Datasets:

abhishek-shrm

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github-issues

like 0

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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 " ]

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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" ]

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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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[ "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!" ]

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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" ]

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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." ]

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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" ]

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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" ]

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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!" ]

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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 :) " ]

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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" ]

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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" ]

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[ "Merging this one, good job and thank you @jplu :) " ]

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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 👋 " ]

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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" ]

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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" ]

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[ "Wrongly branching from existing branch of wisesight_sentiment. Closing and opening another one specifically for prachathai67k" ]

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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." ]

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[ "Replaced by #1126" ]

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[ "thanks ! merging this one" ]

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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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[ "@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" ]

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[ "closing since it's been added in #957 " ]

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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 ?" ]

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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```" ]

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[ "cc @yjernite @lhoestq @thomwolf " ]

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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 [email protected].\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={&lt;p&gt;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.&lt;/p&#38;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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[ "`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" ]

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[ "> Did you generate the dummy data with the cli or manually ?\r\n\r\nIt was generated by the cli. Do you want me to make it smaller keep it like this?\r\n\r\n" ]

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[ "LGTM!" ]

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[ "Hello @lhoestq, I would like just to ask you if it is OK that I include this feature 9f32ba1 in this PR or you would prefer to have it in a separate one.\r\n\r\nI was wondering whether include also a test, but I did not find any test for the other file formats...", "Hi ! Sure that would be valuable to support .xz files. Feel free to open a separate PR for this.\r\nAnd feel free to create the first test case for extracting compressed files if you have some inspiration (maybe create test_file_utils.py ?). We can still spend more time on tests next week when the sprint is over though so don't spend too much time on it.", "@lhoestq, DONE! ;) See PR #950.", "Thanks for adding support for `.xz` files :)\r\n\r\nFeel free to rebase from master to include it in your PR", "@lhoestq DONE; I have merged instead, to avoid changing the history of my public PR ;)", "Hi @lhoestq, I would need that you generate the dataset_infos.json and the dummy data for this dataset with a bigger computer. Sorry, but my laptop did not succeed...", "Thanks for your work @albertvillanova \r\nWe'll definitely look into it after this sprint :)", "Looks like #1456 added CC100 already.\r\nThe difference with your approach is that this implementation uses the `BuilderConfig` parameters to allow the creation of custom configs for all the languages, without having to specify them in the `BUILDER_CONFIGS` class attribute.\r\nFor example even if the dataset doesn't have a config for english already, you can still load the english CC100 with\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nload_dataset(\"cc100\", lang=\"en\")\r\n```", "@lhoestq, oops!! I remember having assigned this dataset to me in the Google sheet, besides having mentioned the corresponding issue in the Pull Request... Nevermind! :)", "Yes indeed I can see that...\r\nSorry for noticing that only now \r\n\r\nThe code of the other PR ended up being pretty close to yours though\r\nIf you want to add more details to the cc100 dataset card or in the script feel to do so, any addition is welcome" ]

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[ "Hi @sumanthd17 \r\n\r\nLooks like a good start! You will also need to add a Dataset card, following the instructions given [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#manually-tag-the-dataset-and-write-the-dataset-card)", "I followed the instructions mentioned there but my dataset isn't showing up in the dropdown list. Am I missing something here? @yjernite ", "> I followed the instructions mentioned there but my dataset isn't showing up in the dropdown list. Am I missing something here? @yjernite\r\n\r\nThe best way is to run the tagging app locally and provide it the location to the `dataset_infos.json` after you've run the CLI:\r\nhttps://github.com/huggingface/datasets-tagging\r\n", "This is a really good data card!!\r\n\r\nSmall changes to make it even better:\r\n- Tags: the dataset has both \"original\" data and data that is \"extended\" from a source dataset: TydiQA - you should choose both options in the tagging apps\r\n- The language and annotation creator tags are off: the language here is the questions: I understand it's a mix of crowd-sourced and expert-generated? Is there any machine translation involved? The annotations are the span selections: is that crowd-sourced?\r\n- Personal and sensitive information: there should be a statement there, even if only to say that none could be found or that it only mentions public figures" ]

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[ "> Awesome good job !\r\n> \r\n> Could you also add a dataset card using the template guide here : https://github.com/huggingface/datasets/blob/master/templates/README_guide.md\r\n> If you can't fill some fields then just leave `[N/A]`\r\n\r\nQuick amendment: `[N/A]` is for fields that are not relevant: if you can't find the information just leave `[More Information Needed]`", "@lhoestq since datset cards are optional for this sprint I'll add those later. Good for merge.", "Indeed we only require the tags to be added now (the yaml part at the top of the dataset card).\r\nCould you add them please ?\r\nYou can find more infos here : https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#tag-the-dataset-and-write-the-dataset-card", "@lhoestq added tags, I'll fill rest of the info after current sprint :)", "The tests are failing tests for other datasets, not this one.", "@lhoestq could you tell me why these tests are failing, they don't seem related to this PR. " ]

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[ "Also, I cannot first convert it to torch format, since huggingface seq2seq_trainer codes process the datasets afterwards during datacollector function to make it optimize for TPUs. ", "sorry I misunderstood length of dataset with dataloader, closed. thanks " ]

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[ "Testing command doesn't work\r\n###trace\r\n-- Docs: https://docs.pytest.org/en/stable/warnings.html\r\n========================================================= short test summary info ========================================================== \r\nERROR tests/test_dataset_common.py - absl.testing.parameterized.NoTestsError: parameterized test decorators did not generate any tests. Ma...\r\n====================================================== 2 warnings, 1 error in 54.23s ======================================================= \r\nERROR: not found: G:\\Work Related\\hf\\datasets\\tests\\test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_concode\r\n(no name 'G:\\\\Work Related\\\\hf\\\\datasets\\\\tests\\\\test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_concode' in any of [<Module test_dataset_common.py>])\r\n", "Hello @lhoestq Test checks are passing in my local, but the commit fails in ci. Any idea onto why? \r\n#### Dummy Dataset Test \r\n====================================================== 1 passed, 6 warnings in 7.14s ======================================================= \r\n#### Real Dataset Test \r\n====================================================== 1 passed, 6 warnings in 25.54s ====================================================== ", "Hello @lhoestq, Have a look, I've changed the file according to the reviews. Thanks!", "@reshinthadithyan that's a great start! You will also need to add a Dataset card, following the instructions given [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#manually-tag-the-dataset-and-write-the-dataset-card)", "> @reshinthadithyan that's a great start! You will also need to add a Dataset card, following the instructions given [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#manually-tag-the-dataset-and-write-the-dataset-card)\r\n\r\nHello @yjernite I'm facing issues in using the datasets-tagger Refer #1 in datasets-tagger. Thanks", "> > @reshinthadithyan that's a great start! You will also need to add a Dataset card, following the instructions given [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#manually-tag-the-dataset-and-write-the-dataset-card)\r\n> \r\n> Hello @yjernite I'm facing issues in using the datasets-tagger Refer #1 in datasets-tagger. Thanks\r\n\r\nHi @reshinthadithyan ! Did you try with the latest version of the tagger? What issues are you facing?\r\n\r\nWe're also relaxed the dataset requirement for now, you'll only add to add the tags :) ", "Could you work on another branch when adding different datasets ?\r\nThe idea is to have one PR per dataset", "Thanks ! The github diff looks all clean now :) \r\nTo fix the CI you just need to rebase from master\r\n\r\nDon't forget to add the tags of the dataset card. It's the yaml part at the top of the dataset card\r\nMore infor here : https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#tag-the-dataset-and-write-the-dataset-card\r\n\r\nThe issue you had with the tagger should be fixed now by https://github.com/huggingface/datasets-tagging/pull/5\r\n" ]

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[ "Yes the use of configs is optional", "@abhishekkrthakur we want to keep track of the information that is and isn't in the dataset cards so we're asking everyone to use the full template :) If there is some information in there that you really can't find or don't feel qualified to add, you can just leave the `[More Information Needed]` text" ]

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[ "This is an interesting idea !\r\nDo you have ideas about how to approach the decoding and the normalization ?", "@lhoestq\r\nI think we first need to save the transformation chain to a list in `self._fingerprint`. Then we can\r\n- decode all the current saved datasets to see if there is already one that is equivalent to the transformation we need now.\r\n- or, calculate all the possible hash value of the current chain for comparison so that we could continue to use hashing.\r\nIf we find one, we can adjust the list in `self._fingerprint` to it.\r\n\r\nAs for the transformation reordering rules, we can just start with some manual rules, like two sort on the same column should merge to one, filter and select can change orders.\r\n\r\nAnd for encoding and decoding, we can just manually specify `sort` is 0, `shuffling` is 2 and create a base-n number or use some general algorithm like `base64.urlsafe_b64encode`.\r\n\r\nBecause we are not doing lazy evaluation now, we may not be able to normalize the transformation to its minimal form. If we want to support that, we can provde a `Sequential` api and let user input a list or transformation, so that user would not use the intermediate datasets. This would look like tf.data.Dataset." ]

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[ "We can look into removing some of the attributes from `GET /api/datasets` to make it smaller/faster, what do you think @lhoestq?", "> We can look into removing some of the attributes from `GET /api/datasets` to make it smaller/faster, what do you think @lhoestq?\r\n\r\nyes at least remove all the `dummy_data.zip`", "`GET /api/datasets` should now be much faster. @zhuzilin can you check if `list_datasets` is now faster for you?", "> `GET /api/datasets` should now be much faster. @zhuzilin can you check if `list_datasets` is now faster for you?\r\n\r\nYes, much faster! Thank you!" ]

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[ "How to resolve these failed checks?", "Thanks for adding this one :) \r\n\r\nTo fix the check_code_quality, please run `make style` with the latest version of black, isort, flake8\r\nTo fix the test_no_encoding_on_file_open, make sure to specify the encoding each time you call `open()` on a text file.\r\nFor example : `encoding=\"utf-8\"`\r\nTo fix the test_load_dataset_pec , you must add the dummy_data.zip file. It is used to test the dataset script and make sure it runs fine. To add it, please refer to the steps in https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-add-a-dataset\r\n\r\n", "Could you also add a dataset card ? you can find a template here : https://github.com/huggingface/datasets/blob/master/templates/README.md\r\n\r\nThat would be awesome", "> Thanks for adding this one :)\r\n> \r\n> To fix the check_code_quality, please run `make style` with the latest version of black, isort, flake8\r\n> To fix the test_no_encoding_on_file_open, make sure to specify the encoding each time you call `open()` on a text file.\r\n> For example : `encoding=\"utf-8\"`\r\n> To fix the test_load_dataset_pec , you must add the dummy_data.zip file. It is used to test the dataset script and make sure it runs fine. To add it, please refer to the steps in https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-add-a-dataset\r\n\r\nThank you for the detailed suggestion.\r\n\r\nI have added dummy_data but it still failed the DistributedDatasetTest check. My dataset has a central file (containing a python dict) that needs to be accessed by each data example. Is it because the central file cannot be distributed (which would lead to a partial dictionary)?\r\n\r\nSpecifically, the central file contains a dictionary of speakers with their attributes. Each data example is also associated with a speaker. As of now, I keep the central file and data files separately. If I remove the central file by appending the speaker attributes to each data example, then there would be lots of redundancy because there are lots of duplicate speakers in the data files.", "The `DistributedDatasetTest` fail and the changes of this PR are not related, there was just a bug in the CI. You can ignore it", "> Really cool thanks !\r\n> \r\n> Could you make the dummy files smaller ? For example by reducing the size of persona.txt ?\r\n> I also left a comment about the files concatenation. It would be cool to replace that with simple iterations through the different files.\r\n> \r\n> Then once this is done, you can add a dataset card using the template guide here : https://github.com/huggingface/datasets/blob/master/templates/README_guide.md\r\n> If some fields can't be filled, just leave `[N/A]`\r\n\r\nSmall change: if you don't have the information for a field, please leave `[More Information Needed]` rather than `[N/A]`\r\n\r\nThe full information can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#manually-tag-the-dataset-and-write-the-dataset-card)" ]

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[ "nvm, I'd made an assumption that the library gets installed with transformers. " ]