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https://api.github.com/repos/huggingface/datasets/issues/1025 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1025/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1025/comments | https://api.github.com/repos/huggingface/datasets/issues/1025/events | https://github.com/huggingface/datasets/pull/1025 | 755,673,371 | MDExOlB1bGxSZXF1ZXN0NTMxMzQxNjE5 | 1,025 | Add Sesotho Ner | {
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"looks like this PR include changes to other files (sepedi)\r\ncould you try to only include the files related to the addition of sesotho ner ?",
"I think i need to clean up my local repo. I am committing everything a fresh after sepedi",
"Feel free to ping me when yuo have a clean PR and it's ready to review :)",
"closing in favor of #1114 "
] | 1,606,950,015,000 | 1,608,136,023,000 | 1,608,136,022,000 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/datasets/issues/1024 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1024/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1024/comments | https://api.github.com/repos/huggingface/datasets/issues/1024/events | https://github.com/huggingface/datasets/pull/1024 | 755,664,113 | MDExOlB1bGxSZXF1ZXN0NTMxMzMzOTc5 | 1,024 | Add ZEST: ZEroShot learning from Task descriptions | {
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"Looks good to me, we can ping the authors for more info later. And yes apply `other-task` labels liberally, we can sort them out later :) \r\n\r\nLooks ready to merge when you're ready @joeddav "
] | 1,606,948,880,000 | 1,607,023,260,000 | 1,607,011,755,000 | CONTRIBUTOR | null | Adds the ZEST dataset on zero-shot learning from task descriptions from AI2.
- Webpage: https://allenai.org/data/zest
- Paper: https://arxiv.org/abs/2011.08115
The nature of this dataset made the supported task tags tricky if you wouldn't mind giving any feedback @yjernite. Also let me know if you think we should have a `other-task-generalization` or something like that... | {
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- https://github.com/google-research-datasets/dstc8-schema-guided-dialogue
A bit simpler than MultiWOZ, the only tricky thing is the sequence of dictionaries that had to be linearized. There is a config for the data proper, and a config for the schemas. | {
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"THanks!\r\nDone!"
] | 1,606,947,476,000 | 1,607,042,066,000 | 1,607,042,065,000 | MEMBER | null | MRQA (shared task 2019)
out of distribution generalization
Framed as extractive question answering
Dataset is the concatenation (of subsets) of existing QA datasets processed to match the SQuAD format | {
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"Description: \"A clean data resource containing all explicit time references in a dataset of 52,183 novels whose full text is available via Project Gutenberg and the Hathi Trust Digital Library 2.\" > This is just the Gutenberg part.\r\n\r\nAlso, the paragraph at the top of the file would make a good Dataset Summary in the README :) "
] | 1,606,946,726,000 | 1,606,991,619,000 | 1,606,991,618,000 | MEMBER | null | This PR adds the gutenberg_time dataset: https://arxiv.org/abs/2011.04124 | {
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https://api.github.com/repos/huggingface/datasets/issues/1018 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1018/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1018/comments | https://api.github.com/repos/huggingface/datasets/issues/1018/events | https://github.com/huggingface/datasets/pull/1018 | 755,570,882 | MDExOlB1bGxSZXF1ZXN0NTMxMjU3NTU2 | 1,018 | Add Sepedi NER | {
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"Sorry for this. I deleted sepedi_ner_corpus as per your earlier advise. Let me check. "
] | 1,606,939,265,000 | 1,607,032,023,000 | 1,607,031,998,000 | CONTRIBUTOR | null | This is a new branch created for this dataset | {
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"Thanks!"
] | 1,606,938,045,000 | 1,606,956,265,000 | 1,606,956,265,000 | MEMBER | null | If not specified, Python uses system default, which for Windows is not "utf-8". | {
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- [x] Followed the instructions in CONTRIBUTING.md
- [x] Ran the tests successfully
- [x] Created the dummy data | {
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https://api.github.com/repos/huggingface/datasets/issues/1015 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1015/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1015/comments | https://api.github.com/repos/huggingface/datasets/issues/1015/events | https://github.com/huggingface/datasets/pull/1015 | 755,508,841 | MDExOlB1bGxSZXF1ZXN0NTMxMjA2MTgy | 1,015 | add hard dataset | {
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"Thanks @sumanthd17 that fixed it. "
] | 1,606,933,656,000 | 1,607,007,834,000 | 1,607,007,834,000 | CONTRIBUTOR | null | Hotel Reviews in Arabic language. | {
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https://api.github.com/repos/huggingface/datasets/issues/1014 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1014/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1014/comments | https://api.github.com/repos/huggingface/datasets/issues/1014/events | https://github.com/huggingface/datasets/pull/1014 | 755,505,851 | MDExOlB1bGxSZXF1ZXN0NTMxMjAzNzAz | 1,014 | Add SciTLDR Dataset (Take 2) | {
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"@lhoestq please review this PR when you get free",
"If the CI fails just because of `RemoteDatasetTest` errors it's fine, they're fixed on master",
"> If the CI fails just because of `RemoteDatasetTest` errors it's fine, they're fixed on master\r\n\r\nThe same 3 tests are failing again :(\r\n```\r\nFAILED tests/test_dataset_common.py::RemoteDatasetTest::test_builder_class_norwegian_ner\r\nFAILED tests/test_dataset_common.py::RemoteDatasetTest::test_builder_configs_norwegian_ner\r\nFAILED tests/test_dataset_common.py::RemoteDatasetTest::test_load_dataset_norwegian_ner\r\n```",
"One trick if you want to add more datasets to avoid these errors : you can just rebase the master branch of your fork from the master branch of the repo. Then each time you make a new branch from master on your fork, it will include the fix for these errors",
"> One trick if you want to add more datasets to avoid these errors : you can just rebase the master branch of your fork from the master branch of the repo. Then each time you make a new branch from master on your fork, it will include the fix for these errors\r\n\r\nYes, I almost always do that, but somehow seems even this branch got old 😓 \r\nI also do the following if I directly create a new branch locally: `git checkout -b <branchname> upstream/master` so it stays up-to date irrespective of my fork, still don't know how this crept in again",
"Merging this one since the CI is fixed on master"
] | 1,606,933,370,000 | 1,606,935,310,000 | 1,606,934,278,000 | CONTRIBUTOR | null | Adds the SciTLDR Dataset by AI2
Added the `README.md` card with tags to the best of my knowledge
Multi-target summaries or TLDRs of Scientific Documents
Continued from #986 | {
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https://arxiv.org/pdf/2005.04177.pdf | {
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"IMO, the problem with this dataset is that it is not really a text/nlp dataset. These are just collections of tweet ids. So, ultimately, one needs to crawl twitter to get the actual text.",
"That's true.\r\n\r\n",
"at least it's clear in the description that one needs to collect the tweets : \r\n```\r\nThis resource is a result of a generic method for collecting parallel tweets.\r\n```",
"Looks like this is failing for other datasets. Should I rebase it and push again?\r\nAlso rebasing and pushing is reflecting changes in many other files (ultimately forcing me to open a new branch and a new PR) any way to avoid this?",
"No let me merge this one directly, it's fine",
"merging since the CI is fixed on master"
] | 1,606,929,602,000 | 1,607,093,110,000 | 1,607,093,073,000 | CONTRIBUTOR | null | Added Bilingual Corpus of Arabic-English Parallel Tweets. The link to the dataset can be found [here](https://alt.qcri.org/wp-content/uploads/2020/08/Bilingual-Corpus-of-Arabic-English-Parallel-Tweets.zip) and the paper can be found [here](https://www.aclweb.org/anthology/2020.bucc-1.3.pdf)
- [x] Followed the instructions in CONTRIBUTING.md
- [x] Ran the tests successfully
- [x] Created the dummy data | {
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"Dupe of #1009 "
] | 1,606,922,885,000 | 1,606,923,655,000 | 1,606,923,655,000 | CONTRIBUTOR | null | null | {
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"feel free to merge/ping me to merge if there're no more changes to do"
] | 1,606,922,173,000 | 1,607,013,878,000 | 1,606,932,092,000 | MEMBER | null | This PR adds yahoo answers topic classification dataset.
More info:
https://github.com/LC-John/Yahoo-Answers-Topic-Classification-Dataset
cc @joeddav, @yjernite | {
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https://api.github.com/repos/huggingface/datasets/issues/1004 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1004/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1004/comments | https://api.github.com/repos/huggingface/datasets/issues/1004/events | https://github.com/huggingface/datasets/issues/1004 | 755,325,368 | MDU6SXNzdWU3NTUzMjUzNjg= | 1,004 | how large datasets are handled under the hood | {
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"This library uses Apache Arrow under the hood to store datasets on disk.\r\nThe advantage of Apache Arrow is that it allows to memory map the dataset. This allows to load datasets bigger than memory and with almost no RAM usage. It also offers excellent I/O speed.\r\n\r\nFor example when you access one element or one batch\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nsquad = load_dataset(\"squad\", split=\"train\")\r\nfirst_element = squad[0]\r\none_batch = squad[:8]\r\n```\r\n\r\nthen only this element/batch is loaded in memory, while the rest of the dataset is memory mapped.",
"How can we change how much data is loaded to memory with Arrow? I think that I am having some performance issue with it. When Arrow loads the data from disk it does it in multiprocess? It's almost twice slower training with arrow than in memory.\r\n\r\nEDIT:\r\nMy fault! I had not seen the `dataloader_num_workers` in `TrainingArguments` ! Now I can parallelize and go fast! Sorry, and thanks.",
"> How can we change how much data is loaded to memory with Arrow? I think that I am having some performance issue with it. When Arrow loads the data from disk it does it in multiprocess? It's almost twice slower training with arrow than in memory.\r\n\r\nLoading arrow data from disk is done with memory-mapping. This allows to load huge datasets without filling your RAM.\r\nMemory mapping is almost instantaneous and is done within one process.\r\n\r\nThen, the speed of querying examples from the dataset is I/O bounded depending on your disk. If it's an SSD then fetching examples from the dataset will be very fast.\r\nBut since the I/O speed of an SSD is lower than the one of RAM it's expected to be slower to fetch data from disk than from memory.\r\nStill, if you load the dataset in different processes then it can be faster but there will still be the I/O bottleneck of the disk.\r\n\r\n> EDIT:\r\n> My fault! I had not seen the `dataloader_num_workers` in `TrainingArguments` ! Now I can parallelize and go fast! Sorry, and thanks.\r\n\r\nOk let me know if that helps !\r\n"
] | 1,606,919,560,000 | 1,612,175,031,000 | null | NONE | null | Hi
I want to use multiple large datasets with a mapping style dataloader, where they cannot fit into memory, could you tell me how you handled the datasets under the hood? is this you bring all in memory in case of mapping style ones? or is this some sharding under the hood and you bring in memory when necessary, thanks | {
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github repo: https://github.com/yaolu/Multi-XScience
paper: [Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles](https://arxiv.org/abs/2010.14235) | {
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"Could you fix the dummy data before we merge ?\r\nLooks like the dummy `train.csv` is missing",
"Thanks @Narsil @lhoestq for adding MeDAL :)"
] | 1,606,918,397,000 | 1,607,360,283,000 | 1,607,001,273,000 | CONTRIBUTOR | null | null | {
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} | [] | null | [] | 1,606,913,443,000 | 1,606,994,248,000 | 1,606,994,248,000 | CONTRIBUTOR | null | `generated_reviews_enth` is created as part of [scb-mt-en-th-2020](https://arxiv.org/pdf/2007.03541.pdf) for machine translation task. This dataset (referred to as `generated_reviews_yn` in [scb-mt-en-th-2020](https://arxiv.org/pdf/2007.03541.pdf)) are English product reviews generated by [CTRL](https://arxiv.org/abs/1909.05858), translated by Google Translate API and annotated as accepted or rejected (`correct`) based on fluency and adequacy of the translation by human annotators. This allows it to be used for English-to-Thai translation quality esitmation (binary label), machine translation, and sentiment analysis. | {
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More info:
https://ciir.cs.umass.edu/downloads/nfL6/ | {
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"#978 is working on adding code refinement\r\n\r\nmaybe we should keep the CodeXGlue benchmark (as glue) and don't merge the code_refinement dataset proposed in #978 ?\r\n\r\ncc @reshinthadithyan",
"Hi @madlag and @lhoestq , I am extremely interested in getting this dataset into HF's library as I research in this area a lot. I see that it hasn't been updated in a while, but it is very close to being finished. If no one is currently working on this, I'd be happy to do any final touches that might be needed to get this merged.",
"Hi @ncoop57 ! Thanks for your interest and sorry for the inactivity on this PR.\r\nSure feel free to create another PR to continue this one ! This one was really close to being merged so I think it won't require that much changes. In addition to my previous comments, there should also be a \"Contributions\" subsection (see the template of the README [here](https://github.com/huggingface/datasets/blob/master/templates/README.md))",
"Superseded by https://github.com/huggingface/datasets/pull/2357 ."
] | 1,606,908,078,000 | 1,623,159,745,000 | 1,623,159,744,000 | CONTRIBUTOR | null | Datasets from https://github.com/microsoft/CodeXGLUE
This contains 13 datasets:
code_x_glue_cc_clone_detection_big_clone_bench
code_x_glue_cc_clone_detection_poj_104
code_x_glue_cc_cloze_testing_all
code_x_glue_cc_cloze_testing_maxmin
code_x_glue_cc_code_completion_line
code_x_glue_cc_code_completion_token
code_x_glue_cc_code_refinement
code_x_glue_cc_code_to_code_trans
code_x_glue_cc_defect_detection
code_x_glue_ct_code_to_text
code_x_glue_tc_nl_code_search_adv
code_x_glue_tc_text_to_code
code_x_glue_tt_text_to_text
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"Looks like the google drive download failed.\r\nI'm getting a `Google Drive - Quota exceeded` error while looking at the downloaded file.\r\n\r\nWe should consider finding a better host than google drive for this dataset imo\r\nrelated : #873 #864 ",
"It is working now, thank you. \r\n\r\nShould I leave this issue open to address the Quota-exceeded error?",
"Yes please. It's been happening several times, we definitely need to address it",
"Any updates on this one? I'm facing a similar issue trying to add CelebA.",
"I've looked into it and couldn't find a solution. This looks like a Google Drive limitation..\r\nPlease try to use other hosts when possible",
"The original links are google drive links. Would it be feasible for HF to maintain their own servers for this? Also, I think the same issue must also exist with TFDS.",
"It's possible to host data on our side but we should ask the authors. TFDS has the same issue and doesn't have a solution either afaik.\r\nOtherwise you can use the google drive link, but it it's not that convenient because of this quota issue.",
"Okay. I imagine asking every author who shares their dataset on Google Drive will also be cumbersome.",
"I am getting this error as well. Is there a fix?",
"Not as long as the data is stored on GG drive unfortunately.\r\nMaybe we can ask if there's a mirror ?\r\n\r\nHi @JafferWilson is there a download link to get cnn dailymail from another host than GG drive ?\r\n\r\nTo give you some context, this library provides tools to download and process datasets. For CNN DailyMail the data are downloaded from the link you provide on your github repository. Unfortunately because of GG drive quotas, many users are not able to load this dataset.",
"The following copy of CNN/DM dataset, fixed the problem for me:\r\nhttps://huggingface.co/datasets/ccdv/cnn_dailymail",
"Thanks for the link @mrazizi !\r\n\r\nApparently the original authors don't host the dataset themselves (\"for legal reasons\", source [here](https://github.com/abisee/cnn-dailymail/issues/9))."
] | 1,606,907,276,000 | 1,640,082,003,000 | null | NONE | null |
Downloading and preparing dataset cnn_dailymail/3.0.0 (download: 558.32 MiB, generated: 1.28 GiB, post-processed: Unknown size, total: 1.82 GiB) to /root/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602...
---------------------------------------------------------------------------
NotADirectoryError Traceback (most recent call last)
<ipython-input-9-cd4bf8bea840> in <module>()
22
23
---> 24 train = load_dataset('cnn_dailymail', '3.0.0', split='train')
25 validation = load_dataset('cnn_dailymail', '3.0.0', split='validation')
26 test = load_dataset('cnn_dailymail', '3.0.0', split='test')
5 frames
/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)
132 else:
133 logging.fatal("Unsupported publisher: %s", publisher)
--> 134 files = sorted(os.listdir(top_dir))
135
136 ret_files = []
NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories' | {
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"Blocked @k125-ak ;) Bye-bye"
] | 1,606,907,199,000 | 1,607,079,496,000 | 1,606,988,377,000 | CONTRIBUTOR | null | Dataset Circa added. Only README.md and dataset card left | {
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"Looks like the PR includes commits about many other files.\r\nCould you create a clean branch from master, and create another PR ?",
"Sorry, will do that. "
] | 1,606,905,007,000 | 1,606,990,754,000 | 1,606,933,208,000 | CONTRIBUTOR | null | {
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"Hi @hfawaz ! This is working fine for me. Is it a repeated occurence? Have you tried from the latest verion?",
"Hi, it seems a connection problem. \r\nNow it says: \r\n`ConnectionError: Couldn't reach https://amazon-reviews-ml.s3-us-west-2.amazonaws.com/json/train/dataset_ja_train.json`"
] | 1,606,904,157,000 | 1,607,074,693,000 | null | CONTRIBUTOR | null | Thanks for adding the dataset.
After trying to load the dataset, I am getting the following error:
`ConnectionError: Couldn't reach https://amazon-reviews-ml.s3-us-west-2.amazonaws.com/json/train/dataset_fr_train.json
`
I used the following code to load the dataset:
`load_dataset(
dataset_name,
"all_languages",
cache_dir=".data"
)`
I am using version 1.1.3 of `datasets`
Note that I can perform a successfull `wget https://amazon-reviews-ml.s3-us-west-2.amazonaws.com/json/train/dataset_fr_train.json` | {
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More info here : http://www.macs.hw.ac.uk/InteractionLab/E2E/
### Checkbox
- [x] Create the dataset script `/datasets/my_dataset/my_dataset.py` using the template
- [x] Fill the `_DESCRIPTION` and `_CITATION` variables
- [x] Implement `_infos()`, `_split_generators()` and `_generate_examples()`
- [x] Make sure that the `BUILDER_CONFIGS` class attribute is filled with the different configurations of the dataset and that the `BUILDER_CONFIG_CLASS` is specified if there is a custom config class.
- [x] Generate the metadata file `dataset_infos.json` for all configurations
- [x] Generate the dummy data `dummy_data.zip` files to have the dataset script tested and that they don't weigh too much (<50KB)
- [x] Add the dataset card `README.md` using the template and at least fill the tags
- [x] Both tests for the real data and the dummy data pass.
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"my implementation of sharding per TPU core: https://github.com/google-research/ruse/blob/d4dd58a2d8efe0ffb1a9e9e77e3228d6824d3c3c/seq2seq/trainers/t5_trainer.py#L316 \r\nmy implementation of dataloader for this case https://github.com/google-research/ruse/blob/d4dd58a2d8efe0ffb1a9e9e77e3228d6824d3c3c/seq2seq/tasks/tasks.py#L496 "
] | 1,606,898,715,000 | 1,606,899,034,000 | null | CONTRIBUTOR | null | Hi
I am dealing with large-scale datasets which I need to train distributedly, I used the shard function to divide the dataset across the cores, without any sampler, this does not work for distributed training and does not become any faster than 1 TPU core. 1) how I can make sure data is not loaded in memory 2) in case of distributed training with iterative datasets which measures needs to be taken? Is this all sharding the data only. I was wondering if there can be possibility for me to discuss this with someone with distributed training with iterative datasets using dataset library. thanks | {
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"merging since the CI is fixed on master"
] | 1,606,897,832,000 | 1,607,088,461,000 | 1,607,088,461,000 | MEMBER | null | {
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"CI failures seem to be unrelated (related to `norwegian_ner`)\r\n```\r\nFAILED tests/test_dataset_common.py::RemoteDatasetTest::test_builder_class_norwegian_ner\r\nFAILED tests/test_dataset_common.py::RemoteDatasetTest::test_builder_configs_norwegian_ner\r\nFAILED tests/test_dataset_common.py::RemoteDatasetTest::test_load_dataset_norwegian_ner\r\n```",
"you can just rebase from master to fix the CI :) ",
"can you just rebase from master before we merge ?",
"Sorry, the rebase from master went horribly wrong, I guess I'll just open another PR\r\n\r\nClosing this one due to a mistake in rebasing :(",
"Continued in #1014 "
] | 1,606,896,676,000 | 1,606,934,242,000 | 1,606,932,179,000 | CONTRIBUTOR | null | Adds the SciTLDR Dataset by AI2
Added README card with tags to the best of my knowledge
Multi-target summaries or TLDRs of Scientific Documents | {
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"This dataset already exists apparently, sorry :/ \r\nsee\r\nhttps://github.com/huggingface/datasets/blob/master/datasets/gap/gap.py\r\n\r\nFeel free to re-use the dataset card you did for `/datasets/gap`\r\n",
"oh heck, my bad 🤦♂️ sorry"
] | 1,606,893,911,000 | 1,606,925,792,000 | 1,606,925,792,000 | MEMBER | null | GAP dataset
Gender bias coreference resolution | {
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"can't find the Whoa file since there' nothing left",
"The classic `rm -rf` command - nice one"
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continuing from https://github.com/huggingface/datasets/pull/954
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"Thank you for contributing a Thai dataset, @mapmeld ! I'm super hyped. \r\nOne comment I may add is that wongnai-corpus has two datasets: review classification (this) and word tokenization (https://github.com/wongnai/wongnai-corpus/blob/master/search/labeled_queries_by_judges.txt).\r\nWould it be possible for you to rename this one something along the line of `wongnai-reviews` so that when/if we include the word tokenization dataset, we will know which is which.\r\n\r\nThis helps solve my check_code_quality issue.\r\n```\r\nmake style\r\nblack --line-length 119 --target-version py36 datasets/wongnai\r\nflake8 datasets/wongnai\r\nisort datasets/wongnai/wongnai.py\r\n```",
"@cstorm125 thanks! following your suggestions on formatting and on naming the dataset\r\n\r\nI am writing a blog post about Thai NLP and transformers (example: mBERT does 1-2 character tokens instead of doing word segmentation), started adding this dataset to use as an example, and then saw you were adding other datasets. Great work! And if you know any Thai BERT models beyond https://github.com/ThAIKeras/bert we should maybe talk over email!"
] | 1,606,879,208,000 | 1,606,923,281,000 | 1,606,923,005,000 | CONTRIBUTOR | null | 40,000 reviews, previously released on GitHub ( https://github.com/wongnai/wongnai-corpus ) with an LGPL license, and on a closed Kaggle competition ( https://www.kaggle.com/c/wongnai-challenge-review-rating-prediction/ ) | {
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It was a pretty big chunk of work to figure out the structure, so I stil have tol add the description to the README.md
On the plus side the structure is broadly similar to that of the Google Schema Guided dialogue [dataset](https://github.com/google-research-datasets/dstc8-schema-guided-dialogue), so will take care of that one next. | {
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"Also cc @madlag since I recall you wanted to work on CodeXGlue as well ?",
"Yes, sorry I did not see earlier your message. I added 34 on the 35 datasets in CodeXGlue, tomorrow I will wrap it up, and so I will remove my version for code_refinement. Maybe we can just have a renaming after the merge, to have a consistent naming with all the other codexglue datasets ? What do you think @reshinthadithyan ?",
"> Yes, sorry I did not see earlier your message. I added 34 on the 35 datasets in CodeXGlue, tomorrow I will wrap it up, and so I will remove my version for code_refinement. Maybe we can just have a renaming after the merge, to have a consistent naming with all the other codexglue datasets ? What do you think @reshinthadithyan ?\r\n\r\nHello @madlag, I think you can retain that in your script. Let's stick onto the same file like how Glue is maintained.",
"Hi @reshinthadithyan ! Are you still working on this version of the dataset or are we going with @madlag 's only ?",
"> Hi @reshinthadithyan ! Are you still working on this version of the dataset or are we going with @madlag 's only ?\r\n\r\nHello, yes. We are going with Madlag's"
] | 1,606,872,598,000 | 1,607,305,978,000 | 1,607,305,978,000 | CONTRIBUTOR | null | ### OVERVIEW
Millions of open-source projects with numerous bug fixes
are available in code repositories. This proliferation
of software development histories can be leveraged to
learn how to fix common programming bugs
Code refinement aims to automatically fix bugs in the code,
which can contribute to reducing the cost of bug-fixes for developers.
Given a piece of Java code with bugs,
the task is to remove the bugs to output the refined code. | {
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Reasoning over paragraph effects in situations - testing a system's ability to apply knowledge from a passage of text to a new situation. The task is framed into a reading comprehension task following squad-style extractive qa.
One thing to note: labels of the test set are hidden (leaderboard submission) so I encoded that as an empty list (ropes.py:L125) | {
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"looks like this PR includes changes about many other files than the oens for Araboc POS Dialect\r\n\r\nCan you create a another branch and another PR please ?",
"Sorry! I'm not sure how I managed to do that. I'll make a new branch."
] | 1,606,868,473,000 | 1,607,535,032,000 | 1,607,535,032,000 | CONTRIBUTOR | null | A README.md and loading script for the Arabic POS Dialect dataset. The README is missing the sections on personal information, biases, and limitations, as it would probably be better for those to be filled by someone who can read the contents of the dataset and is familiar with Arabic NLP. | {
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} | [] | null | [] | 1,606,868,155,000 | 1,606,906,736,000 | 1,606,906,735,000 | CONTRIBUTOR | null | This PR adds the #MeToo MA dataset. It presents multi-label data points for tweets mined in the backdrop of the #MeToo movement. The dataset includes data points in the form of Tweet ids and appropriate labels. Please refer to the accompanying paper for detailed information regarding annotation, collection, and guidelines.
Paper: https://ojs.aaai.org/index.php/ICWSM/article/view/7292
Dataset Link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/JN4EYU
---
annotations_creators:
- expert-generated
language_creators:
- found
languages:
- en
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
- text-retrieval
task_ids:
- multi-class-classification
- multi-label-classification
---
# Dataset Card for #MeTooMA dataset
## 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://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/JN4EYU
- **Paper:** https://ojs.aaai.org//index.php/ICWSM/article/view/7292
- **Point of Contact:** https://github.com/midas-research/MeTooMA
### Dataset Summary
- The dataset consists of tweets belonging to #MeToo movement on Twitter, labeled into different categories.
- This dataset includes more data points and has more labels than any of the previous datasets that contain social media
posts about sexual abuse disclosures. Please refer to the Related Datasets of the publication for detailed information about this.
- Due to Twitter's development policies, the authors provide only the tweet IDs and corresponding labels,
other data can be fetched via Twitter API.
- The data has been labeled by experts, with the majority taken into the account for deciding the final label.
- The authors provide these labels for each of the tweets.
- Relevance
- Directed Hate
- Generalized Hate
- Sarcasm
- Allegation
- Justification
- Refutation
- Support
- Oppose
- The definitions for each task/label are in the main publication.
- Please refer to the accompanying paper https://aaai.org/ojs/index.php/ICWSM/article/view/7292 for statistical analysis on the textual data
extracted from this dataset.
- The language of all the tweets in this dataset is English
- Time period: October 2018 - December 2018
- Suggested Use Cases of this dataset:
- Evaluating usage of linguistic acts such as hate-speech and sarcasm in the context of public sexual abuse disclosures.
- Extracting actionable insights and virtual dynamics of gender roles in sexual abuse revelations.
- Identifying how influential people were portrayed on the public platform in the
events of mass social movements.
- Polarization analysis based on graph simulations of social nodes of users involved
in the #MeToo movement.
### Supported Tasks and Leaderboards
Multi-Label and Multi-Class Classification
### Languages
English
## Dataset Structure
- The dataset is structured into CSV format with TweetID and accompanying labels.
- Train and Test sets are split into respective files.
### Data Instances
Tweet ID and the appropriate labels
### Data Fields
Tweet ID and appropriate labels (binary label applicable for a data point) and multiple labels for each Tweet ID
### Data Splits
- Train: 7979
- Test: 1996
## Dataset Creation
### Curation Rationale
- Twitter was the major source of all the public disclosures of sexual abuse incidents during the #MeToo movement.
- People expressed their opinions over issues that were previously missing from the social media space.
- This provides an option to study the linguistic behaviors of social media users in an informal setting,
therefore the authors decide to curate this annotated dataset.
- The authors expect this dataset would be of great interest and use to both computational and socio-linguists.
- For computational linguists, it provides an opportunity to model three new complex dialogue acts (allegation, refutation, and justification) and also to study how these acts interact with some of the other linguistic components like stance, hate, and sarcasm. For socio-linguists, it provides an opportunity to explore how a movement manifests in social media.
### Source Data
- Source of all the data points in this dataset is a Twitter social media platform.
#### Initial Data Collection and Normalization
- All the tweets are mined from Twitter with initial search parameters identified using keywords from the #MeToo movement.
- Redundant keywords were removed based on manual inspection.
- Public streaming APIs of Twitter was used for querying with the selected keywords.
- Based on text de-duplication and cosine similarity score, the set of tweets were pruned.
- Non-English tweets were removed.
- The final set was labeled by experts with the majority label taken into the account for deciding the final label.
- Please refer to this paper for detailed information: https://ojs.aaai.org//index.php/ICWSM/article/view/7292
#### Who are the source language producers?
Please refer to this paper for detailed information: https://ojs.aaai.org//index.php/ICWSM/article/view/7292
### Annotations
#### Annotation process
- The authors chose against crowdsourcing for labeling this dataset due to its highly sensitive nature.
- The annotators are domain experts having degrees in advanced clinical psychology and gender studies.
- They were provided a guidelines document with instructions about each task and its definitions, labels, and examples.
- They studied the document, worked on a few examples to get used to this annotation task.
- They also provided feedback for improving the class definitions.
- The annotation process is not mutually exclusive, implying that the presence of one label does not mean the
absence of the other one.
#### Who are the annotators?
- The annotators are domain experts having a degree in clinical psychology and gender studies.
- Please refer to the accompanying paper for a detailed annotation process.
### Personal and Sensitive Information
- Considering Twitter's policy for distribution of data, only Tweet ID and applicable labels are shared for public use.
- It is highly encouraged to use this dataset for scientific purposes only.
- This dataset collection completely follows the Twitter mandated guidelines for distribution and usage.
## Considerations for Using the Data
### Social Impact of Dataset
- The authors of this dataset do not intend to conduct a population-centric analysis of the #MeToo movement on Twitter.
- The authors acknowledge that findings from this dataset cannot be used as-is for any direct social intervention, these
should be used to assist already existing human intervention tools and therapies.
- Enough care has been taken to ensure that this work comes off as trying to target a specific person for their
the personal stance of issues pertaining to the #MeToo movement.
- The authors of this work do not aim to vilify anyone accused in the #MeToo movement in any manner.
- Please refer to the ethics and discussion section of the mentioned publication for appropriate sharing of this dataset
and the social impact of this work.
### Discussion of Biases
- The #MeToo movement acted as a catalyst for implementing social policy changes to benefit the members of
the community affected by sexual abuse.
- Any work undertaken on this dataset should aim to minimize the bias against minority groups which
might amplify in cases of a sudden outburst of public reactions over sensitive social media discussions.
### Other Known Limitations
- Considering privacy concerns, social media practitioners should be aware of making automated interventions
to aid the victims of sexual abuse as some people might not prefer to disclose their notions.
- Concerned social media users might also repeal their social information if they found out that their
information is being used for computational purposes, hence it is important to seek subtle individual consent
before trying to profile authors involved in online discussions to uphold personal privacy.
## Additional Information
Please refer to this link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/JN4EYU
### Dataset Curators
- If you use the corpus in a product or application, then please credit the authors
and [Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi]
(http://midas.iiitd.edu.in) appropriately.
Also, if you send us an email, we will be thrilled to know about how you have used the corpus.
- If interested in the commercial use of the corpus, send an email to midas@iiitd.ac.in.
- Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi, India
disclaims any responsibility for the use of the corpus and does not provide technical support.
However, the contact listed above will be happy to respond to queries and clarifications
- Please feel free to send us an email:
- with feedback regarding the corpus.
- with information on how you have used the corpus.
- if interested in having us analyze your social media data.
- if interested in a collaborative research project.
### Licensing Information
[More Information Needed]
### Citation Information
Please cite the following publication if you make use of the dataset: https://ojs.aaai.org/index.php/ICWSM/article/view/7292
```
@article{Gautam_Mathur_Gosangi_Mahata_Sawhney_Shah_2020, title={#MeTooMA: Multi-Aspect Annotations of Tweets Related to the MeToo Movement}, volume={14}, url={https://aaai.org/ojs/index.php/ICWSM/article/view/7292}, abstractNote={<p>In this paper, we present a dataset containing 9,973 tweets related to the MeToo movement that were manually annotated for five different linguistic aspects: relevance, stance, hate speech, sarcasm, and dialogue acts. We present a detailed account of the data collection and annotation processes. The annotations have a very high inter-annotator agreement (0.79 to 0.93 k-alpha) due to the domain expertise of the annotators and clear annotation instructions. We analyze the data in terms of geographical distribution, label correlations, and keywords. Lastly, we present some potential use cases of this dataset. We expect this dataset would be of great interest to psycholinguists, socio-linguists, and computational linguists to study the discursive space of digitally mobilized social movements on sensitive issues like sexual harassment.</p&gt;}, number={1}, journal={Proceedings of the International AAAI Conference on Web and Social Media}, author={Gautam, Akash and Mathur, Puneet and Gosangi, Rakesh and Mahata, Debanjan and Sawhney, Ramit and Shah, Rajiv Ratn}, year={2020}, month={May}, pages={209-216} }
```
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"I have to manually copy a dataset_infos.json file from other dataset and modify it since the `datasets-cli` isn't able to handle manually downloaded datasets yet (as far as I know).",
"you can generate the dataset_infos.json file even for dataset with manual data\r\nTo do so just specify `--data_dir <path/to/the/folder/containing/the/manual/data>`",
"Also, dummy_data seems having difficulty to handle manually downloaded datasets. `python datasets-cli dummy_data datasets/ms_terms --data_dir ...` reported `error: unrecognized arguments: --data_dir` error. Without `--data_dir`, it reported this error:\r\n```\r\nDataset ms_terms with config BuilderConfig(name='ms_terms-full', version=1.0.0, data_dir=None, data_files=None, description='...\\n') seems to already open files in the method `_split_generators(...)`. You might consider to instead only open files in the method `_generate_examples(...)` instead. If this is not possible the dummy data has to be created with less guidance. Make sure you create the file None.\r\nTraceback (most recent call last):\r\n File \"datasets-cli\", line 36, in <module>\r\n service.run()\r\n File \"/Users/lzhao/Downloads/huggingface/datasets/src/datasets/commands/dummy_data.py\", line 326, in run\r\n dataset_builder=dataset_builder, mock_dl_manager=mock_dl_manager\r\n File \"/Users/lzhao/Downloads/huggingface/datasets/src/datasets/commands/dummy_data.py\", line 406, in _print_dummy_data_instructions\r\n for split in generator_splits:\r\nUnboundLocalError: local variable 'generator_splits' referenced before assignment\r\n```",
"Oh yes `--data_dir` seems to only be supported for the `datasets_cli test` command. Sorry about that.\r\n\r\nCan you try to build the dummy_data.zip file manually ?\r\n\r\nIt has to be inside `./datasets/ms_terms/dummy/ms_terms-full/1.0.0`.\r\nInside this folder, please create a folder `dummy_data` that contains a dummy file `MicrosoftTermCollection.tbx` (with just a few examples in it). Then you can zip the `dummy_data` folder to `dummy_data.zip`\r\n\r\nThen you can check if it worked using the command\r\n```\r\npytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_ms_terms\r\n```\r\n\r\nFeel free to use some debugging print statements in your script if it doesn't work first try to see what `dl_manager.manual_dir` ends up being and also `path_to_manual_file`.\r\n\r\nFeel free to ping me if you have other questions",
"`pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_ms_terms` gave `1 passed, 4 warnings in 8.13s`. Existing datasets, like `wikihow`, and `newsroom`, also report 4 warnings. So, I guess that is not related to this dataset.",
"Could you run `make style` before we merge @leoxzhao ?",
"the other errors are fixed on master so it's fine",
"> Could you run `make style` before we merge @leoxzhao ?\r\n\r\nSure thing. Done. Thanks Quentin. I have other datasets in mind. All of which requires manual download. This process is very helpful",
"Thank you :) "
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"Hi @lhoestq,\r\n\r\nI guess this PR can be closed since we merged #2044?\r\n\r\nI have used the same link for the homepage, as it is where the dataset is provided, hope that is okay?",
"Closing in favor of #2044, thanks again :)\r\n\r\n> I have used the same link for the homepage, as it is where the dataset is provided, hope that is okay?\r\n\r\nYea it's ok actually, at that time I thought there was another homepage for this dataset"
] | 1,606,863,206,000 | 1,616,153,403,000 | 1,616,153,403,000 | MEMBER | null | Add the Children's Book Test (CBT) from Facebook (Hill et al. 2016).
Sentence completion given a few sentences as context from a children's book. | {
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https://github.com/chaojiang06/wiki-auto
This is also a prospective GEM task, hence the README.md | {
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"One trick if you want to add other datasets: consider running these commands each time you want to add a new dataset\r\n```\r\ngit checkout master\r\ngit fetch upstream\r\ngit rebase upstream/master\r\ngit checkout -b add-<my_dataset_name>\r\n```"
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"Oh yeah, for some reason I thought you had to do it after the merge, I'll get on it",
"Weird, now the CI seems to fail because of other datasets (XGLUE, Norwegian_NER)",
"Yea you just need to rebase from master",
"Re-opening a PR without the messed-up rebase"
] | 1,606,843,057,000 | 1,606,931,864,000 | 1,606,931,845,000 | MEMBER | null | This PR adds the Czech restaurants dataset for Czech NLG. | {
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"Looks like your PR now shows changes in many other files than the ones for CLINC150.\r\nFeel free to create another branch and another PR",
"created new [PR](https://github.com/huggingface/datasets/pull/1016)\r\n\r\nclosing this!"
] | 1,606,841,413,000 | 1,606,934,743,000 | 1,606,934,730,000 | CONTRIBUTOR | null | Added CLINC150 Dataset. The link to the dataset can be found [here](https://github.com/clinc/oos-eval) and the paper can be found [here](https://www.aclweb.org/anthology/D19-1131.pdf)
- [x] Followed the instructions in CONTRIBUTING.md
- [x] Ran the tests successfully
- [x] Created the dummy data | {
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- [x] Followed the instructions in CONTRIBUTING.md
- [x] Ran the tests successfully
- [x] Created the dummy data | {
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"This is task is part of the GEM suite so will actually need a more complete dataset card. I'm taking a break for now though and will get back to it before merging :) "
] | 1,606,835,123,000 | 1,606,930,445,000 | 1,606,930,445,000 | MEMBER | null | This PR adds data from the WebNLG challenge, with one configuration per release and challenge iteration.
More information can be found [here](https://webnlg-challenge.loria.fr/)
Unfortunately, the data itself comes from a pretty large number of small XML files, so the dummy data ends up being quite large (8.4 MB even keeping only one example per file). | {
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More info:
https://github.com/Websail-NU/CODAH | {
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https://api.github.com/repos/huggingface/datasets/issues/961 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/961/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/961/comments | https://api.github.com/repos/huggingface/datasets/issues/961/events | https://github.com/huggingface/datasets/issues/961 | 754,434,398 | MDU6SXNzdWU3NTQ0MzQzOTg= | 961 | sample multiple datasets | {
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"here I share my dataloader currently for multiple tasks: https://gist.github.com/rabeehkarimimahabadi/39f9444a4fb6f53dcc4fca5d73bf8195 \r\n\r\nI need to train my model distributedly with this dataloader, \"MultiTasksataloader\", currently this does not work in distributed fasion,\r\nto save on memory I tried to use iterative datasets, could you have a look in this dataloader and tell me if this is indeed the case? not sure how to make datasets being iterative to not load them in memory, then I remove the sampler for dataloader, and shard the data per core, could you tell me please how I should implement this case in datasets library? and how do you find my implementation in terms of correctness? thanks \r\n"
] | 1,606,832,402,000 | 1,606,872,764,000 | null | CONTRIBUTOR | null | Hi
I am dealing with multiple datasets, I need to have a dataloader over them with a condition that in each batch data samples are coming from one of the datasets. My main question is:
- I need to have a way to sample the datasets first with some weights, lets say 2x dataset1 1x dataset2, could you point me how I can do it
sub-questions:
- I want to concat sampled datasets and define one dataloader on it, then I need a way to make sure batches come from 1 dataset in each iteration, could you assist me how I can do?
- I use iterative-type of datasets, but I need a method of shuffling still since it brings accuracy performance issues if not doing it, thanks for the help. | {
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"@lhoestq I added the README files, and now the tests fail... (check commit history, only changed MD file)\r\nThe tests seem a bit unstable",
"the `RemoteDatasetTest ` errors in the CI are fixed on master so it's fine",
"merging since the CI is fixed on master"
] | 1,606,830,077,000 | 1,607,358,939,000 | 1,607,358,939,000 | CONTRIBUTOR | null | clean #789 | {
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"Merging this one, good job and thank you @jplu :) "
] | 1,606,827,062,000 | 1,606,899,191,000 | 1,606,846,161,000 | CONTRIBUTOR | null | This PR adds the [Norwegian NER](https://github.com/ljos/navnkjenner) dataset.
I have added the `conllu` package as a test dependency. This is required to properly parse the `.conllu` files. | {
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"> Really cool ! Thanks for adding this one :)\r\n> Good job at adding all those citations for each task\r\n> \r\n> Looks like the dummy data test doesn't pass. Maybe some files are missing in the dummy_data.zip files ?\r\n> The error reports `pragmeval/verifiability/train.tsv` to be missing\r\n> \r\n> Also could you add the tags part of the dataset card (the rest is optional) ?\r\n> See more info here : https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#tag-the-dataset-and-write-the-dataset-card\r\n\r\nIn the prior commits I generated dataset_infos and the dummy files myself\r\nNow they are generated with the cli, and the tests now seem to be passing better\r\nI will look into the tag\r\n",
"Looks like you did a good job with dummy data in the first place !\r\nThe downside of automatically generated dummy data is that the files are heavier (here 40KB per file).\r\nIf you could replace the generated dummy files with the one you created yourself it would be awesome, since the one you did yourself are way lighter (around 1KB per file). Using small files make `git clone` run faster so we encourage to use small dummy_data files.",
"could you rebase from master ? it should fix the CI",
"> could you rebase from master ? it should fix the CI\r\n\r\nI think it is due to the file structure of the dummy data that causes test failure. The automatically generated dummy data pass the tests",
"Indeed the error reports that `pragmeval/verifiability/train.tsv` is missing for the verifiability dummy_data.zip file.\r\nTo fix that you should add the missing data files in each dummy_data.zip file.\r\nTo test that your dummy data work you can run\r\n```\r\nRUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_\r\n```\r\nif some file is missing it should tell you which one",
"Also it looks like you haven't rebased from master yet, even though you did a `rebase` commit. \r\n\r\nrebasing should fix the other CI fails",
"It's ok if we have `RemoteDatasetTest ` errors, they're fixed on master",
"merging since the CI is fixed on master",
"Hey @sileod! Super nice to see you participating ;)\r\n\r\nDid you officially joined the sprint by posting on [the forum thread](https://discuss.huggingface.co/t/open-to-the-community-one-week-team-effort-to-reach-v2-0-of-hf-datasets-library/2176) and joining our slack?\r\n\r\nI can't seem to find you there! Should I add you directly with your gmail address?",
"Hi @sileod 👋 "
] | 1,606,826,955,000 | 1,607,078,612,000 | 1,606,988,207,000 | CONTRIBUTOR | null | {
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"Test failing for same issues as https://github.com/huggingface/datasets/pull/939\r\nPlease advise.\r\n\r\n```\r\n=========================== short test summary info ============================\r\nFAILED tests/test_dataset_common.py::RemoteDatasetTest::test_builder_class_flue\r\nFAILED tests/test_dataset_common.py::RemoteDatasetTest::test_builder_class_norwegian_ner\r\nFAILED tests/test_dataset_common.py::RemoteDatasetTest::test_builder_configs_flue\r\nFAILED tests/test_dataset_common.py::RemoteDatasetTest::test_builder_configs_norwegian_ner\r\nFAILED tests/test_dataset_common.py::RemoteDatasetTest::test_load_dataset_flue\r\nFAILED tests/test_dataset_common.py::RemoteDatasetTest::test_load_dataset_norwegian_ner\r\nFAILED tests/test_dataset_common.py::RemoteDatasetTest::test_load_dataset_xglue\r\n===== 7 failed, 1309 passed, 932 skipped, 11 warnings in 166.71s (0:02:46) =====\r\n```",
"Closing and opening a new pull request to solve rebase issues",
"To be continued on https://github.com/huggingface/datasets/pull/982"
] | 1,606,826,455,000 | 1,606,885,931,000 | 1,606,884,232,000 | CONTRIBUTOR | null | `prachathai-67k`: News Article Corpus and Multi-label Text Classificdation from Prachathai.com
The prachathai-67k dataset was scraped from the news site Prachathai.
We filtered out those articles with less than 500 characters of body text, mostly images and cartoons.
It contains 67,889 articles wtih 12 curated tags from August 24, 2004 to November 15, 2018.
The dataset was originally scraped by @lukkiddd and cleaned by @cstorm125.
You can also see preliminary exploration at https://github.com/PyThaiNLP/prachathai-67k/blob/master/exploration.ipynb | {
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"Hi @lhoestq,\r\nInitially I tried int(-1) only in place of nan labels and missing values but I kept on getting this error ```pyarrow.lib.ArrowTypeError: Expected bytes, got a 'int' object``` maybe because I'm sending int values (-1) to objects which are string type"
] | 1,606,826,264,000 | 1,606,864,293,000 | 1,606,864,293,000 | CONTRIBUTOR | null | Added dataset Explainable Fact-Checking for Public Health Claims (dataset_id: health_fact) | {
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Paper: [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321) | {
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"Wrongly branching from existing branch of wisesight_sentiment. Closing and opening another one specifically for prachathai67k"
] | 1,606,825,312,000 | 1,606,825,793,000 | 1,606,825,706,000 | CONTRIBUTOR | null | Add `prachathai-67k`: News Article Corpus and Multi-label Text Classificdation from Prachathai.com
The `prachathai-67k` dataset was scraped from the news site [Prachathai](prachathai.com). We filtered out those articles with less than 500 characters of body text, mostly images and cartoons. It contains 67,889 articles wtih 12 curated tags from August 24, 2004 to November 15, 2018. The dataset was originally scraped by [@lukkiddd](https://github.com/lukkiddd) and cleaned by [@cstorm125](https://github.com/cstorm125). Download the dataset [here](https://www.dropbox.com/s/fsxepdka4l2pr45/prachathai-67k.zip?dl=1). You can also see preliminary exploration in [exploration.ipynb](https://github.com/PyThaiNLP/prachathai-67k/blob/master/exploration.ipynb).
This dataset is a part of [pyThaiNLP](https://github.com/PyThaiNLP/) Thai text [classification-benchmarks](https://github.com/PyThaiNLP/classification-benchmarks). For the benchmark, we selected the following tags with substantial volume that resemble **classifying types of articles**:
* `การเมือง` - politics
* `สิทธิมนุษยชน` - human_rights
* `คุณภาพชีวิต` - quality_of_life
* `ต่างประเทศ` - international
* `สังคม` - social
* `สิ่งแวดล้อม` - environment
* `เศรษฐกิจ` - economics
* `วัฒนธรรม` - culture
* `แรงงาน` - labor
* `ความมั่นคง` - national_security
* `ไอซีที` - ict
* `การศึกษา` - education | {
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"@lhoestq added. "
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"The checks failed again even if I didn't make any changes.",
"you just need to rebase from master to fix the CI :)",
"Sorry for the mess, I'm confused by the rebase and thus created a new branch."
] | 1,606,819,301,000 | 1,606,963,634,000 | 1,606,963,634,000 | CONTRIBUTOR | null | A persona-based empathetic conversation dataset published at EMNLP 2020. | {
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"Replaced by #1126"
] | 1,606,818,936,000 | 1,607,096,585,000 | 1,607,096,574,000 | MEMBER | null | Adding the English version of bAbI.
Samples are taken from ParlAI for consistency with the main users at the moment. | {
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"thanks ! merging this one"
] | 1,606,815,502,000 | 1,607,000,816,000 | 1,607,000,815,000 | MEMBER | null | {
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Two datasets are missing, the French Treebank that we can use only for research purpose and we are not allowed to distribute, and the Word Sense disambiguation for Nouns that will be added later. | {
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- **Name:** *name of the dataset*
- **Description:** *short description of the dataset (or link to social media or blog post)*
- **Paper:** *link to the dataset paper if available*
- **Data:** *link to the Github repository or current dataset location*
- **Motivation:** *what are some good reasons to have this dataset*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). | {
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"> LGTM thanks :)\n> \n> \n> \n> Before we merge, could you add a dataset card ? see here for more info: https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#tag-the-dataset-and-write-the-dataset-card\n> \n> \n> \n> Note that only the tags at the top of the dataset card are mandatory, if you feel like it's going to take too much time writing the rest to fill it all you can just skip the paragraphs\n\nNope. I don't think there is a citation. Also, can I do the dataset card later (maybe in bulk)?",
"We're doing one PR = one dataset to keep track of things. Feel free to add the tags later in this PR if you want to.\r\nAlso only the tags are required now, because we don't want people spending too much time on the cards",
"added @lhoestq ",
"Merging since the CI is fixed on master"
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"LGTM, don't forget the tags ;)"
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https://api.github.com/repos/huggingface/datasets/issues/939 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/939/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/939/comments | https://api.github.com/repos/huggingface/datasets/issues/939/events | https://github.com/huggingface/datasets/pull/939 | 753,965,405 | MDExOlB1bGxSZXF1ZXN0NTI5OTQwOTYz | 939 | add wisesight_sentiment | {
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"@lhoestq Thanks, Quentin. Removed the .ipynb_checkpoints and edited the README.md. The tests are failing because of other dataets. I'm figuring out why since the commits only have changes on `wisesight_sentiment`\r\n\r\n```\r\nFAILED tests/test_dataset_common.py::RemoteDatasetTest::test_builder_class_flue\r\nFAILED tests/test_dataset_common.py::RemoteDatasetTest::test_builder_class_norwegian_ner\r\nFAILED tests/test_dataset_common.py::RemoteDatasetTest::test_builder_configs_flue\r\nFAILED tests/test_dataset_common.py::RemoteDatasetTest::test_builder_configs_norwegian_ner\r\nFAILED tests/test_dataset_common.py::RemoteDatasetTest::test_load_dataset_flue\r\nFAILED tests/test_dataset_common.py::RemoteDatasetTest::test_load_dataset_norwegian_ner\r\nFAILED tests/test_dataset_common.py::RemoteDatasetTest::test_load_dataset_xglue\r\n```",
"@cstorm125 I really like the dataset and dataset card but there seems to have been a rebase issue at some point since it's now changing 140 files :D \r\n\r\nCould you rebase from master?",
"I think it might be faster to close and reopen.",
"To be continued on: https://github.com/huggingface/datasets/pull/981"
] | 1,606,791,999,000 | 1,606,884,758,000 | 1,606,883,751,000 | CONTRIBUTOR | null | Add `wisesight_sentiment` Social media messages in Thai language with sentiment label (positive, neutral, negative, question)
Model Card:
---
YAML tags:
annotations_creators:
- expert-generated
language_creators:
- found
languages:
- th
licenses:
- cc0-1.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
---
# Dataset Card for wisesight_sentiment
## 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://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
- We trying 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}
}
```
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https://api.github.com/repos/huggingface/datasets/issues/938 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/938/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/938/comments | https://api.github.com/repos/huggingface/datasets/issues/938/events | https://github.com/huggingface/datasets/pull/938 | 753,940,979 | MDExOlB1bGxSZXF1ZXN0NTI5OTIxNzU5 | 938 | V-1.0.0 of isizulu_ner_corpus | {
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"closing since it's been added in #957 "
] | 1,606,788,272,000 | 1,606,865,676,000 | 1,606,865,676,000 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/datasets/issues/937 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/937/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/937/comments | https://api.github.com/repos/huggingface/datasets/issues/937/events | https://github.com/huggingface/datasets/issues/937 | 753,921,078 | MDU6SXNzdWU3NTM5MjEwNzg= | 937 | Local machine/cluster Beam Datasets example/tutorial | {
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"I tried to make it run once on the SparkRunner but it seems that this runner has some issues when it is run locally.\r\nFrom my experience the DirectRunner is fine though, even if it's clearly not memory efficient.\r\n\r\nIt would be awesome though to make it work locally on a SparkRunner !\r\nDid you manage to make your processing work ?"
] | 1,606,785,103,000 | 1,608,731,696,000 | null | NONE | null | Hi,
I'm wondering if https://huggingface.co/docs/datasets/beam_dataset.html has an non-GCP or non-Dataflow version example/tutorial? I tried to migrate it to run on DirectRunner and SparkRunner, however, there were way too many runtime errors that I had to fix during the process, and even so I wasn't able to get either runner correctly producing the desired output.
Thanks!
Shang | {
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https://api.github.com/repos/huggingface/datasets/issues/935 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/935/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/935/comments | https://api.github.com/repos/huggingface/datasets/issues/935/events | https://github.com/huggingface/datasets/pull/935 | 753,863,055 | MDExOlB1bGxSZXF1ZXN0NTI5ODU5MjM4 | 935 | add PIB dataset | {
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"Hi, \r\n\r\nI am unable to get success in these tests. Can someone help me by pointing out possible errors?\r\n\r\nThanks",
"Hi ! you can read the tests by logging in to circleci.\r\n\r\nAnyway for information here are the errors : \r\n```\r\ndatasets/pib/pib.py:19:1: F401 'csv' imported but unused\r\ndatasets/pib/pib.py:20:1: F401 'json' imported but unused\r\ndatasets/pib/pib.py:36:84: W291 trailing whitespace\r\n```\r\nand \r\n```\r\nFAILED tests/test_file_encoding.py::TestFileEncoding::test_no_encoding_on_file_open\r\n```\r\n\r\nTo fix the `test_no_encoding_on_file_open` you just have to specify an encoding while opening a text file. For example `encoding=\"utf-8\"`\r\n",
"All suggested changes are done.",
"Nice ! can you re-generate the dataset_infos.json file to take into account the feature type change ?\r\n```\r\ndatasets-cli test ./datasets/pib --save_infos --all_configs --ignore_verifications\r\n```\r\nAnd also format your code ?\r\n```\r\nmake style\r\n```"
] | 1,606,776,943,000 | 1,606,864,631,000 | 1,606,864,631,000 | CONTRIBUTOR | null | This pull request will add PIB dataset. | {
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"cc @yjernite @lhoestq @thomwolf "
] | 1,606,776,550,000 | 1,606,798,582,000 | 1,606,778,040,000 | MEMBER | null | small updates (corrections/typos) to the "add new dataset" guide | {
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} | [] | null | [] | 1,606,775,793,000 | 1,606,854,350,000 | 1,606,852,316,000 | CONTRIBUTOR | null | Adds the NumerSense dataset
- Webpage/leaderboard: https://inklab.usc.edu/NumerSense/
- Paper: https://arxiv.org/abs/2005.00683
- Description: NumerSense is a new numerical commonsense reasoning probing task, with a diagnostic dataset consisting of 3,145 masked-word-prediction probes. Basically, it's a benchmark to see whether your MLM can figure out the right number in a fill-in-the-blank task based on commonsense knowledge (a bird has **two** legs) | {
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"This PR adds the #MeToo MA dataset. It presents multi-label data points for tweets mined in the backdrop of the #MeToo movement. The dataset includes data points in the form of Tweet ids and appropriate labels. Please refer to the accompanying paper for detailed information regarding annotation, collection, and guidelines. \r\n\r\nPaper: https://ojs.aaai.org/index.php/ICWSM/article/view/7292\r\nDataset Link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/JN4EYU\r\n\r\nYAML tags:\r\nannotations_creators:\r\n- expert-generated\r\nlanguage_creators:\r\n- found\r\nlanguages:\r\n- en\r\nmultilinguality:\r\n- monolingual\r\nsize_categories:\r\n- 1K<n<10K\r\nsource_datasets:\r\n- original\r\ntask_categories:\r\n- text-classification\r\n- text-retrieval\r\ntask_ids:\r\n- multi-class-classification\r\n- multi-label-classification\r\n\r\n# Dataset Card for #MeTooMA dataset\r\n\r\n## Table of Contents\r\n- [Dataset Description](#dataset-description)\r\n - [Dataset Summary](#dataset-summary)\r\n - [Supported Tasks](#supported-tasks-and-leaderboards)\r\n - [Languages](#languages)\r\n- [Dataset Structure](#dataset-structure)\r\n - [Data Instances](#data-instances)\r\n - [Data Fields](#data-instances)\r\n - [Data Splits](#data-instances)\r\n- [Dataset Creation](#dataset-creation)\r\n - [Curation Rationale](#curation-rationale)\r\n - [Source Data](#source-data)\r\n - [Annotations](#annotations)\r\n - [Personal and Sensitive Information](#personal-and-sensitive-information)\r\n- [Considerations for Using the Data](#considerations-for-using-the-data)\r\n - [Social Impact of Dataset](#social-impact-of-dataset)\r\n - [Discussion of Biases](#discussion-of-biases)\r\n - [Other Known Limitations](#other-known-limitations)\r\n- [Additional Information](#additional-information)\r\n - [Dataset Curators](#dataset-curators)\r\n - [Licensing Information](#licensing-information)\r\n - [Citation Information](#citation-information)\r\n\r\n## Dataset Description\r\n\r\n- **Homepage:** https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/JN4EYU\r\n- **Paper:** https://ojs.aaai.org//index.php/ICWSM/article/view/7292\r\n- **Point of Contact:** https://github.com/midas-research/MeTooMA\r\n\r\n\r\n### Dataset Summary\r\n\r\n- The dataset consists of tweets belonging to #MeToo movement on Twitter, labelled into different categories.\r\n- This dataset includes more data points and has more labels than any of the previous datasets in that contain social media\r\nposts about sexual abuse discloures. Please refer to the Related Datasets of the publication for a detailed information about this.\r\n- Due to Twitters development policies, the authors provide only the tweet IDs and corresponding labels,\r\nother data can be fetched via Twitter API.\r\n- The data has been labelled by experts, with the majority taken into the account for deciding the final label.\r\n- The authors provide these labels for each of the tweets.\r\n - Relevance\r\n - Directed Hate\r\n - Generalized Hate\r\n - Sarcasm\r\n - Allegation\r\n - Justification\r\n - Refutation\r\n - Support\r\n - Oppose\r\n- The definitions for each task/label is in the main publication.\r\n- Please refer to the accompanying paper https://aaai.org/ojs/index.php/ICWSM/article/view/7292 for statistical analysis on the textual data\r\nextracted from this dataset.\r\n- The language of all the tweets in this dataset is English\r\n- Time period: October 2018 - December 2018\r\n- Suggested Use Cases of this dataset:\r\n - Evaluating usage of linguistic acts such as: hate-spech and sarcasm in the incontext of public sexual abuse discloures.\r\n - Extracting actionable insights and virtual dynamics of gender roles in sexual abuse revelations.\r\n - Identifying how influential people were potrayed on public platform in the\r\n events of mass social movements.\r\n - Polarization analysis based on graph simulations of social nodes of users involved\r\n in the #MeToo movement.\r\n\r\n\r\n### Supported Tasks and Leaderboards\r\n\r\nMulti Label and Multi-Class Classification\r\n\r\n### Languages\r\n\r\nEnglish\r\n\r\n## Dataset Structure\r\n- The dataset is structured into CSV format with TweetID and accompanying labels.\r\n- Train and Test sets are split into respective files.\r\n\r\n### Data Instances\r\n\r\nTweet ID and the appropriatelabels\r\n\r\n### Data Fields\r\n\r\nTweet ID and appropriate labels (binary label applicable for a data point) and multiple labels for each Tweet ID\r\n\r\n### Data Splits\r\n\r\n- Train: 7979\r\n- Test: 1996\r\n\r\n## Dataset Creation\r\n\r\n### Curation Rationale\r\n\r\n- Twitter was the major source of all the public discloures of sexual abuse incidents during the #MeToo movement.\r\n- People expressed their opinions over issues which were previously missing from the social media space.\r\n- This provides an option to study the linguistic behaviours of social media users in an informal setting,\r\ntherefore the authors decide to curate this annotated dataset.\r\n- The authors expect this dataset would be of great interest and use to both computational and socio-linguists.\r\n- For computational linguists, it provides an opportunity to model three new complex dialogue acts (allegation, refutation, and justification) and also to study how these acts interact with some of the other linguistic components like stance, hate, and sarcasm. For socio-linguists, it provides an opportunity to explore how a movement manifests in social media.\r\n\r\n\r\n### Source Data\r\n- Source of all the data points in this dataset is Twitter.\r\n\r\n#### Initial Data Collection and Normalization\r\n\r\n- All the tweets are mined from Twitter with initial search paramters identified using keywords from the #MeToo movement.\r\n- Redundant keywords were removed based on manual inspection.\r\n- Public streaming APIs of Twitter were used for querying with the selected keywords.\r\n- Based on text de-duplication and cosine similarity score, the set of tweets were pruned.\r\n- Non english tweets were removed.\r\n- The final set was labelled by experts with the majority label taken into the account for deciding the final label.\r\n- Please refer to this paper for detailed information: https://ojs.aaai.org//index.php/ICWSM/article/view/7292\r\n\r\n#### Who are the source language producers?\r\n\r\nPlease refer to this paper for detailed information: https://ojs.aaai.org//index.php/ICWSM/article/view/7292\r\n\r\n### Annotations\r\n\r\n#### Annotation process\r\n\r\n- The authors chose against crowd sourcing for labeling this dataset due to its highly sensitive nature.\r\n- The annotators are domain experts having degress in advanced clinical psychology and gender studies.\r\n- They were provided a guidelines document with instructions about each task and its definitions, labels and examples.\r\n- They studied the document, worked a few examples to get used to this annotation task.\r\n- They also provided feedback for improving the class definitions.\r\n- The annotation process is not mutually exclusive, implying that presence of one label does not mean the\r\nabsence of the other one.\r\n\r\n\r\n#### Who are the annotators?\r\n\r\n- The annotators are domain experts having a degree in clinical psychology and gender studies.\r\n- Please refer to the accompnaying paper for a detailed annotation process.\r\n\r\n### Personal and Sensitive Information\r\n\r\n- Considering Twitters policy for distribution of data, only Tweet ID and applicable labels are shared for the public use.\r\n- It is highly encouraged to use this dataset for scientific purposes only.\r\n- This dataset collection completely follows the Twitter mandated guidelines for distribution and usage.\r\n\r\n## Considerations for Using the Data\r\n\r\n### Social Impact of Dataset\r\n\r\n- The authors of this dataset do not intend to conduct a population centric analysis of #MeToo movement on Twitter.\r\n- The authors acknowledge that findings from this dataset cannot be used as-is for any direct social intervention, these\r\nshould be used to assist already existing human intervention tools and therapies.\r\n- Enough care has been taken to ensure that this work comes of as trying to target a specific person for their\r\npersonal stance of issues pertaining to the #MeToo movement.\r\n- The authors of this work do not aim to vilify anyone accused in the #MeToo movement in any manner.\r\n- Please refer to the ethics and discussion section of the mentioned publication for appropriate sharing of this dataset\r\nand social impact of this work.\r\n\r\n\r\n### Discussion of Biases\r\n\r\n- The #MeToo movement acted as a catalyst for implementing social policy changes to benefit the members of\r\ncommunity affected by sexual abuse.\r\n- Any work undertaken on this dataset should aim to minimize the bias against minority groups which\r\nmight amplified in cases of sudden outburst of public reactions over sensitive social media discussions.\r\n\r\n### Other Known Limitations\r\n\r\n- Considering privacy concerns, social media practitioners should be aware of making automated interventions\r\nto aid the victims of sexual abuse as some people might not prefer to disclose their notions.\r\n- Concerned social media users might also repeal their social information, if they found out that their\r\ninformation is being used for computational purposes, hence it is important seek subtle individual consent\r\nbefore trying to profile authors involved in online discussions to uphold personal privacy.\r\n\r\n## Additional Information\r\n\r\nPlease refer to this link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/JN4EYU\r\n\r\n### Dataset Curators\r\n\r\n- If you use the corpus in a product or application, then please credit the authors\r\nand [Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi]\r\n(http://midas.iiitd.edu.in) appropriately.\r\nAlso, if you send us an email, we will be thrilled to know about how you have used the corpus.\r\n- If interested in commercial use of the corpus, send email to midas@iiitd.ac.in.\r\n- Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi, India\r\ndisclaims any responsibility for the use of the corpus and does not provide technical support.\r\nHowever, the contact listed above will be happy to respond to queries and clarifications\r\n- Please feel free to send us an email:\r\n - with feedback regarding the corpus.\r\n - with information on how you have used the corpus.\r\n - if interested in having us analyze your social media data.\r\n - if interested in a collaborative research project.\r\n\r\n### Licensing Information\r\n\r\n[More Information Needed]\r\n\r\n### Citation Information\r\n\r\nPlease cite the following publication if you make use of the dataset: https://ojs.aaai.org/index.php/ICWSM/article/view/7292\r\n\r\n```\r\n\r\n@article{Gautam_Mathur_Gosangi_Mahata_Sawhney_Shah_2020, title={#MeTooMA: Multi-Aspect Annotations of Tweets Related to the MeToo Movement}, volume={14}, url={https://aaai.org/ojs/index.php/ICWSM/article/view/7292}, abstractNote={<p>In this paper, we present a dataset containing 9,973 tweets related to the MeToo movement that were manually annotated for five different linguistic aspects: relevance, stance, hate speech, sarcasm, and dialogue acts. We present a detailed account of the data collection and annotation processes. The annotations have a very high inter-annotator agreement (0.79 to 0.93 k-alpha) due to the domain expertise of the annotators and clear annotation instructions. We analyze the data in terms of geographical distribution, label correlations, and keywords. Lastly, we present some potential use cases of this dataset. We expect this dataset would be of great interest to psycholinguists, socio-linguists, and computational linguists to study the discursive space of digitally mobilized social movements on sensitive issues like sexual harassment.</p&gt;}, number={1}, journal={Proceedings of the International AAAI Conference on Web and Social Media}, author={Gautam, Akash and Mathur, Puneet and Gosangi, Rakesh and Mahata, Debanjan and Sawhney, Ramit and Shah, Rajiv Ratn}, year={2020}, month={May}, pages={209-216} }\r\n\r\n```\r\n\r\n\r\n\r\n",
"Hi, @lhoestq I have resolved all the comments you have raised. Can you review the PR again? However, I do need assistance on how to remove other files that came along in my PR. Should I manually delete unwanted files from the PR raised?",
"I am closing this PR, @lhoestq please review this PR instead https://github.com/huggingface/datasets/pull/975 where I have removed the unwanted files of other datasets and addressed each of your points. "
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} | [] | null | [] | 1,606,771,821,000 | 1,606,771,821,000 | null | MEMBER | null | Have a string `zipfile.BadZipFile: Bad CRC-32 for file 'web_snippets_train.json'` error when downloading the largest file from dropbox: `https://www.dropbox.com/sh/7pkwkrfnwqhsnpo/AABVENv_Q9rFtnM61liyzO0La/web_snippets_train.json.zip?dl=1`
Didn't managed to see how to solve that.
Putting aside for now.
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https://api.github.com/repos/huggingface/datasets/issues/930 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/930/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/930/comments | https://api.github.com/repos/huggingface/datasets/issues/930/events | https://github.com/huggingface/datasets/pull/930 | 753,801,204 | MDExOlB1bGxSZXF1ZXN0NTI5ODA5MzM1 | 930 | Lambada | {
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} | [] | null | [] | 1,606,770,153,000 | 1,606,783,032,000 | 1,606,783,031,000 | MEMBER | null | Added LAMBADA dataset.
A couple of points of attention (mostly because I am not sure)
- The training data are compressed in a .tar file inside the main tar.gz file. I had to manually un-tar the training file to access the examples.
- The dev and test splits don't have the `category` field so I put `None` by default.
Happy to make changes if it doesn't respect the guidelines!
Victor | {
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} | [] | null | [] | 1,606,762,686,000 | 1,606,838,670,000 | 1,606,838,667,000 | CONTRIBUTOR | null | - **Name:** Multilingual Amazon Reviews Corpus* (`amazon_reviews_multi`)
- **Description:** A collection of Amazon reviews in English, Japanese, German, French, Spanish and Chinese.
- **Paper:** https://arxiv.org/abs/2010.02573
### Checkbox
- [x] Create the dataset script `/datasets/my_dataset/my_dataset.py` using the template
- [x] Fill the `_DESCRIPTION` and `_CITATION` variables
- [x] Implement `_infos()`, `_split_generators()` and `_generate_examples()`
- [x] Make sure that the `BUILDER_CONFIGS` class attribute is filled with the different configurations of the dataset and that the `BUILDER_CONFIG_CLASS` is specified if there is a custom config class.
- [x] Generate the metadata file `dataset_infos.json` for all configurations
- [x] Generate the dummy data `dummy_data.zip` files to have the dataset script tested and that they don't weigh too much (<50KB)
- [x] Add the dataset card `README.md` using the template : fill the tags and the various paragraphs
- [x] Both tests for the real data and the dummy data pass. | {
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https://api.github.com/repos/huggingface/datasets/issues/926 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/926/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/926/comments | https://api.github.com/repos/huggingface/datasets/issues/926/events | https://github.com/huggingface/datasets/pull/926 | 753,676,069 | MDExOlB1bGxSZXF1ZXN0NTI5NzA4MTcy | 926 | add inquisitive | {
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"`dummy_data` right now contains all article files, keeping only the required articles for dummy data fails the dummy data test.\r\nAny idea ?",
"> `dummy_data` right now contains all article files, keeping only the required articles for dummy data fails the dummy data test.\r\n> Any idea ?\r\n\r\nWe should definitely find a way to make it work with only a few articles.\r\n\r\nIf it doesn't work right now for dummy data, I guess it's because it tries to load every single article file ?\r\n\r\nIf so, then maybe you can use `os.listdir` method to first check all the data files available in the path where the `articles.tgz` file is extracted. Then you can simply iter through the data files and depending on their ID, include them in the train or test set. With this method you should be able to have only a few articles files per split in the dummy data. Does that make sense ?",
"fixed! so the issue was, `articles_ids` were prepared based on the number of files in articles dir, so for dummy data questions it was not able to load some articles due to incorrect ids and the test was failing"
] | 1,606,758,322,000 | 1,606,916,722,000 | 1,606,916,413,000 | MEMBER | null | Adding inquisitive qg dataset
More info: https://github.com/wjko2/INQUISITIVE | {
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