0 license. ### Citation Information ``` @misc{twittersent, title={TwitterSent}, author={Alexandra Institute}, year={2020}, note={\url{https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#twitsent}} } ``` ### Contributions Thanks to [@saattrupdan](https://github.com/saattrupdan) for adding this dataset to the Hugging Face Hub.

DDSC/twitter-sent

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["da"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "pretty_name": "TwitterSent"}

2022-07-01T14:44:26+00:00

TAGS #task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Danish #license-cc-by-4.0 #region-us

# Dataset Card for DKHate ## Table of Contents - Table of Contents - Dataset Description - Dataset Summary - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Direct Download: URL ### Dataset Summary This dataset consists of anonymised Danish Twitter data that has been annotated for sentiment analysis by the Alexandra Institute - all credits go to them. ### Supported Tasks and Leaderboards This dataset is suitable for sentiment analysis. ### Languages This dataset is in Danish. ## Dataset Structure ### Data Instances Every entry in the dataset has a tweet and an associated label. ### Data Fields An entry in the dataset consists of the following fields: - 'text' ('str'): The tweet content. - 'label' ('str'): The label of the 'text'. Can be "positiv", "neutral" or "negativ" for positive, neutral and negative sentiment, respectively. ### Data Splits A 'train' and 'test' split is available, being identical to the original splits. There are 1,007 tweets in the training split and 431 in the test split. ## Additional Information ### Dataset Curators The collection and annotation of the dataset is solely due to the Alexandra Institute. The tweets have been anonymised by @saattrupdan. ### Licensing Information The dataset is released under the CC BY 4.0 license. ### Contributions Thanks to @saattrupdan for adding this dataset to the Hugging Face Hub.

4ed6a771c6c5b38cb2c4e84b11fc04646d0818fb

# Dataset Card for scientific-challenges-and-directions ## 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) - [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: [repo](https://github.com/Dan-La/scientific-challenges-and-directions)** - **Paper: [A Search Engine for Discovery of Scientific Challenges and Directions](https://arxiv.org/abs/2108.13751)** - **Point of Contact: lahav@mail.tau.ac.il,tomh@allenai.org** ### Dataset Summary The scientific challenges and directions dataset is a collection of 2894 sentences and their surrounding contexts, from 1786 full-text papers in the [CORD-19](https://arxiv.org/abs/2004.10706) corpus, labeled for classification of _challenges_ and _directions_ by expert annotators with biomedical and bioNLP backgrounds. At a high level, our labels are defined as follows: * **Challenge**: A sentence mentioning a problem, difficulty, flaw, limitation, failure, lack of clarity, or knowledge gap. * **Research direction**: A sentence mentioning suggestions or needs for further research, hypotheses, speculations, indications or hints that an issue is worthy of exploration. The dataset was developed to help scientists and medical professionals discover challenges and potential directions across scientific literature. ### Languages The language in the dataset is English as written by authors of the scientific papers in the CORD-19 corpus. ## Dataset Structure ### Data Instances For each instance, there is a unique id, a string for the text sentence, a string for the previous sentence, a string for the next sentence, and a list for the challenge and direction labels. ``` {'id': 'PMC7152165_152', 'label': [0.0, 0.0], 'next_sent': 'The railways brought a new technology and vast engineering and architectural structures into Britain’s rural and urban landscapes.', 'prev_sent': 'In Britain, improvements in coaching technologies and roads helped to increase stage coach speeds in the late eighteenth and early nineteenth centuries, while the railway construction boom of the 1830s and 1840s led to a massive reduction in journey times, and the emergence of distinctly new experiences and geographies.', 'text': 'Britain’s railway companies were among the nation’s largest employers in the nineteenth century, and they facilitated the mobility of passengers and important commodities.'} ``` ### Data Fields * id: A string as a unique id for the instance. The id is composed of the unique PMC id of the paper, an underscore, and the index of the sentence within the paper. * next_sent_: A string of a sentence that is following the _text_ of the instance. If the text is the first in its paragraph the string is saved as '|'. * prev_sent_: A string of a sentence that is preceding the _text_ of the instance. If the text is the first in its paragraph the string is saved as '|'. * text: A string of the sentence we seek to classify. * label: A list of 2 values - the first is the label for _challenge_ and the last of _direction_. Each value may be either 0, indicating that the _text_ is **not** _challenge_ or _direction_, or 1, indicating that the the _text_ is _challenge_ or _direction_. Each instance can be a _challenge_, a _direction_, both, or neither. ### Data Splits The scientific-challenges-and-directions dataset has 3 splits: _train_, _dev_, and _test_. Each instances shows up in only one split. The splits are stratified with no overlap in papers. | Labels | Train | Dev | Test | All | |:----------------------------:|:------:|:-----:|:----:|:----:| | Not Challenge, Not Direction | 602 | 146 | 745 | 1493 | | Not Challenge, Direction | 106 | 25 | 122 | 253 | | Challenge, Not Direction | 288 | 73 | 382 | 743 | | Challenge, Direction | 155 | 40 | 210 | 405 | ## Dataset Creation ### Curation Rationale The resource was developed to help scientists and medical professionals discover challenges and potential directions across scientific literature, focusing on a broad corpus pertaining to the COVID-19 pandemic and related historical research. ### Source Data #### Initial Data Collection and Normalization See section 3.1 in our [paper](https://arxiv.org/abs/2108.13751). #### Who are the source language producers? The authors of the subset of full-text papers in the [CORD-19 dataset](https://arxiv.org/abs/2004.10706), which at the time of creating our dataset included roughly 180K documents. ### Annotations #### Annotation process See section 3.1 in our [paper](https://arxiv.org/abs/2108.13751). #### Who are the annotators? Four expert annotators with biomedical and bioNLP backgrounds. For more details see section 3.1 in our [paper](https://arxiv.org/abs/2108.13751). ### Personal and Sensitive Information The dataset does not contain any personal information about the authors or annotators. ## Considerations for Using the Data ### Social Impact of Dataset As mentioned, the dataset was developed to help scientists and medical professionals discover challenges and potential directions across scientific literature, focusing on a broad corpus pertaining to the COVID-19 pandemic and related historical research. Studies were conducted to evaluate the utility of the dataset for researchers and medical professionals, in which a prototype based on the dataset was found to outperform other biomedical search tools. For more details see section 4 in our [paper](https://arxiv.org/abs/2108.13751). This dataset was also developed for evaluating representational systems for scientific text classification and can be used as such. ### Discussion of Biases The source of the dataset is the full-text papers in the [CORD-19 dataset](https://arxiv.org/abs/2004.10706), so biases in CORD-19 may be replicated to our dataset. ### Other Known Limitations N/A ## Additional Information ### Dataset Curators The dataset was developed by Dan Lahav, Jon Saad Falcon, Bailey Kuehl, Sophie Johnson, Sravanthi Parasa, Noam Shomron, Duen Horng Chau, Diyi Yang, Eric Horvitz, Daniel S. Weld and Tom Hope as part of _Tel Aviv University_, the _Allen Institute for AI_, _University of Washington_, _Georgia Institute of Technology_, _Microsoft_ and _Swedish Medical Group_. It was supported by the Edmond J. Safra Center for Bioinformatics at Tel-Aviv University, ONR grant N00014-18-1-2193, NSF RAPID grant 2040196, the WR-F/Cable Professorship, and AI2. ### Licensing Information [More Information Needed] ### Citation Information If using our dataset and models, please cite: ``` @misc{lahav2021search, title={A Search Engine for Discovery of Scientific Challenges and Directions}, author={Dan Lahav and Jon Saad Falcon and Bailey Kuehl and Sophie Johnson and Sravanthi Parasa and Noam Shomron and Duen Horng Chau and Diyi Yang and Eric Horvitz and Daniel S. Weld and Tom Hope}, year={2021}, eprint={2108.13751}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@Dan-La](https://github.com/Dan-La) and [@tomhoper](https://github.com/tomhoper) for adding this dataset.

DanL/scientific-challenges-and-directions-dataset

2022-03-02T23:29:22+00:00

{"annotations_creators": ["expert-generated"], "language_creators": [], "language": ["en"], "license": [], "multilinguality": ["monolingual"], "source_datasets": ["CORD-19"], "task_categories": ["text-classification"], "task_ids": ["multi-label-classification"], "pretty_name": "DanL/scientific-challenges-and-directions-dataset"}

2022-10-25T07:56:00+00:00

TAGS #task_categories-text-classification #task_ids-multi-label-classification #annotations_creators-expert-generated #multilinguality-monolingual #source_datasets-CORD-19 #language-English #arxiv-2108.13751 #arxiv-2004.10706 #region-us

Dataset Card for scientific-challenges-and-directions ===================================================== Table of Contents ----------------- * Table of Contents * Dataset Description + Dataset Summary + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Repository: repo * Paper: A Search Engine for Discovery of Scientific Challenges and Directions * Point of Contact: lahav@URL,tomh@URL ### Dataset Summary The scientific challenges and directions dataset is a collection of 2894 sentences and their surrounding contexts, from 1786 full-text papers in the CORD-19 corpus, labeled for classification of *challenges* and *directions* by expert annotators with biomedical and bioNLP backgrounds. At a high level, our labels are defined as follows: * Challenge: A sentence mentioning a problem, difficulty, flaw, limitation, failure, lack of clarity, or knowledge gap. * Research direction: A sentence mentioning suggestions or needs for further research, hypotheses, speculations, indications or hints that an issue is worthy of exploration. The dataset was developed to help scientists and medical professionals discover challenges and potential directions across scientific literature. ### Languages The language in the dataset is English as written by authors of the scientific papers in the CORD-19 corpus. Dataset Structure ----------------- ### Data Instances For each instance, there is a unique id, a string for the text sentence, a string for the previous sentence, a string for the next sentence, and a list for the challenge and direction labels. ### Data Fields * id: A string as a unique id for the instance. The id is composed of the unique PMC id of the paper, an underscore, and the index of the sentence within the paper. * next\_sent\_: A string of a sentence that is following the *text* of the instance. If the text is the first in its paragraph the string is saved as '|'. * prev\_sent\_: A string of a sentence that is preceding the *text* of the instance. If the text is the first in its paragraph the string is saved as '|'. * text: A string of the sentence we seek to classify. * label: A list of 2 values - the first is the label for *challenge* and the last of *direction*. Each value may be either 0, indicating that the *text* is not *challenge* or *direction*, or 1, indicating that the the *text* is *challenge* or *direction*. Each instance can be a *challenge*, a *direction*, both, or neither. ### Data Splits The scientific-challenges-and-directions dataset has 3 splits: *train*, *dev*, and *test*. Each instances shows up in only one split. The splits are stratified with no overlap in papers. Dataset Creation ---------------- ### Curation Rationale The resource was developed to help scientists and medical professionals discover challenges and potential directions across scientific literature, focusing on a broad corpus pertaining to the COVID-19 pandemic and related historical research. ### Source Data #### Initial Data Collection and Normalization See section 3.1 in our paper. #### Who are the source language producers? The authors of the subset of full-text papers in the CORD-19 dataset, which at the time of creating our dataset included roughly 180K documents. ### Annotations #### Annotation process See section 3.1 in our paper. #### Who are the annotators? Four expert annotators with biomedical and bioNLP backgrounds. For more details see section 3.1 in our paper. ### Personal and Sensitive Information The dataset does not contain any personal information about the authors or annotators. Considerations for Using the Data --------------------------------- ### Social Impact of Dataset As mentioned, the dataset was developed to help scientists and medical professionals discover challenges and potential directions across scientific literature, focusing on a broad corpus pertaining to the COVID-19 pandemic and related historical research. Studies were conducted to evaluate the utility of the dataset for researchers and medical professionals, in which a prototype based on the dataset was found to outperform other biomedical search tools. For more details see section 4 in our paper. This dataset was also developed for evaluating representational systems for scientific text classification and can be used as such. ### Discussion of Biases The source of the dataset is the full-text papers in the CORD-19 dataset, so biases in CORD-19 may be replicated to our dataset. ### Other Known Limitations N/A Additional Information ---------------------- ### Dataset Curators The dataset was developed by Dan Lahav, Jon Saad Falcon, Bailey Kuehl, Sophie Johnson, Sravanthi Parasa, Noam Shomron, Duen Horng Chau, Diyi Yang, Eric Horvitz, Daniel S. Weld and Tom Hope as part of *Tel Aviv University*, the *Allen Institute for AI*, *University of Washington*, *Georgia Institute of Technology*, *Microsoft* and *Swedish Medical Group*. It was supported by the Edmond J. Safra Center for Bioinformatics at Tel-Aviv University, ONR grant N00014-18-1-2193, NSF RAPID grant 2040196, the WR-F/Cable Professorship, and AI2. ### Licensing Information If using our dataset and models, please cite: ### Contributions Thanks to @Dan-La and @tomhoper for adding this dataset.

107a08bf312451a432f6cd75ae38688b67f85646

# Dataset Card for [Dataset Name] ## 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) - [Additional Information](#additional-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary All literary production of great poet Dante Alighieri. ### Supported Tasks and Leaderboards Fill Mask task. ### Languages (Ancient) Italian. ### Contributions Thanks to [@danielekp](https://github.com/danielekp) for adding this dataset.

Daniele/dante-corpus

2022-03-02T23:29:22+00:00

{"YAML tags": [{"copy-paste the tags obtained with the online tagging app": "https://huggingface.co/spaces/huggingface/datasets-tagging"}]}

2021-11-12T11:44:16+00:00

TAGS #region-us

# Dataset Card for [Dataset Name] ## Table of Contents - Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Additional Information - Contributions ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary All literary production of great poet Dante Alighieri. ### Supported Tasks and Leaderboards Fill Mask task. ### Languages (Ancient) Italian. ### Contributions Thanks to @danielekp for adding this dataset.

4d967ec76b798f045762874994de4113e82a763a

# Dataset Card for accented-english ## 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) ## Dataset Description - **Homepage:** https://nexdata.ai/?source=Huggingface - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The dataset contains 20,000 hours of accented English speech data. It's collected from local English speakers in more than 20 countries, such as USA, China, UK, Germany, Japan, India, France, Spain, Russia, Latin America, covering a variety of pronunciation habits and characteristics, accent severity, and the distribution of speakers. The format is 16kHz, 16bit, uncompressed wav, mono channel. The sentence accuracy is over 95%. For more details, please refer to the link: https://nexdata.ai/speechRecognition?source=Huggingface ### Supported Tasks and Leaderboards automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR). ### Languages English ## 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 Commercial License

Nexdata/accented_english

2022-03-02T23:29:22+00:00

{"language": ["en"], "task_categories": ["automatic-speech-recognition"], "YAML tags": [{"copy-paste the tags obtained with the tagging app": "https://github.com/huggingface/datasets-tagging"}]}

2023-11-22T09:51:18+00:00

TAGS #task_categories-automatic-speech-recognition #language-English #region-us

# Dataset Card for accented-english ## Table of Contents - Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information ## Dataset Description - Homepage: URL - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary The dataset contains 20,000 hours of accented English speech data. It's collected from local English speakers in more than 20 countries, such as USA, China, UK, Germany, Japan, India, France, Spain, Russia, Latin America, covering a variety of pronunciation habits and characteristics, accent severity, and the distribution of speakers. The format is 16kHz, 16bit, uncompressed wav, mono channel. The sentence accuracy is over 95%. For more details, please refer to the link: URL ### Supported Tasks and Leaderboards automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR). ### Languages English ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information Commercial License

c1b9ce6857454d2bacc1257072b2a36e75a811e5

# Dataset Card for accented_mandarin ## 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://nexdata.ai/?source=Huggingface - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The dataset contains 2,000 hours of Mandarin Chinese speech data. The data is collected from local speakers in 26 provinces like Henan, Shanxi, Sichuan, Hunan, Fujian, etc.The content covers generic catagory,human machine interaction, smart home command and control, in-car,numbers etc. The format is 16kHz, 16bit, uncompressed wav, mono channel. The sentence accuracy is over 97%. For more details, please refer to the link: https://nexdata.ai/speechRecognition?source=Huggingface ### Supported Tasks and Leaderboards automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR). ### Languages Accented Mandarin ## 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 Commercial License ### Citation Information [More Information Needed] ### Contributions

Nexdata/accented_mandarin

2022-03-02T23:29:22+00:00

{"YAML tags": [{"copy-paste the tags obtained with the tagging app": "https://github.com/huggingface/datasets-tagging"}]}

2023-11-22T09:49:28+00:00

TAGS #region-us

# Dataset Card for accented_mandarin ## Table of Contents - Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary The dataset contains 2,000 hours of Mandarin Chinese speech data. The data is collected from local speakers in 26 provinces like Henan, Shanxi, Sichuan, Hunan, Fujian, etc.The content covers generic catagory,human machine interaction, smart home command and control, in-car,numbers etc. The format is 16kHz, 16bit, uncompressed wav, mono channel. The sentence accuracy is over 97%. For more details, please refer to the link: URL ### Supported Tasks and Leaderboards automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR). ### Languages Accented Mandarin ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information Commercial License ### Contributions

bcecbc91a80fae050e938d0ddec61136bb9deb18

# Dataset Card for chinese_dialect ## 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://nexdata.ai/?source=Huggingface - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The dataset contains 25,000 hours of Chinese Dialect speech data. It's collected from local dialect speakers in multiple dialect regions, covering Hokkien, Cantonese, Sichuan Dialect, Henan Dialects,Northeastern Dialect, Shanghai Dialect,Uyghur and Tibetan etc. The format is 16kHz, 16bit, uncompressed wav, mono channel. The sentence accuracy is over 95%. For more details, please refer to the link: https://nexdata.ai/speechRecognition?source=Huggingface ### Supported Tasks and Leaderboards automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR). ### Languages Chinese Dialect ## 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 Commercial License ### Citation Information [More Information Needed] ### Contributions

Nexdata/chinese_dialect

2022-03-02T23:29:22+00:00

{"language": ["zh"], "task_categories": ["automatic-speech-recognition"], "YAML tags": [{"copy-paste the tags obtained with the tagging app": "https://github.com/huggingface/datasets-tagging"}]}

2023-11-22T09:49:16+00:00

TAGS #task_categories-automatic-speech-recognition #language-Chinese #region-us

# Dataset Card for chinese_dialect ## Table of Contents - Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary The dataset contains 25,000 hours of Chinese Dialect speech data. It's collected from local dialect speakers in multiple dialect regions, covering Hokkien, Cantonese, Sichuan Dialect, Henan Dialects,Northeastern Dialect, Shanghai Dialect,Uyghur and Tibetan etc. The format is 16kHz, 16bit, uncompressed wav, mono channel. The sentence accuracy is over 95%. For more details, please refer to the link: URL ### Supported Tasks and Leaderboards automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR). ### Languages Chinese Dialect ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information Commercial License ### Contributions

1fb2242a826377d77064ecabf22378b44ef71d35

# Dataset Card for mandarin_chinese ## 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://nexdata.ai/?source=Huggingface - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The dataset contains 15,000 hours of Mandarin Chinese speech data. It's collected from local Mandarin speakers in 33 provinces of China, covering mutiple scenes and enviroments. The format is 16kHz, 16bit, uncompressed wav, mono channel. The sentence accuracy is over 97%. For more details, please refer to the link: https://nexdata.ai/speechRecognition?source=Huggingface ### Supported Tasks and Leaderboards automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR). ### Languages Mandarin ## 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 Commercial License ### Citation Information [More Information Needed] ### Contributions

Nexdata/mandarin_chinese

2022-03-02T23:29:22+00:00

{"YAML tags": [{"copy-paste the tags obtained with the tagging app": "https://github.com/huggingface/datasets-tagging"}]}

2023-11-22T09:52:56+00:00

TAGS #region-us

# Dataset Card for mandarin_chinese ## Table of Contents - Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary The dataset contains 15,000 hours of Mandarin Chinese speech data. It's collected from local Mandarin speakers in 33 provinces of China, covering mutiple scenes and enviroments. The format is 16kHz, 16bit, uncompressed wav, mono channel. The sentence accuracy is over 97%. For more details, please refer to the link: URL ### Supported Tasks and Leaderboards automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR). ### Languages Mandarin ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information Commercial License ### Contributions

223e6b2a3c72c5bd59b4aa23c3be6b9c47ee1df5

# Dataset Card for mixed_speech_chinese_english ## 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://nexdata.ai/?source=Huggingface - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The dataset contains 2,000 hours of mixed speech with Chinese and English. The data is collected from speakers in 26 provinces like Henan, Shanxi, Sichuan, Hunan, Fujian, etc.The content covers generic scene and multiple human machine interation scenes, such as music, entertainment, travel, daily life. The data covers more than 30,000 English words. The sentence accuracy is over 97%. For more details, please refer to the link: https://nexdata.ai/speechRecognition?source=Huggingface ### Supported Tasks and Leaderboards automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR). ### Languages Chinese, English ## 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 Commercial License ### Citation Information [More Information Needed] ### Contributions

Nexdata/mixed_speech_chinese_english

2022-03-02T23:29:22+00:00

{"language": ["zh", "en"], "task_categories": ["automatic-speech-recognition"], "YAML tags": [{"copy-paste the tags obtained with the tagging app": "https://github.com/huggingface/datasets-tagging"}]}

2023-11-22T09:52:13+00:00

TAGS #task_categories-automatic-speech-recognition #language-Chinese #language-English #region-us

# Dataset Card for mixed_speech_chinese_english ## Table of Contents - Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary The dataset contains 2,000 hours of mixed speech with Chinese and English. The data is collected from speakers in 26 provinces like Henan, Shanxi, Sichuan, Hunan, Fujian, etc.The content covers generic scene and multiple human machine interation scenes, such as music, entertainment, travel, daily life. The data covers more than 30,000 English words. The sentence accuracy is over 97%. For more details, please refer to the link: URL ### Supported Tasks and Leaderboards automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR). ### Languages Chinese, English ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information Commercial License ### Contributions

a9b131f8303be3b20576c4a611c8102ba3f14ff5

# Dataset Card for multi_language ## 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://nexdata.ai/?source=Huggingface - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The dataset contains 25,000 hours of multi-language reading speech data. It's recorded by native speakers, covering English, French, German, Russian, Spanish, Portuguese, Italian, Japanese, Korean, Hindi, Vietnamese, Tagalog, Thai etc.The recording is rich in content, covering multiple categories such as economy, entertainment, news, oral language, numbers, and letters. The format is 16kHz, 16bit, uncompressed wav, mono channel. The sentence accuracy is over 95%. For more details, please refer to the link: https://nexdata.ai/speechRecognition?source=Huggingface ### Supported Tasks and Leaderboards automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR). ### Languages English, French, German, Russian, Spanish, Portuguese, Italian, Japanese, Korean, Hindi, Vietnamese, Tagalog, Thai etc. ## 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 Commercial License ### Citation Information [More Information Needed] ### Contributions

Nexdata/multi_language

2022-03-02T23:29:22+00:00

{"language": ["en", "de", "fr", "it", "es", "ko", "ja"], "task_categories": ["automatic-speech-recognition"], "YAML tags": [{"copy-paste the tags obtained with the tagging app": "https://github.com/huggingface/datasets-tagging"}]}

2023-11-22T09:43:08+00:00

TAGS #task_categories-automatic-speech-recognition #language-English #language-German #language-French #language-Italian #language-Spanish #language-Korean #language-Japanese #region-us

# Dataset Card for multi_language ## Table of Contents - Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary The dataset contains 25,000 hours of multi-language reading speech data. It's recorded by native speakers, covering English, French, German, Russian, Spanish, Portuguese, Italian, Japanese, Korean, Hindi, Vietnamese, Tagalog, Thai etc.The recording is rich in content, covering multiple categories such as economy, entertainment, news, oral language, numbers, and letters. The format is 16kHz, 16bit, uncompressed wav, mono channel. The sentence accuracy is over 95%. For more details, please refer to the link: URL ### Supported Tasks and Leaderboards automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR). ### Languages English, French, German, Russian, Spanish, Portuguese, Italian, Japanese, Korean, Hindi, Vietnamese, Tagalog, Thai etc. ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information Commercial License ### Contributions

f80fbb935cbe98eaf7a611d069752b7bcc098926

# Dataset Card for multi_language_conversation ## 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://nexdata.ai/?source=Huggingface - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The dataset contains 12,000 hours of multi-language conversation speech data. It's recorded by native speakers, covering English, French, German, Russian, Spanish, Japanese, Korean, Hindi, Vietnamese etc. The speakers start the conversation around a familar topic, to ensure the smoothness and nature of the conversation. The format is 16kHz, 16bit, uncompressed wav, mono channel. The sentence accuracy is over 95%. For more details, please refer to the link: https://nexdata.ai/speechRecognition?source=Huggingface ### Supported Tasks and Leaderboards automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR). ### Languages English, French, German, Russian, Spanish, Japanese, Korean, Hindi, Vietnamese etc. ## 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 Commercial License ### Citation Information [More Information Needed] ### Contributions

Nexdata/multi_language_conversation

2022-03-02T23:29:22+00:00

{"task_categories": ["conversational"], "YAML tags": [{"copy-paste the tags obtained with the tagging app": "https://github.com/huggingface/datasets-tagging"}]}

2023-11-22T09:49:46+00:00

TAGS #task_categories-conversational #region-us

# Dataset Card for multi_language_conversation ## Table of Contents - Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary The dataset contains 12,000 hours of multi-language conversation speech data. It's recorded by native speakers, covering English, French, German, Russian, Spanish, Japanese, Korean, Hindi, Vietnamese etc. The speakers start the conversation around a familar topic, to ensure the smoothness and nature of the conversation. The format is 16kHz, 16bit, uncompressed wav, mono channel. The sentence accuracy is over 95%. For more details, please refer to the link: URL ### Supported Tasks and Leaderboards automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR). ### Languages English, French, German, Russian, Spanish, Japanese, Korean, Hindi, Vietnamese etc. ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information Commercial License ### Contributions

89349b4f6b2b3dc5e3b4da9a505c969421da3e6c

# Pokemon Dataset This dataset contains a text representation of more that 10k pokemon sprites from different pokemon videogames (red, yellow, gold, ruby,...). The original images are from 40 to 96 pixel of size and every pixel is represented with an ASCII character depending to its color. # Supported Tasks * Text Generation # Languages * ASCII colo representation # Data Fields ``` {'pokemon': pokemon sprite in ASCII representation 'game': videogame in witch the sprite appears 'size': pixel size 'number': number of the pokemon} ``` # License * All the creative right are property of Nintendo # Preview ``` 00 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 01 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 02 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 03 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 04 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 05 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 06 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 07 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; ; ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 08 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; ; ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 09 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; P ; ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 10 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; P P ; ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; ; ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 11 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; P P P ; ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; ; ; P P ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 12 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; P P P F ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; ; P P P P ; ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 13 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; P P J J ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ F F J P P P P ; ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 14 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; J J J F ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ F J J J P P P ; ; ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 15 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ F J J J F ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ F F J J J J F P ; ; ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 16 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ F J J J F ; ~ ~ ~ ~ ~ ~ ~ ~ ~ A J J J J J J J ; ; ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 17 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ F J F F ; F F F F F F F A A J J J J J J J F ; ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 18 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ F F F F F Z Z Z Z Z J J F J J J J J J F F ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 19 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ A F Z Z Z Z Z Z Z Z J J J J J J F F ; ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ F F ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 20 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ F J J Z Z Z Z Z Z Z Z J J J J J F A ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ F F Z J A ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 21 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ A J J J J Z Z Z Z Z J F ; ; F J J F A ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ F Z J J J A ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 22 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ F F ; ; J J J J J J J ; ~ ; ; J J F F ; ~ ~ ~ ~ ~ ~ ~ ~ F Z J J J F A ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 23 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ F J ; ~ ; J J J J J J J ; ; P ; J J F F ; ~ ~ ~ ~ ~ ~ F F Z J J F F F F A ~ ~ ~ ~ ~ ~ ~ ~ ~ 24 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ A J ; ; P J J J J J J J F ; ; F J J F F A ~ ~ ~ ~ ~ F Z Z J F F F F F F A ~ ~ ~ ~ ~ ~ ~ ~ ~ 25 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ A F J F ; F J J A F J J J J J J J > > F F F ; ~ ~ F F J J J F F F F F F F A ~ ~ ~ ~ ~ ~ ~ ~ ~ 26 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; R J J J J F J J J J J F J J J > > > = F F ; A A J J F F F F F F F F F F A ~ ~ ~ ~ ~ ~ ~ ~ ~ 27 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; > F J J J J F = = = = F J J J > > > = F A A Z F A F F F F F F F F F F F A ~ ~ ~ ~ ~ ~ ~ ~ ~ 28 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ A ~ ; = J J J J J = = R R J J J J > > = = A Z F J Z Z A F F F F F F F F F F ; ~ ~ ~ ~ ~ ~ ~ ~ ~ 29 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ A Z A A = J J J J J = R R = J J J J J = = F A J J J J F A F F F F F F F F A ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 30 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ A Z J J F A J J J J J J = = J J J J J J J F A J J J J J J ; F F F F F F A ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 31 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ F J J F F A J J J J J J J J J J J J J J A J J J J F F ; F F F F F A ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 32 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ A F F F F F F J J J J J J J J J J J J J J J J J F F F ; F F F F A ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 33 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; F F F F J J J J J J J J J J J J J J J J J F F F ; A F F F ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 34 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; F F F J J J J J J J J J J J J J J J J F F F ; ~ ~ A F F F ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 35 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; A J J J J J J J J J J J J J J J J F F F F ; ~ ~ ~ A F F F ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 36 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ F J J J J J J J J J J J J J J J J F F F A ~ ~ A A F F F F F ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 37 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ A J J J J J J J J J J J J J J J J J F F F A ; ; J F F F F A ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 38 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ F J J J J J J J J J J J J J J J J J F F F A J F F F F A ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 39 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ A J J J J J J J J J J J J J J J J F F F F A F F F F ; ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 40 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ A A A J J J J J J J J J J J J J J F F F F F ; F F F ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 41 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ A Z Z F A J J J J J J J J J J J J J F F F F F ; ; F F F ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 42 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ F F J F J A J J J J J J J J J J J F F F F F F ; ~ ; F F F ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 43 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ A F F F F F J J J J J J J J J F F F F F F F F ; ; A A A F F ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 44 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ F F F F F A F F J J J J F F F F F F F F F F A A A A ; ; ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 45 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ A F F F A F F F F F F F F F F F F F F F A A ; ; ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 46 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; F F F ; F F F F F F F F F F F F F F F ; ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 47 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ A ; ; F F F F F F F F F F F F F F F F ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 48 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; ; ; ; A F F F F F F F F F F ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 49 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; ; A F F F F F A ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 50 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; ; F F ; ; ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 51 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ A F F F F ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 52 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ A F A F F ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 53 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ A F F F ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 54 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ; ; ; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 55 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 56 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 57 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 58 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 59 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 60 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 61 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 62 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 63 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ```

DelgadoPanadero/Pokemon

2022-03-02T23:29:22+00:00

{}

2022-01-03T10:10:40+00:00

TAGS #region-us

# Pokemon Dataset This dataset contains a text representation of more that 10k pokemon sprites from different pokemon videogames (red, yellow, gold, ruby,...). The original images are from 40 to 96 pixel of size and every pixel is represented with an ASCII character depending to its color. # Supported Tasks * Text Generation # Languages * ASCII colo representation # Data Fields # License * All the creative right are property of Nintendo # Preview

8d33120c04ada67489ab862d4a8e1438a1114316

# Asian Language Treebank (ALT) This is a **subset** of ALT dataset published by Riza et al. It included following low-resource languages: - fil - vi - id - ms - khm - th - hi - my It also includes ja and zh languages.

DeskDown/ALTDataset

2022-03-02T23:29:22+00:00

{}

2022-02-13T17:03:25+00:00

TAGS #region-us

# Asian Language Treebank (ALT) This is a subset of ALT dataset published by Riza et al. It included following low-resource languages: - fil - vi - id - ms - khm - th - hi - my It also includes ja and zh languages.

27aeb98712ca9cded7d7fadd0027afdbe4f22746

__Introduction__ The ALT project aims to advance the state-of-the-art Asian natural language processing (NLP) techniques through the open collaboration for developing and using ALT. It was first conducted by NICT and UCSY as described in Ye Kyaw Thu, Win Pa Pa, Masao Utiyama, Andrew Finch and Eiichiro Sumita (2016). Then, it was developed under ASEAN IVO as described in this Web page. The process of building ALT began with sampling about 20,000 sentences from English Wikinews, and then these sentences were translated into the other languages. ALT now has 13 languages: Bengali, English, Filipino, Hindi, Bahasa Indonesia, Japanese, Khmer, Lao, Malay, Myanmar (Burmese), Thai, Vietnamese, Chinese (Simplified Chinese). In this dataset you can find parallel corpus of fil, vi, id, ms, ja, khm languages. Dataset is tokenized using mbart50-like tokenizer. (To be added soon) Tokens are padded\truncated at a size of 128.

DeskDown/ALTDataset_en-to-fil-vi-id-ms-ja-khm

2022-03-02T23:29:22+00:00

{}

2022-01-03T22:31:36+00:00

TAGS #region-us

__Introduction__ The ALT project aims to advance the state-of-the-art Asian natural language processing (NLP) techniques through the open collaboration for developing and using ALT. It was first conducted by NICT and UCSY as described in Ye Kyaw Thu, Win Pa Pa, Masao Utiyama, Andrew Finch and Eiichiro Sumita (2016). Then, it was developed under ASEAN IVO as described in this Web page. The process of building ALT began with sampling about 20,000 sentences from English Wikinews, and then these sentences were translated into the other languages. ALT now has 13 languages: Bengali, English, Filipino, Hindi, Bahasa Indonesia, Japanese, Khmer, Lao, Malay, Myanmar (Burmese), Thai, Vietnamese, Chinese (Simplified Chinese). In this dataset you can find parallel corpus of fil, vi, id, ms, ja, khm languages. Dataset is tokenized using mbart50-like tokenizer. (To be added soon) Tokens are padded\truncated at a size of 128.

578af74e6d9abf50e091ad2292a79dda85998e0f

Human Activity Recognition (HAR) using smartphones dataset. Classifying the type of movement amongst five categories: - WALKING, - WALKING_UPSTAIRS, - WALKING_DOWNSTAIRS, - SITTING, - STANDING The experiments have been carried out with a group of 16 volunteers within an age bracket of 19-26 years. Each person performed five activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING) wearing a smartphone (Samsung Galaxy S8) in the pucket. Using its embedded accelerometer and gyroscope, we captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The experiments have been video-recorded to label the data manually. ```bash 'raw_data/labels.txt': include all the activity labels available for the dataset (1 per row). Column 1: experiment number ID, Column 2: user number ID, Column 3: activity number ID Column 4: Label start point (in number of signal log samples (recorded at 50Hz)) Column 5: Label end point (in number of signal log samples) activity_type: 1 WALKING 2 WALKING_UPSTAIRS 3 WALKING_DOWNSTAIRS 4 SITTING 5 STANDING ``` Repository: [DiFronzo/LSTM-for-Human-Activity-Recognition-classification](https://github.com/DiFronzo/LSTM-for-Human-Activity-Recognition-classification)

DiFronzo/Human_Activity_Recognition

2022-03-02T23:29:22+00:00

{}

2022-02-08T11:18:07+00:00

TAGS #region-us

Human Activity Recognition (HAR) using smartphones dataset. Classifying the type of movement amongst five categories: - WALKING, - WALKING_UPSTAIRS, - WALKING_DOWNSTAIRS, - SITTING, - STANDING The experiments have been carried out with a group of 16 volunteers within an age bracket of 19-26 years. Each person performed five activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING) wearing a smartphone (Samsung Galaxy S8) in the pucket. Using its embedded accelerometer and gyroscope, we captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The experiments have been video-recorded to label the data manually. Repository: DiFronzo/LSTM-for-Human-Activity-Recognition-classification

54e3bc2eb96a5f8c346ca715909f717f02eba22b

Datasets for Relation Extraction Task Source from Wikipedia (CC-BY-2.0) Contributors : Doohae Jung, Hyesu Kim, Bosung Kim, Isaac Park, Miwon Jeon, Dagon Lee, Jihoo Kim

Doohae/modern_music_re

2022-03-02T23:29:22+00:00

{}

2021-12-06T05:58:20+00:00

TAGS #region-us

Datasets for Relation Extraction Task Source from Wikipedia (CC-BY-2.0) Contributors : Doohae Jung, Hyesu Kim, Bosung Kim, Isaac Park, Miwon Jeon, Dagon Lee, Jihoo Kim

2933270d52e548c9efd75451f085034d145c748c

This datasets consists in the last version of the common-voice-dataset for romanian language. Also contains data from RSS (Romanian Speech Synthesis Dataset) from this site http://romaniantts.com/

Dumiiii/common-voice-romaniarss

2022-03-02T23:29:22+00:00

{}

2022-01-11T11:29:09+00:00

TAGS #region-us

This datasets consists in the last version of the common-voice-dataset for romanian language. Also contains data from RSS (Romanian Speech Synthesis Dataset) from this site URL