Datasets:

---

shzhang

/

tutorial_datasets_github_issues

like 0

[]

[]

[ "@lhoestq added. " ]

[]

[]

[]

[]

[]

[]

[]

[]

[ "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." ]

[]

[]

[ "Replaced by #1126" ]

[]

[]

[ "thanks ! merging this one" ]

[]

[]

[]

[]

[]

[]

[]

[]

[ "> 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" ]

[]

[]

[ "LGTM, don't forget the tags ;)" ]

[]

[]

[ "@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" ]

[]

[]

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

[]

[]

[ "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 ?" ]

[]

[]

[]

[]

[]

[ "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```" ]

[]

[]

[ "cc @yjernite @lhoestq @thomwolf " ]

[]

[]

[]

[]

[]

[ "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. " ]

[]

[]

[]

[]

[]

[]

[]

[]

[]

[]

[]

[]

[]

[]

[]

[]

[]

[ "`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" ]

[]

[]

[ "> 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" ]

[]

[]

[ "LGTM!" ]

[ { "id": 1935892913, "node_id": "MDU6TGFiZWwxOTM1ODkyOTEz", "url": "https://api.github.com/repos/huggingface/datasets/labels/wontfix", "name": "wontfix", "color": "ffffff", "default": true, "description": "This will not be worked on" } ]

[]

[ "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" ]

[]

[]

[ "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" ]

[]

[]

[ "> 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. " ]

[]

[]

[ "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 " ]

[]

[]

[]

[]

[]

[ "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" ]

[]

[]

[ "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" ]

[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" }, { "id": 2067400324, "node_id": "MDU6TGFiZWwyMDY3NDAwMzI0", "url": "https://api.github.com/repos/huggingface/datasets/labels/generic%20discussion", "name": "generic discussion", "color": "c5def5", "default": false, "description": "Generic discussion on the library" } ]

[]

[ "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." ]

[]

[]

[ "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!" ]

[]

[]

[ "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)" ]

[]

[]

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

[]

[]

[]

[]

[]

[ "> That's really cool thank you !\r\n> \r\n> Could you also add a dataset card ?\r\n> You can find a template here : https://github.com/huggingface/datasets/blob/master/templates/README.md\r\n\r\nThe full information for adding a dataset card can be found here :) \r\nhttps://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#manually-tag-the-dataset-and-write-the-dataset-card\r\n", "Thanks your suggestions! I've fixed them, and currently working on the dataset card!", "@yjernite and @lhoestq I will add the dataset card a bit later in a separate PR if that's ok for you!", "Yes I want to re-emphasize if it was not clear that dataset cards are optional for the sprint. \r\n\r\nOnly the tags are required for merging a datasets.\r\n\r\nPlease try to enforce this rule as well @lhoestq and @yjernite ", "Yes @stefan-it if you could just add the tags (the yaml part at the top of the dataset card) that'd be perfect :) ", "Oh, sorry, will add them now!\r\n", "Initial README file is now added :) ", "the `RemoteDatasetTest ` errors in the CI are fixed on master so it's fine", "merging since the CI is fixed on master" ]

[]

[]

[ "Sorry, I have just seen that it was already in `QUALITY_REQUIRE`.\r\n\r\nFor some reason it did not get installed on my virtual environment..." ]

[]

[]

[]

[]

[]

[]

[]

[]

[ "Looks like the test doesn't detect all the problems fixed by #907 , I'll fix that", "Ok found why it doesn't detect the problems fixed by #907 . That's because for all those datasets the urls are actually fine (no backslash) on windows, even if it uses `os.path.join`.\r\n\r\nThis is because of the behavior of `os.path.join` on windows when the first path ends with a slash : \r\n\r\n```python\r\nimport os\r\nos.path.join(\"https://test.com/foo\", \"bar.txt\")\r\n# 'https://test.com/foo\\\\bar.txt'\r\nos.path.join(\"https://test.com/foo/\", \"bar.txt\")\r\n# 'https://test.com/foo/bar.txt'\r\n```\r\n\r\nHowever even though the urls are correct, this is definitely bad practice and we should never use `os.path.join` for urls" ]

[]

[]

[ "Awesome! Thanks @lhoestq " ]

[]

[]

[ "@lhoestq I was indeed working on that... to make another commit on this feature branch...", "But as you prefer... nevermind! :)", "Ah what do you have in mind for the tests ? I was thinking of adding a check in the MockDownloadManager used for tests based on dummy data. I'm creating a PR right now, I'd be happy to have your opinion", "Indeed I was thinking of something similar: monckeypatching the HTTP request...", "Therefore, if you agree, I am removing all the rest of `os.path.join`, both from the code and the docs...", "If you spot other `os.path.join` for urls in dataset scripts or metrics scripts feel free to fix them.\r\nIn the library itself (/src/datasets) it should be fine since there are tests and a windows CI, but if you have doubts of some usage of `os.path.join` somewhere, let me know.", "Alright create the test in #905 .\r\nThe windows CI is failing for all the datasets that have bad usage of `os.path.join` for urls.\r\nThere are of course the ones you fixed in this PR (thanks again !) but I found others as well such as pg19 and blimp.\r\nYou can check the full list by looking at the CI failures of the commit 1ce3354", "I am merging this one as well as #906 that should fix all of the datasets.\r\nThen I'll rebase #905 which adds the test that checks for bad urls and make sure it' all green now" ]

[]

[]

[]

[]

[]

[ "Hello, thanks. I had a talk with the dataset authors, found out that the data now is obsolete and they'll get a stable version soon. So temporality closing the PR.\r\n Although I have a question, What should _id_ be in the return statement? Should that be something like a start index (or) the type of split will do? Thanks. ", "@reshinthadithyan we should hold off on this for a couple of weeks till NeurIPS concludes. The [NLC2CMD](http://nlc2cmd.us-east.mybluemix.net/) data will be out then; which includes a cleaner version of this NL2Bash data. The older data is sort of obsolete now. ", "Ah nvm you already commented 😆 " ]

[]

[ { "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false } ]

[ "Thanks for reporting ! I'm looking into it." ]

[]

[]

[]

[]

[]

[ "This dataset seems to have around 1000 configs. Therefore when creating the dummy data we end up with hundreds of MB of dummy data which we don't want to add in the repo.\r\nLet's make this PR on hold for now and find a solution after the sprint of next week", "Closing in favor of #1566 " ]

[ { "id": 2107841032, "node_id": "MDU6TGFiZWwyMTA3ODQxMDMy", "url": "https://api.github.com/repos/huggingface/datasets/labels/nlp-viewer", "name": "nlp-viewer", "color": "94203D", "default": false, "description": "" } ]

[]

[ "Thanks for reporting !\r\ncc @srush for the empty feature list issue and the encoding issue\r\ncc @julien-c maybe we can update the url and just have a redirection from the old url to the new one ?", "Ok, I redirected on our side to a new url. ⚠️ @srush: if you update the Streamlit config too to `/datasets/viewer`, let me know because I'll need to change our nginx config at the same time", "9", "‏⠀‏‏‏⠀‏‏‏⠀ ‏⠀ ", "‏⠀‏‏‏⠀‏‏‏⠀ ‏⠀ " ]

[]

[]

[]

[]

[]

[]

[]

[]

[ "Closing since we don't need to update the tags of those three datasets (for each one of them there is only one tag set)" ]

[]

[]

[ "@lhoestq removed prints and added the dataset card. ", "@lhoestq, I want to add other datasets as well. I am not sure if it is possible to do so with the same branch. ", "Hi @zaidalyafeai, really excited to get more Arabic coverage in the lib, thanks for your contribution!\r\n\r\nCouple of last comments:\r\n- this PR seems to modify some files that are unrelated to your dataset. Could you rebase from master? It should take care of that.\r\n- The dataset card is a good start! Can you describe the task in a few words and add more information in the Data Structure part, including listing and describing the fields? Also, if you don't know how to fill out a paragraph, or if you have some information but think more would be beneficial, please leave `[More Information Needed]` instead of `[N/A]`", "> Hi @zaidalyafeai, really excited to get more Arabic coverage in the lib, thanks for your contribution!\r\n> \r\n> Couple of last comments:\r\n> \r\n> * this PR seems to modify some files that are unrelated to your dataset. Could you rebase from master? It should take care of that.\r\n> * The dataset card is a good start! Can you describe the task in a few words and add more information in the Data Structure part, including listing and describing the fields? Also, if you don't know how to fill out a paragraph, or if you have some information but think more would be beneficial, please leave `[More Information Needed]` instead of `[N/A]`\r\n\r\nI have no idea how some other files changed. I tried to rebase and push but this created some errors. I had to run the command \r\n`git push -u --force origin add-metrec-dataset` which might cause some problems. ", "Feel free to create another branch/another PR without all the other changes", "@yjernite can you explain which other files are changed because of the PR ? https://github.com/huggingface/datasets/pull/893/files only shows files related to the dataset. ", "Right ! github is nice with us today :)", "Looks like this one is ready to merge, thanks @zaidalyafeai !", "@lhoestq thanks for the merge. I am not a GitHub geek. I already have another dataset to add. I'm not sure how to add another given my forked repo. Do I follow the same steps with a different checkout name ?", "If you've followed the instructions in here : https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#start-by-preparing-your-environment\r\n\r\n(especially point 2. and the command `git remote add upstream ....`)\r\n\r\nThen you can try\r\n```\r\ngit checkout master\r\ngit fetch upstream\r\ngit rebase upstream/master\r\ngit checkout -b add-<my-new-dataset-name>\r\n```" ]

[]

[]

[ "Looks good to me. Do we also support TSV in this helper (explain if it should be text or CSV) and in the dummy-data creator?", "snli is basically based on tsv files (but named as .txt) and it is in the list of datasets of reference.\r\nThe dummy data creator supports tsv", "merging this one.\r\nIf you think of other datasets of reference to add we can still add them later" ]

[]

[]

[]

[]

[]

[ "Thanks for the comments. I addressed them and pushed again.\r\nWhen I run \"make quality\" I get the following error but I don't know how to resolve it or what the problem ist respectively:\r\nwould reformat /Users/joelniklaus/NextCloud/PhDJoelNiklaus/Code/datasets/datasets/ler/ler.py\r\nOh no! 💥 💔 💥\r\n1 file would be reformatted, 257 files would be left unchanged.\r\nmake: *** [quality] Error 1\r\n", "Awesome thanks :)\r\nTo automatically format the python files you can run `make style`", "I did that now. But still getting the following error:\r\nblack --check --line-length 119 --target-version py36 tests src benchmarks datasets metrics\r\nAll done! ✨ 🍰 ✨\r\n258 files would be left unchanged.\r\nisort --check-only tests src benchmarks datasets metrics\r\nflake8 tests src benchmarks datasets metrics\r\ndatasets/ler/ler.py:46:96: W291 trailing whitespace\r\ndatasets/ler/ler.py:47:68: W291 trailing whitespace\r\ndatasets/ler/ler.py:48:102: W291 trailing whitespace\r\ndatasets/ler/ler.py:49:112: W291 trailing whitespace\r\ndatasets/ler/ler.py:50:92: W291 trailing whitespace\r\ndatasets/ler/ler.py:51:116: W291 trailing whitespace\r\ndatasets/ler/ler.py:52:84: W291 trailing whitespace\r\nmake: *** [quality] Error 1\r\n\r\nHowever: When I look at the file I don't see any trailing whitespace", "maybe a bug with flake8 ? could you try to update it ? which version do you have ?", "This is my flake8 version: 3.7.9 (mccabe: 0.6.1, pycodestyle: 2.5.0, pyflakes: 2.1.1) CPython 3.8.5 on Darwin\r\n", "Now I updated to: 3.8.4 (mccabe: 0.6.1, pycodestyle: 2.6.0, pyflakes: 2.2.0) CPython 3.8.5 on Darwin\r\n\r\nAnd now I even get additional errors:\r\nblack --check --line-length 119 --target-version py36 tests src benchmarks datasets metrics\r\nAll done! ✨ 🍰 ✨\r\n258 files would be left unchanged.\r\nisort --check-only tests src benchmarks datasets metrics\r\nflake8 tests src benchmarks datasets metrics\r\ndatasets/polyglot_ner/polyglot_ner.py:123:64: F541 f-string is missing placeholders\r\ndatasets/ler/ler.py:46:96: W291 trailing whitespace\r\ndatasets/ler/ler.py:47:68: W291 trailing whitespace\r\ndatasets/ler/ler.py:48:102: W291 trailing whitespace\r\ndatasets/ler/ler.py:49:112: W291 trailing whitespace\r\ndatasets/ler/ler.py:50:92: W291 trailing whitespace\r\ndatasets/ler/ler.py:51:116: W291 trailing whitespace\r\ndatasets/ler/ler.py:52:84: W291 trailing whitespace\r\ndatasets/math_dataset/math_dataset.py:233:25: E741 ambiguous variable name 'l'\r\nmetrics/coval/coval.py:236:31: F541 f-string is missing placeholders\r\nmake: *** [quality] Error 1\r\n\r\nI do this on macOS Catalina 10.15.7 in case this matters", "Code quality test now passes, thanks :) \r\n\r\nTo fix the other tests failing I think you can just rebase from master.\r\nAlso make sure that the dummy data test passes with\r\n```python\r\nRUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_ler\r\n```", "I will close this PR because abishek did the same better (https://github.com/huggingface/datasets/pull/944)", "Sorry you had to close your PR ! It looks like this week's sprint doesn't always make it easy to see what's being added/what's already added. \r\nThank you for contributing to the library. You did a great job on adding LER so feel free to add other ones that you would like to see in the library, it will be a pleasure to review" ]

[]

[]

[ "I like the idea ! And the approach is right imo\r\n\r\nNote that by changing this we will have to add a way for users to get the config lists of a dataset. In the current user workflow, the user could see the list of the config when the missing config error is raised but now it won't be the case because of the default config.", "Maybe let's add a test in the test_builder.py test script ?", "@lhoestq Okay great, I added a test as well as two new inspect functions: `get_dataset_config_names` and `get_dataset_infos` (the latter is something I've been wanting anyway). As a quick hack, you can also just pass a random config name (e.g. an empty string) to `load_dataset` to get the config names in the error msg as before. Also added a couple paragraphs to the adding new datasets doc.\r\n\r\nI'll send a separate PR incorporating this in existing datasets so we can get this merged before our sprint on Monday.\r\n\r\nAny ideas on the failing tests? I'm having trouble making sense of it. **Edit**: nvm, it was master." ]

[]

[]

[ "Yes following the Tensorflow Datasets convention, objects with type `Sequence of a Dict` are actually stored as a `dictionary of lists`.\r\nSee the [documentation](https://huggingface.co/docs/datasets/features.html?highlight=features) for more details", "Thanks.\r\nThis is a bit (very) confusing, but I guess if its intended, I'll just work with it as if its how my data was originally structured :) \r\n" ]

[ { "id": 1935892857, "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug", "name": "bug", "color": "d73a4a", "default": true, "description": "Something isn't working" } ]

[]

[ "Yes right now `ArrayXD` can only be used as a column feature type, not a subtype.\r\nWith the current Arrow limitations I don't think we'll be able to make it work as a subtype, however it should be possible to allow dimensions of dynamic sizes (`Array3D(shape=(None, 137, 2), dtype=\"float32\")` for example since the [underlying arrow type](https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L236) allows dynamic sizes.\r\n\r\nFor now I'd suggest the use of nested `Sequence` types. Once we have the dynamic sizes you can update the dataset.\r\nWhat do you think ?", "> Yes right now ArrayXD can only be used as a column feature type, not a subtype. \r\n\r\nMeaning it can't be nested under `Sequence`?\r\nIf so, for now I'll just make it a python list and make it with the nested `Sequence` type you suggested.", "Yea unfortunately..\r\nThat's a current limitation with Arrow ExtensionTypes that can't be used in the default Arrow Array objects.\r\nWe already have an ExtensionArray that allows us to use them as column types but not for subtypes.\r\nMaybe we can extend it, I haven't experimented with that yet", "Cool\r\nSo please consider this issue as a feature request for:\r\n```\r\nArray3D(shape=(None, 137, 2), dtype=\"float32\")\r\n```\r\n\r\nits a way to represent videos, poses, and other cool sequences", "@lhoestq well, so sequence of sequences doesn't work either...\r\n\r\n```\r\npyarrow.lib.ArrowCapacityError: List array cannot contain more than 2147483646 child elements, have 2147483648\r\n```\r\n\r\n\r\n", "Working with Arrow can be quite fun sometimes.\r\nYou can fix this issue by trying to reduce the writer batch size (same trick than the one used to reduce the RAM usage in https://github.com/huggingface/datasets/issues/741).\r\n\r\nLet me know if it works.\r\nI haven't investigated yet on https://github.com/huggingface/datasets/issues/741 since I was preparing this week's sprint to add datasets but this is in my priority list for early next week.", "The batch size fix doesn't work... not for #741 and not for this dataset I'm trying (DGS corpus)\r\nLoading the DGS corpus takes 400GB of RAM, which is fine with me as my machine is large enough\r\n", "Sorry it doesn't work. Will let you know once I fixed it", "Hi @lhoestq , any update on dynamic sized arrays?\r\n(`Array3D(shape=(None, 137, 2), dtype=\"float32\")`)", "Not yet, I've been pretty busy with the dataset sprint lately but this is something that's been asked several times already. So I'll definitely work on this as soon as I'm done with the sprint and with the RAM issue you reported.", "Hi @lhoestq,\r\nAny chance you have some updates on the supporting `ArrayXD` as a subtype or support of dynamic sized arrays?\r\n\r\ne.g.:\r\n`datasets.features.Sequence(datasets.features.Array2D(shape=(137, 2), dtype=\"float32\"))`\r\n`Array3D(shape=(None, 137, 2), dtype=\"float32\")`", "Hi ! We haven't worked in this lately and it's not in our very short-term roadmap since it requires a bit a work to make it work with arrow. Though this will definitely be added at one point.", "@lhoestq, thanks for the update.\r\n\r\nI actually tried to modify some piece of code to make it work. Can you please tell if I missing anything here?\r\nI think that for vast majority of cases it's enough to make first dimension of the array dynamic i.e. `shape=(None, 100, 100)`. For that, it's enough to modify class [ArrayExtensionArray](https://github.com/huggingface/datasets/blob/9ca24250ea44e7611c4dabd01ecf9415a7f0be6c/src/datasets/features.py#L397) to output list of arrays of different sizes instead of list of arrays of same sizes (current version)\r\nBelow are my modifications of this class.\r\n\r\n```\r\nclass ArrayExtensionArray(pa.ExtensionArray):\r\n def __array__(self):\r\n zero_copy_only = _is_zero_copy_only(self.storage.type)\r\n return self.to_numpy(zero_copy_only=zero_copy_only)\r\n\r\n def __getitem__(self, i):\r\n return self.storage[i]\r\n\r\n def to_numpy(self, zero_copy_only=True):\r\n storage: pa.ListArray = self.storage\r\n size = 1\r\n for i in range(self.type.ndims):\r\n size *= self.type.shape[i]\r\n storage = storage.flatten()\r\n numpy_arr = storage.to_numpy(zero_copy_only=zero_copy_only)\r\n numpy_arr = numpy_arr.reshape(len(self), *self.type.shape)\r\n return numpy_arr\r\n\r\n def to_list_of_numpy(self, zero_copy_only=True):\r\n storage: pa.ListArray = self.storage\r\n shape = self.type.shape\r\n arrays = []\r\n for dim in range(1, self.type.ndims):\r\n assert shape[dim] is not None, f\"Support only dynamic size on first dimension. Got: {shape}\"\r\n\r\n first_dim_offsets = np.array([off.as_py() for off in storage.offsets])\r\n for i in range(len(storage)):\r\n storage_el = storage[i:i+1]\r\n first_dim = first_dim_offsets[i+1] - first_dim_offsets[i]\r\n # flatten storage\r\n for dim in range(self.type.ndims):\r\n storage_el = storage_el.flatten()\r\n\r\n numpy_arr = storage_el.to_numpy(zero_copy_only=zero_copy_only)\r\n arrays.append(numpy_arr.reshape(first_dim, *shape[1:]))\r\n\r\n return arrays\r\n\r\n def to_pylist(self):\r\n zero_copy_only = _is_zero_copy_only(self.storage.type)\r\n if self.type.shape[0] is None:\r\n return self.to_list_of_numpy(zero_copy_only=zero_copy_only)\r\n else:\r\n return self.to_numpy(zero_copy_only=zero_copy_only).tolist()\r\n```\r\n\r\nI ran few tests and it works as expected. Let me know what you think.", "Thanks for diving into this !\r\n\r\nIndeed focusing on making the first dimensions dynamic make total sense (and users could still re-order their dimensions to match this constraint).\r\nYour code looks great :) I think it can even be extended to support several dynamic dimensions if we want to.\r\n\r\nFeel free to open a PR to include these changes, then we can update our test suite to make sure it works in all use cases.\r\nIn particular I think we might need a few tweaks to allow it to be converted to pandas (though I haven't tested yet):\r\n\r\n```python\r\nfrom datasets import Dataset, Features, Array3D\r\n\r\n# this works\r\nmatrix = [[1, 0], [0, 1]]\r\nfeatures = Features({\"a\": Array3D(dtype=\"int32\", shape=(1, 2, 2))})\r\nd = Dataset.from_dict({\"a\": [[matrix], [matrix]]})\r\nprint(d.to_pandas())\r\n\r\n# this should work as well\r\nmatrix = [[1, 0], [0, 1]]\r\nfeatures = Features({\"a\": Array3D(dtype=\"int32\", shape=(None, 2, 2))})\r\nd = Dataset.from_dict({\"a\": [[matrix], [matrix] * 2]})\r\nprint(d.to_pandas())\r\n```\r\n\r\nI'll be happy to help you on this :)" ]