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https://api.github.com/repos/huggingface/datasets/issues/988 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/988/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/988/comments | https://api.github.com/repos/huggingface/datasets/issues/988/events | https://github.com/huggingface/datasets/issues/988 | 755,069,159 | MDU6SXNzdWU3NTUwNjkxNTk= | 988 | making sure datasets are not loaded in memory and distributed training of them | {
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"my implementation of sharding per TPU core: https://github.com/google-research/ruse/blob/d4dd58a2d8efe0ffb1a9e9e77e3228d6824d3c3c/seq2seq/trainers/t5_trainer.py#L316 \r\nmy implementation of dataloader for this case https://github.com/google-research/ruse/blob/d4dd58a2d8efe0ffb1a9e9e77e3228d6824d3c3c/seq2seq/tasks... | 1,606,898,715,000 | 1,606,899,034,000 | null | CONTRIBUTOR | null | null | null | Hi
I am dealing with large-scale datasets which I need to train distributedly, I used the shard function to divide the dataset across the cores, without any sampler, this does not work for distributed training and does not become any faster than 1 TPU core. 1) how I can make sure data is not loaded in memory 2) in case of distributed training with iterative datasets which measures needs to be taken? Is this all sharding the data only. I was wondering if there can be possibility for me to discuss this with someone with distributed training with iterative datasets using dataset library. thanks | {
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"merging since the CI is fixed on master"
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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::t... | 1,606,896,676,000 | 1,606,934,242,000 | 1,606,932,179,000 | CONTRIBUTOR | null | false | {
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} | Adds the SciTLDR Dataset by AI2
Added README card with tags to the best of my knowledge
Multi-target summaries or TLDRs of Scientific Documents | {
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"This dataset already exists apparently, sorry :/ \r\nsee\r\nhttps://github.com/huggingface/datasets/blob/master/datasets/gap/gap.py\r\n\r\nFeel free to re-use the dataset card you did for `/datasets/gap`\r\n",
"oh heck, my bad 🤦♂️ sorry"
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Gender bias coreference resolution | {
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"can't find the Whoa file since there' nothing left",
"The classic `rm -rf` command - nice one"
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} | I decided it will be faster to create a new pull request instead of fixing the rebase issues.
continuing from https://github.com/huggingface/datasets/pull/954
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https://api.github.com/repos/huggingface/datasets/issues/980 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/980/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/980/comments | https://api.github.com/repos/huggingface/datasets/issues/980/events | https://github.com/huggingface/datasets/pull/980 | 754,899,301 | MDExOlB1bGxSZXF1ZXN0NTMwNzEzNjY3 | 980 | Wongnai - Thai reviews dataset | {
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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 ... | 1,606,879,208,000 | 1,606,923,281,000 | 1,606,923,005,000 | CONTRIBUTOR | null | false | {
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} | 40,000 reviews, previously released on GitHub ( https://github.com/wongnai/wongnai-corpus ) with an LGPL license, and on a closed Kaggle competition ( https://www.kaggle.com/c/wongnai-challenge-review-rating-prediction/ ) | {
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https://api.github.com/repos/huggingface/datasets/issues/979 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/979/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/979/comments | https://api.github.com/repos/huggingface/datasets/issues/979/events | https://github.com/huggingface/datasets/pull/979 | 754,893,337 | MDExOlB1bGxSZXF1ZXN0NTMwNzA4OTA5 | 979 | [WIP] Add multi woz | {
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} | This PR adds version 2.2 of the Multi-domain Wizard of OZ dataset: https://github.com/budzianowski/multiwoz/tree/master/data/MultiWOZ_2.2
It was a pretty big chunk of work to figure out the structure, so I stil have tol add the description to the README.md
On the plus side the structure is broadly similar to that of the Google Schema Guided dialogue [dataset](https://github.com/google-research-datasets/dstc8-schema-guided-dialogue), so will take care of that one next. | {
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https://api.github.com/repos/huggingface/datasets/issues/978 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/978/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/978/comments | https://api.github.com/repos/huggingface/datasets/issues/978/events | https://github.com/huggingface/datasets/pull/978 | 754,854,478 | MDExOlB1bGxSZXF1ZXN0NTMwNjc4NTUy | 978 | Add code refinement | {
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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 cons... | 1,606,872,598,000 | 1,607,305,978,000 | 1,607,305,978,000 | CONTRIBUTOR | null | false | {
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} | ### OVERVIEW
Millions of open-source projects with numerous bug fixes
are available in code repositories. This proliferation
of software development histories can be leveraged to
learn how to fix common programming bugs
Code refinement aims to automatically fix bugs in the code,
which can contribute to reducing the cost of bug-fixes for developers.
Given a piece of Java code with bugs,
the task is to remove the bugs to output the refined code. | {
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https://api.github.com/repos/huggingface/datasets/issues/977 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/977/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/977/comments | https://api.github.com/repos/huggingface/datasets/issues/977/events | https://github.com/huggingface/datasets/pull/977 | 754,839,594 | MDExOlB1bGxSZXF1ZXN0NTMwNjY2ODg3 | 977 | Add ROPES dataset | {
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} | ROPES dataset
Reasoning over paragraph effects in situations - testing a system's ability to apply knowledge from a passage of text to a new situation. The task is framed into a reading comprehension task following squad-style extractive qa.
One thing to note: labels of the test set are hidden (leaderboard submission) so I encoded that as an empty list (ropes.py:L125) | {
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https://api.github.com/repos/huggingface/datasets/issues/976 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/976/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/976/comments | https://api.github.com/repos/huggingface/datasets/issues/976/events | https://github.com/huggingface/datasets/pull/976 | 754,826,146 | MDExOlB1bGxSZXF1ZXN0NTMwNjU1NzM5 | 976 | Arabic pos dialect | {
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"looks like this PR includes changes about many other files than the oens for Araboc POS Dialect\r\n\r\nCan you create a another branch and another PR please ?",
"Sorry! I'm not sure how I managed to do that. I'll make a new branch."
] | 1,606,868,473,000 | 1,607,535,032,000 | 1,607,535,032,000 | CONTRIBUTOR | null | false | {
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} | A README.md and loading script for the Arabic POS Dialect dataset. The README is missing the sections on personal information, biases, and limitations, as it would probably be better for those to be filled by someone who can read the contents of the dataset and is familiar with Arabic NLP. | {
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https://api.github.com/repos/huggingface/datasets/issues/975 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/975/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/975/comments | https://api.github.com/repos/huggingface/datasets/issues/975/events | https://github.com/huggingface/datasets/pull/975 | 754,823,701 | MDExOlB1bGxSZXF1ZXN0NTMwNjUzNjg4 | 975 | add MeTooMA dataset | {
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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.
Paper: https://ojs.aaai.org/index.php/ICWSM/article/view/7292
Dataset Link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/JN4EYU
---
annotations_creators:
- expert-generated
language_creators:
- found
languages:
- en
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
- text-retrieval
task_ids:
- multi-class-classification
- multi-label-classification
---
# Dataset Card for #MeTooMA dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/JN4EYU
- **Paper:** https://ojs.aaai.org//index.php/ICWSM/article/view/7292
- **Point of Contact:** https://github.com/midas-research/MeTooMA
### Dataset Summary
- The dataset consists of tweets belonging to #MeToo movement on Twitter, labeled into different categories.
- This dataset includes more data points and has more labels than any of the previous datasets that contain social media
posts about sexual abuse disclosures. Please refer to the Related Datasets of the publication for detailed information about this.
- Due to Twitter's development policies, the authors provide only the tweet IDs and corresponding labels,
other data can be fetched via Twitter API.
- The data has been labeled by experts, with the majority taken into the account for deciding the final label.
- The authors provide these labels for each of the tweets.
- Relevance
- Directed Hate
- Generalized Hate
- Sarcasm
- Allegation
- Justification
- Refutation
- Support
- Oppose
- The definitions for each task/label are in the main publication.
- Please refer to the accompanying paper https://aaai.org/ojs/index.php/ICWSM/article/view/7292 for statistical analysis on the textual data
extracted from this dataset.
- The language of all the tweets in this dataset is English
- Time period: October 2018 - December 2018
- Suggested Use Cases of this dataset:
- Evaluating usage of linguistic acts such as hate-speech and sarcasm in the context of public sexual abuse disclosures.
- Extracting actionable insights and virtual dynamics of gender roles in sexual abuse revelations.
- Identifying how influential people were portrayed on the public platform in the
events of mass social movements.
- Polarization analysis based on graph simulations of social nodes of users involved
in the #MeToo movement.
### Supported Tasks and Leaderboards
Multi-Label and Multi-Class Classification
### Languages
English
## Dataset Structure
- The dataset is structured into CSV format with TweetID and accompanying labels.
- Train and Test sets are split into respective files.
### Data Instances
Tweet ID and the appropriate labels
### Data Fields
Tweet ID and appropriate labels (binary label applicable for a data point) and multiple labels for each Tweet ID
### Data Splits
- Train: 7979
- Test: 1996
## Dataset Creation
### Curation Rationale
- Twitter was the major source of all the public disclosures of sexual abuse incidents during the #MeToo movement.
- People expressed their opinions over issues that were previously missing from the social media space.
- This provides an option to study the linguistic behaviors of social media users in an informal setting,
therefore the authors decide to curate this annotated dataset.
- The authors expect this dataset would be of great interest and use to both computational and socio-linguists.
- For computational linguists, it provides an opportunity to model three new complex dialogue acts (allegation, refutation, and justification) and also to study how these acts interact with some of the other linguistic components like stance, hate, and sarcasm. For socio-linguists, it provides an opportunity to explore how a movement manifests in social media.
### Source Data
- Source of all the data points in this dataset is a Twitter social media platform.
#### Initial Data Collection and Normalization
- All the tweets are mined from Twitter with initial search parameters identified using keywords from the #MeToo movement.
- Redundant keywords were removed based on manual inspection.
- Public streaming APIs of Twitter was used for querying with the selected keywords.
- Based on text de-duplication and cosine similarity score, the set of tweets were pruned.
- Non-English tweets were removed.
- The final set was labeled by experts with the majority label taken into the account for deciding the final label.
- Please refer to this paper for detailed information: https://ojs.aaai.org//index.php/ICWSM/article/view/7292
#### Who are the source language producers?
Please refer to this paper for detailed information: https://ojs.aaai.org//index.php/ICWSM/article/view/7292
### Annotations
#### Annotation process
- The authors chose against crowdsourcing for labeling this dataset due to its highly sensitive nature.
- The annotators are domain experts having degrees in advanced clinical psychology and gender studies.
- They were provided a guidelines document with instructions about each task and its definitions, labels, and examples.
- They studied the document, worked on a few examples to get used to this annotation task.
- They also provided feedback for improving the class definitions.
- The annotation process is not mutually exclusive, implying that the presence of one label does not mean the
absence of the other one.
#### Who are the annotators?
- The annotators are domain experts having a degree in clinical psychology and gender studies.
- Please refer to the accompanying paper for a detailed annotation process.
### Personal and Sensitive Information
- Considering Twitter's policy for distribution of data, only Tweet ID and applicable labels are shared for public use.
- It is highly encouraged to use this dataset for scientific purposes only.
- This dataset collection completely follows the Twitter mandated guidelines for distribution and usage.
## Considerations for Using the Data
### Social Impact of Dataset
- The authors of this dataset do not intend to conduct a population-centric analysis of the #MeToo movement on Twitter.
- The authors acknowledge that findings from this dataset cannot be used as-is for any direct social intervention, these
should be used to assist already existing human intervention tools and therapies.
- Enough care has been taken to ensure that this work comes off as trying to target a specific person for their
the personal stance of issues pertaining to the #MeToo movement.
- The authors of this work do not aim to vilify anyone accused in the #MeToo movement in any manner.
- Please refer to the ethics and discussion section of the mentioned publication for appropriate sharing of this dataset
and the social impact of this work.
### Discussion of Biases
- The #MeToo movement acted as a catalyst for implementing social policy changes to benefit the members of
the community affected by sexual abuse.
- Any work undertaken on this dataset should aim to minimize the bias against minority groups which
might amplify in cases of a sudden outburst of public reactions over sensitive social media discussions.
### Other Known Limitations
- Considering privacy concerns, social media practitioners should be aware of making automated interventions
to aid the victims of sexual abuse as some people might not prefer to disclose their notions.
- Concerned social media users might also repeal their social information if they found out that their
information is being used for computational purposes, hence it is important to seek subtle individual consent
before trying to profile authors involved in online discussions to uphold personal privacy.
## Additional Information
Please refer to this link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/JN4EYU
### Dataset Curators
- If you use the corpus in a product or application, then please credit the authors
and [Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi]
(http://midas.iiitd.edu.in) appropriately.
Also, if you send us an email, we will be thrilled to know about how you have used the corpus.
- If interested in the commercial use of the corpus, send an email to midas@iiitd.ac.in.
- Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi, India
disclaims any responsibility for the use of the corpus and does not provide technical support.
However, the contact listed above will be happy to respond to queries and clarifications
- Please feel free to send us an email:
- with feedback regarding the corpus.
- with information on how you have used the corpus.
- if interested in having us analyze your social media data.
- if interested in a collaborative research project.
### Licensing Information
[More Information Needed]
### Citation Information
Please cite the following publication if you make use of the dataset: https://ojs.aaai.org/index.php/ICWSM/article/view/7292
```
@article{Gautam_Mathur_Gosangi_Mahata_Sawhney_Shah_2020, title={#MeTooMA: Multi-Aspect Annotations of Tweets Related to the MeToo Movement}, volume={14}, url={https://aaai.org/ojs/index.php/ICWSM/article/view/7292}, abstractNote={<p>In this paper, we present a dataset containing 9,973 tweets related to the MeToo movement that were manually annotated for five different linguistic aspects: relevance, stance, hate speech, sarcasm, and dialogue acts. We present a detailed account of the data collection and annotation processes. The annotations have a very high inter-annotator agreement (0.79 to 0.93 k-alpha) due to the domain expertise of the annotators and clear annotation instructions. We analyze the data in terms of geographical distribution, label correlations, and keywords. Lastly, we present some potential use cases of this dataset. We expect this dataset would be of great interest to psycholinguists, socio-linguists, and computational linguists to study the discursive space of digitally mobilized social movements on sensitive issues like sexual harassment.</p&gt;}, number={1}, journal={Proceedings of the International AAAI Conference on Web and Social Media}, author={Gautam, Akash and Mathur, Puneet and Gosangi, Rakesh and Mahata, Debanjan and Sawhney, Ramit and Shah, Rajiv Ratn}, year={2020}, month={May}, pages={209-216} }
```
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"I have to manually copy a dataset_infos.json file from other dataset and modify it since the `datasets-cli` isn't able to handle manually downloaded datasets yet (as far as I know).",
"you can generate the dataset_infos.json file even for dataset with manual data\r\nTo do so just specify `--data_dir <path/to/the... | 1,606,865,783,000 | 1,607,095,544,000 | 1,607,094,766,000 | CONTRIBUTOR | null | false | {
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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 p... | 1,606,863,206,000 | 1,616,153,403,000 | 1,616,153,403,000 | MEMBER | null | false | {
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Sentence completion given a few sentences as context from a children's book. | {
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https://yonatanbisk.com/piqa/ | {
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https://github.com/chaojiang06/wiki-auto
This is also a prospective GEM task, hence the README.md | {
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"One trick if you want to add other datasets: consider running these commands each time you want to add a new dataset\r\n```\r\ngit checkout master\r\ngit fetch upstream\r\ngit rebase upstream/master\r\ngit checkout -b add-<my_dataset_name>\r\n```"
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"Oh yeah, for some reason I thought you had to do it after the merge, I'll get on it",
"Weird, now the CI seems to fail because of other datasets (XGLUE, Norwegian_NER)",
"Yea you just need to rebase from master",
"Re-opening a PR without the messed-up rebase"
] | 1,606,843,057,000 | 1,606,931,864,000 | 1,606,931,845,000 | MEMBER | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/966 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/966/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/966/comments | https://api.github.com/repos/huggingface/datasets/issues/966/events | https://github.com/huggingface/datasets/pull/966 | 754,558,686 | MDExOlB1bGxSZXF1ZXN0NTMwNDM4NDE4 | 966 | Add CLINC150 Dataset | {
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"Looks like your PR now shows changes in many other files than the ones for CLINC150.\r\nFeel free to create another branch and another PR",
"created new [PR](https://github.com/huggingface/datasets/pull/1016)\r\n\r\nclosing this!"
] | 1,606,841,413,000 | 1,606,934,743,000 | 1,606,934,730,000 | CONTRIBUTOR | null | false | {
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} | Added CLINC150 Dataset. The link to the dataset can be found [here](https://github.com/clinc/oos-eval) and the paper can be found [here](https://www.aclweb.org/anthology/D19-1131.pdf)
- [x] Followed the instructions in CONTRIBUTING.md
- [x] Ran the tests successfully
- [x] Created the dummy data | {
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- [x] Followed the instructions in CONTRIBUTING.md
- [x] Ran the tests successfully
- [x] Created the dummy data | {
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"This is task is part of the GEM suite so will actually need a more complete dataset card. I'm taking a break for now though and will get back to it before merging :) "
] | 1,606,835,123,000 | 1,606,930,445,000 | 1,606,930,445,000 | MEMBER | null | false | {
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} | This PR adds data from the WebNLG challenge, with one configuration per release and challenge iteration.
More information can be found [here](https://webnlg-challenge.loria.fr/)
Unfortunately, the data itself comes from a pretty large number of small XML files, so the dummy data ends up being quite large (8.4 MB even keeping only one example per file). | {
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https://api.github.com/repos/huggingface/datasets/issues/963 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/963/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/963/comments | https://api.github.com/repos/huggingface/datasets/issues/963/events | https://github.com/huggingface/datasets/pull/963 | 754,451,234 | MDExOlB1bGxSZXF1ZXN0NTMwMzQ5NjQ4 | 963 | add CODAH dataset | {
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More info:
https://github.com/Websail-NU/CODAH | {
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https://api.github.com/repos/huggingface/datasets/issues/961 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/961/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/961/comments | https://api.github.com/repos/huggingface/datasets/issues/961/events | https://github.com/huggingface/datasets/issues/961 | 754,434,398 | MDU6SXNzdWU3NTQ0MzQzOTg= | 961 | sample multiple datasets | {
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"here I share my dataloader currently for multiple tasks: https://gist.github.com/rabeehkarimimahabadi/39f9444a4fb6f53dcc4fca5d73bf8195 \r\n\r\nI need to train my model distributedly with this dataloader, \"MultiTasksataloader\", currently this does not work in distributed fasion,\r\nto save on memory I tried to us... | 1,606,832,402,000 | 1,606,872,764,000 | null | CONTRIBUTOR | null | null | null | Hi
I am dealing with multiple datasets, I need to have a dataloader over them with a condition that in each batch data samples are coming from one of the datasets. My main question is:
- I need to have a way to sample the datasets first with some weights, lets say 2x dataset1 1x dataset2, could you point me how I can do it
sub-questions:
- I want to concat sampled datasets and define one dataloader on it, then I need a way to make sure batches come from 1 dataset in each iteration, could you assist me how I can do?
- I use iterative-type of datasets, but I need a method of shuffling still since it brings accuracy performance issues if not doing it, thanks for the help. | {
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"@lhoestq I added the README files, and now the tests fail... (check commit history, only changed MD file)\r\nThe tests seem a bit unstable",
"the `RemoteDatasetTest ` errors in the CI are fixed on master so it's fine",
"merging since the CI is fixed on master"
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"Merging this one, good job and thank you @jplu :) "
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I have added the `conllu` package as a test dependency. This is required to properly parse the `.conllu` files. | {
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"> Really cool ! Thanks for adding this one :)\r\n> Good job at adding all those citations for each task\r\n> \r\n> Looks like the dummy data test doesn't pass. Maybe some files are missing in the dummy_data.zip files ?\r\n> The error reports `pragmeval/verifiability/train.tsv` to be missing\r\n> \r\n> Also could y... | 1,606,826,955,000 | 1,607,078,612,000 | 1,606,988,207,000 | CONTRIBUTOR | null | false | {
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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... | 1,606,826,455,000 | 1,606,885,931,000 | 1,606,884,232,000 | CONTRIBUTOR | null | false | {
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} | `prachathai-67k`: News Article Corpus and Multi-label Text Classificdation from Prachathai.com
The prachathai-67k dataset was scraped from the news site Prachathai.
We filtered out those articles with less than 500 characters of body text, mostly images and cartoons.
It contains 67,889 articles wtih 12 curated tags from August 24, 2004 to November 15, 2018.
The dataset was originally scraped by @lukkiddd and cleaned by @cstorm125.
You can also see preliminary exploration at https://github.com/PyThaiNLP/prachathai-67k/blob/master/exploration.ipynb | {
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"Hi @lhoestq,\r\nInitially I tried int(-1) only in place of nan labels and missing values but I kept on getting this error ```pyarrow.lib.ArrowTypeError: Expected bytes, got a 'int' object``` maybe because I'm sending int values (-1) to objects which are string type"
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Paper: [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321) | {
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"Wrongly branching from existing branch of wisesight_sentiment. Closing and opening another one specifically for prachathai67k"
] | 1,606,825,312,000 | 1,606,825,793,000 | 1,606,825,706,000 | CONTRIBUTOR | null | false | {
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The `prachathai-67k` dataset was scraped from the news site [Prachathai](prachathai.com). We filtered out those articles with less than 500 characters of body text, mostly images and cartoons. It contains 67,889 articles wtih 12 curated tags from August 24, 2004 to November 15, 2018. The dataset was originally scraped by [@lukkiddd](https://github.com/lukkiddd) and cleaned by [@cstorm125](https://github.com/cstorm125). Download the dataset [here](https://www.dropbox.com/s/fsxepdka4l2pr45/prachathai-67k.zip?dl=1). You can also see preliminary exploration in [exploration.ipynb](https://github.com/PyThaiNLP/prachathai-67k/blob/master/exploration.ipynb).
This dataset is a part of [pyThaiNLP](https://github.com/PyThaiNLP/) Thai text [classification-benchmarks](https://github.com/PyThaiNLP/classification-benchmarks). For the benchmark, we selected the following tags with substantial volume that resemble **classifying types of articles**:
* `การเมือง` - politics
* `สิทธิมนุษยชน` - human_rights
* `คุณภาพชีวิต` - quality_of_life
* `ต่างประเทศ` - international
* `สังคม` - social
* `สิ่งแวดล้อม` - environment
* `เศรษฐกิจ` - economics
* `วัฒนธรรม` - culture
* `แรงงาน` - labor
* `ความมั่นคง` - national_security
* `ไอซีที` - ict
* `การศึกษา` - education | {
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"@lhoestq added. "
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"The checks failed again even if I didn't make any changes.",
"you just need to rebase from master to fix the CI :)",
"Sorry for the mess, I'm confused by the rebase and thus created a new branch."
] | 1,606,819,301,000 | 1,606,963,634,000 | 1,606,963,634,000 | CONTRIBUTOR | null | false | {
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"Replaced by #1126"
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Samples are taken from ParlAI for consistency with the main users at the moment. | {
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"thanks ! merging this one"
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} | This PR adds the [FLUE](https://github.com/getalp/Flaubert/tree/master/flue) benchmark which is a set of different datasets to evaluate models for French content.
Two datasets are missing, the French Treebank that we can use only for research purpose and we are not allowed to distribute, and the Word Sense disambiguation for Nouns that will be added later. | {
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- **Name:** *name of the dataset*
- **Description:** *short description of the dataset (or link to social media or blog post)*
- **Paper:** *link to the dataset paper if available*
- **Data:** *link to the Github repository or current dataset location*
- **Motivation:** *what are some good reasons to have this dataset*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). | {
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"> LGTM thanks :)\n> \n> \n> \n> Before we merge, could you add a dataset card ? see here for more info: https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#tag-the-dataset-and-write-the-dataset-card\n> \n> \n> \n> Note that only the tags at the top of the dataset card are mandatory, if you feel ... | 1,606,808,933,000 | 1,606,934,563,000 | 1,606,934,561,000 | MEMBER | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/940 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/940/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/940/comments | https://api.github.com/repos/huggingface/datasets/issues/940/events | https://github.com/huggingface/datasets/pull/940 | 754,010,753 | MDExOlB1bGxSZXF1ZXN0NTI5OTc3OTQ2 | 940 | Add MSRA NER dataset | {
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"LGTM, don't forget the tags ;)"
] | 1,606,798,931,000 | 1,607,074,180,000 | 1,606,807,553,000 | MEMBER | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/939 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/939/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/939/comments | https://api.github.com/repos/huggingface/datasets/issues/939/events | https://github.com/huggingface/datasets/pull/939 | 753,965,405 | MDExOlB1bGxSZXF1ZXN0NTI5OTQwOTYz | 939 | add wisesight_sentiment | {
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"@lhoestq Thanks, Quentin. Removed the .ipynb_checkpoints and edited the README.md. The tests are failing because of other dataets. I'm figuring out why since the commits only have changes on `wisesight_sentiment`\r\n\r\n```\r\nFAILED tests/test_dataset_common.py::RemoteDatasetTest::test_builder_class_flue\r\nFAILE... | 1,606,791,999,000 | 1,606,884,758,000 | 1,606,883,751,000 | CONTRIBUTOR | null | false | {
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} | Add `wisesight_sentiment` Social media messages in Thai language with sentiment label (positive, neutral, negative, question)
Model Card:
---
YAML tags:
annotations_creators:
- expert-generated
language_creators:
- found
languages:
- th
licenses:
- cc0-1.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
---
# Dataset Card for wisesight_sentiment
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://github.com/PyThaiNLP/wisesight-sentiment
- **Repository:** https://github.com/PyThaiNLP/wisesight-sentiment
- **Paper:**
- **Leaderboard:** https://www.kaggle.com/c/wisesight-sentiment/
- **Point of Contact:** https://github.com/PyThaiNLP/
### Dataset Summary
Wisesight Sentiment Corpus: Social media messages in Thai language with sentiment label (positive, neutral, negative, question)
- Released to public domain under Creative Commons Zero v1.0 Universal license.
- Labels: {"pos": 0, "neu": 1, "neg": 2, "q": 3}
- Size: 26,737 messages
- Language: Central Thai
- Style: Informal and conversational. With some news headlines and advertisement.
- Time period: Around 2016 to early 2019. With small amount from other period.
- Domains: Mixed. Majority are consumer products and services (restaurants, cosmetics, drinks, car, hotels), with some current affairs.
- Privacy:
- Only messages that made available to the public on the internet (websites, blogs, social network sites).
- For Facebook, this means the public comments (everyone can see) that made on a public page.
- Private/protected messages and messages in groups, chat, and inbox are not included.
- Alternations and modifications:
- Keep in mind that this corpus does not statistically represent anything in the language register.
- Large amount of messages are not in their original form. Personal data are removed or masked.
- Duplicated, leading, and trailing whitespaces are removed. Other punctuations, symbols, and emojis are kept intact.
(Mis)spellings are kept intact.
- Messages longer than 2,000 characters are removed.
- Long non-Thai messages are removed. Duplicated message (exact match) are removed.
- More characteristics of the data can be explore [this notebook](https://github.com/PyThaiNLP/wisesight-sentiment/blob/master/exploration.ipynb)
### Supported Tasks and Leaderboards
Sentiment analysis / [Kaggle Leaderboard](https://www.kaggle.com/c/wisesight-sentiment/)
### Languages
Thai
## Dataset Structure
### Data Instances
```
{'category': 'pos', 'texts': 'น่าสนนน'}
{'category': 'neu', 'texts': 'ครับ #phithanbkk'}
{'category': 'neg', 'texts': 'ซื้อแต่ผ้าอนามัยแบบเย็นมาค่ะ แบบว่าอีห่ากูนอนไม่ได้'}
{'category': 'q', 'texts': 'มีแอลกอฮอลมั้ยคะ'}
```
### Data Fields
- `texts`: texts
- `category`: sentiment of texts ranging from `pos` (positive; 0), `neu` (neutral; 1), `neg` (negative; 2) and `q` (question; 3)
### Data Splits
| | train | valid | test |
|-----------|-------|-------|-------|
| # samples | 21628 | 2404 | 2671 |
| # neu | 11795 | 1291 | 1453 |
| # neg | 5491 | 637 | 683 |
| # pos | 3866 | 434 | 478 |
| # q | 476 | 42 | 57 |
| avg words | 27.21 | 27.18 | 27.12 |
| avg chars | 89.82 | 89.50 | 90.36 |
## Dataset Creation
### Curation Rationale
Originally, the dataset was conceived for the [In-class Kaggle Competition](https://www.kaggle.com/c/wisesight-sentiment/) at Chulalongkorn university by [Ekapol Chuangsuwanich](https://www.cp.eng.chula.ac.th/en/about/faculty/ekapolc/) (Faculty of Engineering, Chulalongkorn University). It has since become one of the benchmarks for sentiment analysis in Thai.
### Source Data
#### Initial Data Collection and Normalization
- Style: Informal and conversational. With some news headlines and advertisement.
- Time period: Around 2016 to early 2019. With small amount from other period.
- Domains: Mixed. Majority are consumer products and services (restaurants, cosmetics, drinks, car, hotels), with some current affairs.
- Privacy:
- Only messages that made available to the public on the internet (websites, blogs, social network sites).
- For Facebook, this means the public comments (everyone can see) that made on a public page.
- Private/protected messages and messages in groups, chat, and inbox are not included.
- Usernames and non-public figure names are removed
- Phone numbers are masked (e.g. 088-888-8888, 09-9999-9999, 0-2222-2222)
- If you see any personal data still remain in the set, please tell us - so we can remove them.
- Alternations and modifications:
- Keep in mind that this corpus does not statistically represent anything in the language register.
- Large amount of messages are not in their original form. Personal data are removed or masked.
- Duplicated, leading, and trailing whitespaces are removed. Other punctuations, symbols, and emojis are kept intact.
- (Mis)spellings are kept intact.
- Messages longer than 2,000 characters are removed.
- Long non-Thai messages are removed. Duplicated message (exact match) are removed.
#### Who are the source language producers?
Social media users in Thailand
### Annotations
#### Annotation process
- Sentiment values are assigned by human annotators.
- A human annotator put his/her best effort to assign just one label, out of four, to a message.
- Agreement, enjoyment, and satisfaction are positive. Disagreement, sadness, and disappointment are negative.
- Showing interest in a topic or in a product is counted as positive. In this sense, a question about a particular product could has a positive sentiment value, if it shows the interest in the product.
- Saying that other product or service is better is counted as negative.
- General information or news title tend to be counted as neutral.
#### Who are the annotators?
Outsourced annotators hired by [Wisesight (Thailand) Co., Ltd.](https://github.com/wisesight/)
### Personal and Sensitive Information
- We trying to exclude any known personally identifiable information from this data set.
- Usernames and non-public figure names are removed
- Phone numbers are masked (e.g. 088-888-8888, 09-9999-9999, 0-2222-2222)
- If you see any personal data still remain in the set, please tell us - so we can remove them.
## Considerations for Using the Data
### Social Impact of Dataset
- `wisesight_sentiment` is the first and one of the few open datasets for sentiment analysis of social media data in Thai
- There are risks of personal information that escape the anonymization process
### Discussion of Biases
- A message can be ambiguous. When possible, the judgement will be based solely on the text itself.
- In some situation, like when the context is missing, the annotator may have to rely on his/her own world knowledge and just guess.
- In some cases, the human annotator may have an access to the message's context, like an image. These additional information are not included as part of this corpus.
### Other Known Limitations
- The labels are imbalanced; over half of the texts are `neu` (neutral) whereas there are very few `q` (question).
- Misspellings in social media texts make word tokenization process for Thai difficult, thus impacting the model performance
## Additional Information
### Dataset Curators
Thanks [PyThaiNLP](https://github.com/PyThaiNLP/pythainlp) community, [Kitsuchart Pasupa](http://www.it.kmitl.ac.th/~kitsuchart/) (Faculty of Information Technology, King Mongkut's Institute of Technology Ladkrabang), and [Ekapol Chuangsuwanich](https://www.cp.eng.chula.ac.th/en/about/faculty/ekapolc/) (Faculty of Engineering, Chulalongkorn University) for advice. The original Kaggle competition, using the first version of this corpus, can be found at https://www.kaggle.com/c/wisesight-sentiment/
### Licensing Information
- If applicable, copyright of each message content belongs to the original poster.
- **Annotation data (labels) are released to public domain.**
- [Wisesight (Thailand) Co., Ltd.](https://github.com/wisesight/) helps facilitate the annotation, but does not necessarily agree upon the labels made by the human annotators. This annotation is for research purpose and does not reflect the professional work that Wisesight has been done for its customers.
- The human annotator does not necessarily agree or disagree with the message. Likewise, the label he/she made to the message does not necessarily reflect his/her personal view towards the message.
### Citation Information
Please cite the following if you make use of the dataset:
Arthit Suriyawongkul, Ekapol Chuangsuwanich, Pattarawat Chormai, and Charin Polpanumas. 2019. **PyThaiNLP/wisesight-sentiment: First release.** September.
BibTeX:
```
@software{bact_2019_3457447,
author = {Suriyawongkul, Arthit and
Chuangsuwanich, Ekapol and
Chormai, Pattarawat and
Polpanumas, Charin},
title = {PyThaiNLP/wisesight-sentiment: First release},
month = sep,
year = 2019,
publisher = {Zenodo},
version = {v1.0},
doi = {10.5281/zenodo.3457447},
url = {https://doi.org/10.5281/zenodo.3457447}
}
```
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"closing since it's been added in #957 "
] | 1,606,788,272,000 | 1,606,865,676,000 | 1,606,865,676,000 | CONTRIBUTOR | null | false | {
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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 ma... | 1,606,785,103,000 | 1,608,731,696,000 | null | NONE | null | null | null | Hi,
I'm wondering if https://huggingface.co/docs/datasets/beam_dataset.html has an non-GCP or non-Dataflow version example/tutorial? I tried to migrate it to run on DirectRunner and SparkRunner, however, there were way too many runtime errors that I had to fix during the process, and even so I wasn't able to get either runner correctly producing the desired output.
Thanks!
Shang | {
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https://api.github.com/repos/huggingface/datasets/issues/935 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/935/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/935/comments | https://api.github.com/repos/huggingface/datasets/issues/935/events | https://github.com/huggingface/datasets/pull/935 | 753,863,055 | MDExOlB1bGxSZXF1ZXN0NTI5ODU5MjM4 | 935 | add PIB dataset | {
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"Hi, \r\n\r\nI am unable to get success in these tests. Can someone help me by pointing out possible errors?\r\n\r\nThanks",
"Hi ! you can read the tests by logging in to circleci.\r\n\r\nAnyway for information here are the errors : \r\n```\r\ndatasets/pib/pib.py:19:1: F401 'csv' imported but unused\r\ndatasets/p... | 1,606,776,943,000 | 1,606,864,631,000 | 1,606,864,631,000 | CONTRIBUTOR | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/934 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/934/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/934/comments | https://api.github.com/repos/huggingface/datasets/issues/934/events | https://github.com/huggingface/datasets/pull/934 | 753,860,095 | MDExOlB1bGxSZXF1ZXN0NTI5ODU2ODY4 | 934 | small updates to the "add new dataset" guide | {
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"cc @yjernite @lhoestq @thomwolf "
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https://api.github.com/repos/huggingface/datasets/issues/933 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/933/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/933/comments | https://api.github.com/repos/huggingface/datasets/issues/933/events | https://github.com/huggingface/datasets/pull/933 | 753,854,272 | MDExOlB1bGxSZXF1ZXN0NTI5ODUyMTI1 | 933 | Add NumerSense | {
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} | Adds the NumerSense dataset
- Webpage/leaderboard: https://inklab.usc.edu/NumerSense/
- Paper: https://arxiv.org/abs/2005.00683
- Description: NumerSense is a new numerical commonsense reasoning probing task, with a diagnostic dataset consisting of 3,145 masked-word-prediction probes. Basically, it's a benchmark to see whether your MLM can figure out the right number in a fill-in-the-blank task based on commonsense knowledge (a bird has **two** legs) | {
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https://api.github.com/repos/huggingface/datasets/issues/932 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/932/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/932/comments | https://api.github.com/repos/huggingface/datasets/issues/932/events | https://github.com/huggingface/datasets/pull/932 | 753,840,300 | MDExOlB1bGxSZXF1ZXN0NTI5ODQwNjQ3 | 932 | adding metooma dataset | {
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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 gu... | 1,606,774,189,000 | 1,606,869,474,000 | 1,606,869,474,000 | CONTRIBUTOR | null | false | {
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} | Have a string `zipfile.BadZipFile: Bad CRC-32 for file 'web_snippets_train.json'` error when downloading the largest file from dropbox: `https://www.dropbox.com/sh/7pkwkrfnwqhsnpo/AABVENv_Q9rFtnM61liyzO0La/web_snippets_train.json.zip?dl=1`
Didn't managed to see how to solve that.
Putting aside for now.
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https://api.github.com/repos/huggingface/datasets/issues/930 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/930/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/930/comments | https://api.github.com/repos/huggingface/datasets/issues/930/events | https://github.com/huggingface/datasets/pull/930 | 753,801,204 | MDExOlB1bGxSZXF1ZXN0NTI5ODA5MzM1 | 930 | Lambada | {
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} | Added LAMBADA dataset.
A couple of points of attention (mostly because I am not sure)
- The training data are compressed in a .tar file inside the main tar.gz file. I had to manually un-tar the training file to access the examples.
- The dev and test splits don't have the `category` field so I put `None` by default.
Happy to make changes if it doesn't respect the guidelines!
Victor | {
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https://api.github.com/repos/huggingface/datasets/issues/929 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/929/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/929/comments | https://api.github.com/repos/huggingface/datasets/issues/929/events | https://github.com/huggingface/datasets/pull/929 | 753,737,794 | MDExOlB1bGxSZXF1ZXN0NTI5NzU4NTU3 | 929 | Add weibo NER dataset | {
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https://api.github.com/repos/huggingface/datasets/issues/928 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/928/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/928/comments | https://api.github.com/repos/huggingface/datasets/issues/928/events | https://github.com/huggingface/datasets/pull/928 | 753,722,324 | MDExOlB1bGxSZXF1ZXN0NTI5NzQ1OTIx | 928 | Add the Multilingual Amazon Reviews Corpus | {
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} | - **Name:** Multilingual Amazon Reviews Corpus* (`amazon_reviews_multi`)
- **Description:** A collection of Amazon reviews in English, Japanese, German, French, Spanish and Chinese.
- **Paper:** https://arxiv.org/abs/2010.02573
### Checkbox
- [x] Create the dataset script `/datasets/my_dataset/my_dataset.py` using the template
- [x] Fill the `_DESCRIPTION` and `_CITATION` variables
- [x] Implement `_infos()`, `_split_generators()` and `_generate_examples()`
- [x] Make sure that the `BUILDER_CONFIGS` class attribute is filled with the different configurations of the dataset and that the `BUILDER_CONFIG_CLASS` is specified if there is a custom config class.
- [x] Generate the metadata file `dataset_infos.json` for all configurations
- [x] Generate the dummy data `dummy_data.zip` files to have the dataset script tested and that they don't weigh too much (<50KB)
- [x] Add the dataset card `README.md` using the template : fill the tags and the various paragraphs
- [x] Both tests for the real data and the dummy data pass. | {
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https://api.github.com/repos/huggingface/datasets/issues/927 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/927/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/927/comments | https://api.github.com/repos/huggingface/datasets/issues/927/events | https://github.com/huggingface/datasets/issues/927 | 753,679,020 | MDU6SXNzdWU3NTM2NzkwMjA= | 927 | Hello | {
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https://api.github.com/repos/huggingface/datasets/issues/926 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/926/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/926/comments | https://api.github.com/repos/huggingface/datasets/issues/926/events | https://github.com/huggingface/datasets/pull/926 | 753,676,069 | MDExOlB1bGxSZXF1ZXN0NTI5NzA4MTcy | 926 | add inquisitive | {
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"`dummy_data` right now contains all article files, keeping only the required articles for dummy data fails the dummy data test.\r\nAny idea ?",
"> `dummy_data` right now contains all article files, keeping only the required articles for dummy data fails the dummy data test.\r\n> Any idea ?\r\n\r\nWe should defin... | 1,606,758,322,000 | 1,606,916,722,000 | 1,606,916,413,000 | MEMBER | null | false | {
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} | Adding inquisitive qg dataset
More info: https://github.com/wjko2/INQUISITIVE | {
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https://api.github.com/repos/huggingface/datasets/issues/925 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/925/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/925/comments | https://api.github.com/repos/huggingface/datasets/issues/925/events | https://github.com/huggingface/datasets/pull/925 | 753,672,661 | MDExOlB1bGxSZXF1ZXN0NTI5NzA1MzM4 | 925 | Add Turku NLP Corpus for Finnish NER | {
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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"
] | 1,606,758,019,000 | 1,607,004,431,000 | 1,607,004,430,000 | MEMBER | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/924 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/924/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/924/comments | https://api.github.com/repos/huggingface/datasets/issues/924/events | https://github.com/huggingface/datasets/pull/924 | 753,631,951 | MDExOlB1bGxSZXF1ZXN0NTI5NjcyMzgw | 924 | Add DART | {
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"LGTM!"
] | 1,606,754,557,000 | 1,606,878,822,000 | 1,606,878,821,000 | MEMBER | null | false | {
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} | - **Name:** *DART*
- **Description:** *DART is a large dataset for open-domain structured data record to text generation.*
- **Paper:** *https://arxiv.org/abs/2007.02871*
- **Data:** *https://github.com/Yale-LILY/dart#leaderboard*
### Checkbox
- [x] Create the dataset script `/datasets/my_dataset/my_dataset.py` using the template
- [x] Fill the `_DESCRIPTION` and `_CITATION` variables
- [x] Implement `_infos()`, `_split_generators()` and `_generate_examples()`
- [x] Make sure that the `BUILDER_CONFIGS` class attribute is filled with the different configurations of the dataset and that the `BUILDER_CONFIG_CLASS` is specified if there is a custom config class.
- [x] Generate the metadata file `dataset_infos.json` for all configurations
- [x] Generate the dummy data `dummy_data.zip` files to have the dataset script tested and that they don't weigh too much (<50KB)
- [x] Add the dataset card `README.md` using the template : fill the tags and the various paragraphs
- [x] Both tests for the real data and the dummy data pass. | {
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https://api.github.com/repos/huggingface/datasets/issues/923 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/923/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/923/comments | https://api.github.com/repos/huggingface/datasets/issues/923/events | https://github.com/huggingface/datasets/pull/923 | 753,569,220 | MDExOlB1bGxSZXF1ZXN0NTI5NjIyMDQx | 923 | Add CC-100 dataset | {
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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 .x... | 1,606,749,802,000 | 1,618,925,657,000 | 1,618,925,657,000 | MEMBER | null | true | {
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} | Add CC-100.
Close #773 | {
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https://api.github.com/repos/huggingface/datasets/issues/922 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/922/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/922/comments | https://api.github.com/repos/huggingface/datasets/issues/922/events | https://github.com/huggingface/datasets/pull/922 | 753,559,130 | MDExOlB1bGxSZXF1ZXN0NTI5NjEzOTA4 | 922 | Add XOR QA Dataset | {
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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 datas... | 1,606,749,054,000 | 1,606,878,741,000 | 1,606,878,741,000 | CONTRIBUTOR | null | false | {
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} | Added XOR Question Answering Dataset. The link to the dataset can be found [here](https://nlp.cs.washington.edu/xorqa/)
- [x] Followed the instructions in CONTRIBUTING.md
- [x] Ran the tests successfully
- [x] Created the dummy data | {
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https://api.github.com/repos/huggingface/datasets/issues/920 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/920/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/920/comments | https://api.github.com/repos/huggingface/datasets/issues/920/events | https://github.com/huggingface/datasets/pull/920 | 753,445,747 | MDExOlB1bGxSZXF1ZXN0NTI5NTIzMTgz | 920 | add dream dataset | {
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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'... | 1,606,740,014,000 | 1,607,013,912,000 | 1,606,923,552,000 | MEMBER | null | false | {
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} | Adding Dream: a Dataset and for Dialogue-Based Reading Comprehension
More details:
https://dataset.org/dream/
https://github.com/nlpdata/dream | {
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https://api.github.com/repos/huggingface/datasets/issues/919 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/919/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/919/comments | https://api.github.com/repos/huggingface/datasets/issues/919/events | https://github.com/huggingface/datasets/issues/919 | 753,434,472 | MDU6SXNzdWU3NTM0MzQ0NzI= | 919 | wrong length with datasets | {
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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 "
] | 1,606,739,019,000 | 1,606,739,847,000 | 1,606,739,846,000 | CONTRIBUTOR | null | null | null | Hi
I have a MRPC dataset which I convert it to seq2seq format, then this is of this format:
`Dataset(features: {'src_texts': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 10)
`
I feed it to a dataloader:
```
dataloader = DataLoader(
train_dataset,
batch_size=self.args.train_batch_size,
sampler=train_sampler,
collate_fn=self.data_collator,
drop_last=self.args.dataloader_drop_last,
num_workers=self.args.dataloader_num_workers,
)
```
now if I type len(dataloader) this is 1, which is wrong, and this needs to be 10. could you assist me please? thanks
| {
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https://api.github.com/repos/huggingface/datasets/issues/918 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/918/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/918/comments | https://api.github.com/repos/huggingface/datasets/issues/918/events | https://github.com/huggingface/datasets/pull/918 | 753,397,440 | MDExOlB1bGxSZXF1ZXN0NTI5NDgzOTk4 | 918 | Add conll2002 | {
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} | Adding the Conll2002 dataset for NER.
More info here : https://www.clips.uantwerpen.be/conll2002/ner/
### Checkbox
- [x] Create the dataset script `/datasets/my_dataset/my_dataset.py` using the template
- [x] Fill the `_DESCRIPTION` and `_CITATION` variables
- [x] Implement `_infos()`, `_split_generators()` and `_generate_examples()`
- [x] Make sure that the `BUILDER_CONFIGS` class attribute is filled with the different configurations of the dataset and that the `BUILDER_CONFIG_CLASS` is specified if there is a custom config class.
- [x] Generate the metadata file `dataset_infos.json` for all configurations
- [x] Generate the dummy data `dummy_data.zip` files to have the dataset script tested and that they don't weigh too much (<50KB)
- [x] Add the dataset card `README.md` using the template : fill the tags and the various paragraphs
- [x] Both tests for the real data and the dummy data pass.
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https://api.github.com/repos/huggingface/datasets/issues/917 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/917/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/917/comments | https://api.github.com/repos/huggingface/datasets/issues/917/events | https://github.com/huggingface/datasets/pull/917 | 753,391,591 | MDExOlB1bGxSZXF1ZXN0NTI5NDc5MTIy | 917 | Addition of Concode Dataset | {
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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.No... | 1,606,735,259,000 | 1,609,210,536,000 | 1,609,210,536,000 | CONTRIBUTOR | null | false | {
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} | ##Overview
Concode Dataset contains pairs of Nl Queries and the corresponding Code.(Contextual Code Generation)
Reference Links
Paper Link = https://arxiv.org/pdf/1904.09086.pdf
Github Link =https://github.com/microsoft/CodeXGLUE/tree/main/Text-Code/text-to-code | {
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https://api.github.com/repos/huggingface/datasets/issues/916 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/916/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/916/comments | https://api.github.com/repos/huggingface/datasets/issues/916/events | https://github.com/huggingface/datasets/pull/916 | 753,376,643 | MDExOlB1bGxSZXF1ZXN0NTI5NDY3MTkx | 916 | Add Swedish NER Corpus | {
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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 `[Mo... | 1,606,733,991,000 | 1,606,878,650,000 | 1,606,878,649,000 | MEMBER | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/915 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/915/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/915/comments | https://api.github.com/repos/huggingface/datasets/issues/915/events | https://github.com/huggingface/datasets/issues/915 | 753,118,481 | MDU6SXNzdWU3NTMxMTg0ODE= | 915 | Shall we change the hashing to encoding to reduce potential replicated cache files? | {
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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 equiv... | 1,606,708,246,000 | 1,608,786,709,000 | null | NONE | null | null | null | Hi there. For now, we are using `xxhash` to hash the transformations to fingerprint and we will save a copy of the processed dataset to disk if there is a new hash value. However, there are some transformations that are idempotent or commutative to each other. I think that encoding the transformation chain as the fingerprint may help in those cases, for example, use `base64.urlsafe_b64encode`. In this way, before we want to save a new copy, we can decode the transformation chain and normalize it to prevent omit potential reuse. As the main targets of this project are the really large datasets that cannot be loaded entirely in memory, I believe it would save a lot of time if we can avoid some write.
If you have interest in this, I'd love to help :). | {
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https://api.github.com/repos/huggingface/datasets/issues/914 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/914/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/914/comments | https://api.github.com/repos/huggingface/datasets/issues/914/events | https://github.com/huggingface/datasets/pull/914 | 752,956,106 | MDExOlB1bGxSZXF1ZXN0NTI5MTM2Njk3 | 914 | Add list_github_datasets api for retrieving dataset name list in github repo | {
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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... | 1,606,668,135,000 | 1,606,893,676,000 | 1,606,893,676,000 | NONE | null | false | {
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} | Thank you for your great effort on unifying data processing for NLP!
This pr is trying to add a new api `list_github_datasets` in the `inspect` module. The reason for it is that the current `list_datasets` api need to access https://huggingface.co/api/datasets to get a large json. However, this connection can be really slow... (I was visiting from China) and from my own experience, most of the time `requests.get` failed to download the whole json after a long wait and will trigger fault in `r.json()`.
I also noticed that the current implementation will first try to download from github, which makes me be able to smoothly run `load_dataset('squad')` in the example.
Therefore, I think it would be better if we can have an api to get the list of datasets that are available on github, and it will also improve newcomers' experience (it is a little frustrating if one cannot successfully run the first function in the README example.) before we have faster source for huggingface.co.
As for the implementation, I've added a `dataset_infos.json` file under the `datasets` folder, and it has the following structure:
```json
{
"id": "aeslc",
"folder": "datasets/aeslc",
"dataset_infos": "datasets/aeslc/dataset_infos.json"
},
...
{
"id": "json",
"folder": "datasets/json"
},
...
```
The script I used to get this file is:
```python
import json
import os
DATASETS_BASE_DIR = "/root/datasets"
DATASET_INFOS_JSON = "dataset_infos.json"
datasets = []
for item in os.listdir(os.path.join(DATASETS_BASE_DIR, "datasets")):
if os.path.isdir(os.path.join(DATASETS_BASE_DIR, "datasets", item)):
datasets.append(item)
datasets.sort()
total_ds_info = []
for ds in datasets:
ds_dir = os.path.join("datasets", ds)
ds_info_dir = os.path.join(ds_dir, DATASET_INFOS_JSON)
if os.path.isfile(os.path.join(DATASETS_BASE_DIR, ds_info_dir)):
total_ds_info.append({"id": ds,
"folder": ds_dir,
"dataset_infos": ds_info_dir})
else:
total_ds_info.append({"id": ds,
"folder": ds_dir})
with open(DATASET_INFOS_JSON, "w") as f:
json.dump(total_ds_info, f)
```
The new `dataset_infos.json` was saved as a formated json so that it will be easy to add new dataset.
When calling `list_github_datasets`, the user will get the list of dataset names in this github repo and if `with_details` is set to be `True`, they can get the url of specific dataset info.
Thank you for your time on reviewing this pr :). | {
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https://api.github.com/repos/huggingface/datasets/issues/913 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/913/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/913/comments | https://api.github.com/repos/huggingface/datasets/issues/913/events | https://github.com/huggingface/datasets/pull/913 | 752,892,020 | MDExOlB1bGxSZXF1ZXN0NTI5MDkyOTc3 | 913 | My new dataset PEC | {
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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 exa... | 1,606,648,237,000 | 1,606,819,313,000 | 1,606,819,313,000 | CONTRIBUTOR | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/911 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/911/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/911/comments | https://api.github.com/repos/huggingface/datasets/issues/911/events | https://github.com/huggingface/datasets/issues/911 | 752,806,215 | MDU6SXNzdWU3NTI4MDYyMTU= | 911 | datasets module not found | {
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"nvm, I'd made an assumption that the library gets installed with transformers. "
] | 1,606,613,055,000 | 1,606,660,389,000 | 1,606,660,389,000 | NONE | null | null | null | Currently, running `from datasets import load_dataset` will throw a `ModuleNotFoundError: No module named 'datasets'` error.
| {
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- **Name:** *name of the dataset*
- **Description:** *short description of the dataset (or link to social media or blog post)*
- **Paper:** *link to the dataset paper if available*
- **Data:** *link to the Github repository or current dataset location*
- **Motivation:** *what are some good reasons to have this dataset*
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html). | {
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"> 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/mas... | 1,606,521,260,000 | 1,607,360,183,000 | 1,607,360,183,000 | CONTRIBUTOR | null | false | {
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this PR adds "A Finnish News Corpus for Named Entity Recognition" as new `finer` dataset.
The dataset is described in [this paper](https://arxiv.org/abs/1908.04212). The data is publicly available in [this GitHub](https://github.com/mpsilfve/finer-data).
Notice: they provide two testsets. The additional test dataset taken from Wikipedia is named as "test_wikipedia" split. | {
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https://api.github.com/repos/huggingface/datasets/issues/908 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/908/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/908/comments | https://api.github.com/repos/huggingface/datasets/issues/908/events | https://github.com/huggingface/datasets/pull/908 | 752,428,652 | MDExOlB1bGxSZXF1ZXN0NTI4NzUzMjcz | 908 | Add dependency on black for tests | {
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"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..."
] | 1,606,504,368,000 | 1,606,513,613,000 | 1,606,513,612,000 | MEMBER | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/907 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/907/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/907/comments | https://api.github.com/repos/huggingface/datasets/issues/907/events | https://github.com/huggingface/datasets/pull/907 | 752,422,351 | MDExOlB1bGxSZXF1ZXN0NTI4NzQ4ODMx | 907 | Remove os.path.join from all URLs | {
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cc @albertvillanova | {
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"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 `o... | 1,606,498,708,000 | 1,606,690,117,000 | 1,606,690,116,000 | MEMBER | null | false | {
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} | Following #903 @albertvillanova noticed that there are sometimes bad usage of `os.path.join` in datasets scripts to create URLS. However this should be avoided since it doesn't work on windows.
I'm suggesting a test to make sure we that all the urls don't have backslashes in them in the datasets scripts.
The tests works by adding a callback feature to the MockDownloadManager used to test the dataset scripts. In a download callback I just make sure that the url is valid. | {
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https://api.github.com/repos/huggingface/datasets/issues/904 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/904/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/904/comments | https://api.github.com/repos/huggingface/datasets/issues/904/events | https://github.com/huggingface/datasets/pull/904 | 752,372,743 | MDExOlB1bGxSZXF1ZXN0NTI4NzA5NTUx | 904 | Very detailed step-by-step on how to add a dataset | {
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"Awesome! Thanks @lhoestq "
] | 1,606,495,521,000 | 1,606,730,187,000 | 1,606,730,186,000 | MEMBER | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/903 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/903/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/903/comments | https://api.github.com/repos/huggingface/datasets/issues/903/events | https://github.com/huggingface/datasets/pull/903 | 752,360,614 | MDExOlB1bGxSZXF1ZXN0NTI4Njk5NDQ3 | 903 | Fix URL with backslash in Windows | {
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"@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 happ... | 1,606,494,384,000 | 1,606,500,286,000 | 1,606,500,286,000 | MEMBER | null | false | {
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In general, `os.path.join` should be avoided to generate URLs. | {
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Fix #900 | {
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"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... | 1,606,481,635,000 | 1,606,673,365,000 | 1,606,673,331,000 | CONTRIBUTOR | null | false | {
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} | ## Overview
The NL2Bash data contains over 10,000 instances of linux shell commands and their corresponding natural language descriptions provided by experts, from the Tellina system. The dataset features 100+ commonly used shell utilities.
## Footnotes
The following dataset marks the first ML on source code related Dataset in datasets module. It'll be really useful as a lot of the research direction involves Transformer Based Model.
Thanks.
### Reference Links
> Paper Link = https://arxiv.org/pdf/1802.08979.pdf
> Github Link = https://github.com/TellinaTool/nl2bash
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"Thanks for reporting ! I'm looking into it."
] | 1,606,479,533,000 | 1,606,690,133,000 | 1,606,690,133,000 | NONE | null | null | null | Hello,
I'm having issue with loading a dataset with a custom `cache_dir`. Despite specifying the output dir, it is still downloaded to
`~/.cache`.
## Environment info
- `datasets` version: 1.1.3
- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1
- Python version: 3.7.3
## The code I'm running:
```python
import datasets
from pathlib import Path
validation_dataset = datasets.load_dataset("natural_questions", split="validation[:5%]", cache_dir=Path("./data"))
```
## The output:
* The dataset is downloaded to my home directory's `.cache`
* A new empty directory named "`natural_questions` is created in the specified directory `.data`
* `tree data` in the shell outputs:
```
data
└── natural_questions
└── default
└── 0.0.2
3 directories, 0 files
```
The output:
```
Downloading: 8.61kB [00:00, 5.11MB/s]
Downloading: 13.6kB [00:00, 7.89MB/s]
Using custom data configuration default
Downloading and preparing dataset natural_questions/default (download: 41.97 GiB, generated: 92.95 GiB, post-processed: Unknown size, total: 134.92 GiB) to ./data/natural_questions/default/0.0.2/867dbbaf9137c1b8
3ecb19f5eb80559e1002ea26e702c6b919cfa81a17a8c531...
Downloading: 100%|██████████████████████████████████████████████████| 13.6k/13.6k [00:00<00:00, 1.51MB/s]
Downloading: 7%|███▎ | 6.70G/97.4G [03:46<1:37:05, 15.6MB/s]
```
## Expected behaviour:
The dataset "Natural Questions" should be downloaded to the directory "./data"
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"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 "
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Seems like automatic dummy-data generation doesn't work if the builder is a `ArrowBasedBuilder`, do you think you could take a look @lhoestq ? | {
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"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/viewe... | 1,606,468,474,000 | 1,635,671,521,000 | 1,635,671,521,000 | CONTRIBUTOR | null | null | null | I was looking through the dataset viewer and I like it a lot. Version numbers, citation information, everything's there! I've spotted a few issues/bugs though:
- the URL is still under `nlp`, perhaps an alias for `datasets` can be made
- when I remove a **feature** (and the feature list is empty), I get an error. This is probably expected, but perhaps a better error message can be shown to the user
```bash
IndexError: list index out of range
Traceback:
File "/home/sasha/streamlit/lib/streamlit/ScriptRunner.py", line 322, in _run_script
exec(code, module.__dict__)
File "/home/sasha/nlp-viewer/run.py", line 316, in <module>
st.table(style)
File "/home/sasha/streamlit/lib/streamlit/DeltaGenerator.py", line 122, in wrapped_method
return dg._enqueue_new_element_delta(marshall_element, delta_type, last_index)
File "/home/sasha/streamlit/lib/streamlit/DeltaGenerator.py", line 367, in _enqueue_new_element_delta
rv = marshall_element(msg.delta.new_element)
File "/home/sasha/streamlit/lib/streamlit/DeltaGenerator.py", line 120, in marshall_element
return method(dg, element, *args, **kwargs)
File "/home/sasha/streamlit/lib/streamlit/DeltaGenerator.py", line 2944, in table
data_frame_proto.marshall_data_frame(data, element.table)
File "/home/sasha/streamlit/lib/streamlit/elements/data_frame_proto.py", line 54, in marshall_data_frame
_marshall_styles(proto_df.style, df, styler)
File "/home/sasha/streamlit/lib/streamlit/elements/data_frame_proto.py", line 73, in _marshall_styles
translated_style = styler._translate()
File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/pandas/io/formats/style.py", line 351, in _translate
* (len(clabels[0]) - len(hidden_columns))
```
- there seems to be **an encoding issue** in the default view, the dataset examples are shown as raw monospace text, without a decent encoding. That makes it hard to read for languages that use a lot of special characters. Take for instance the [cs-en WMT19 set](https://huggingface.co/nlp/viewer/?dataset=wmt19&config=cs-en). This problem goes away when you enable "List view", because then some syntax highlighteris used, and the special characters are coded correctly.
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https://api.github.com/repos/huggingface/datasets/issues/896 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/896/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/896/comments | https://api.github.com/repos/huggingface/datasets/issues/896/events | https://github.com/huggingface/datasets/pull/896 | 751,834,265 | MDExOlB1bGxSZXF1ZXN0NTI4MjcyMjc0 | 896 | Add template and documentation for dataset card | {
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} | This PR adds a template for dataset cards, as well as a guide to filling out the template and a completed example for the ELI5 dataset, building on the work of @mcmillanmajora
New pull requests adding datasets should now have a README.md file which serves both to hold the tags we will have to index the datasets and as a data statement.
The template is designed to be pretty extensive. The idea is that the person who uploads the dataset should put in all the basic information (at least the Dataset Description section) and whatever else they feel comfortable adding and leave the `[More Information Needed]` annotation everywhere else as a placeholder.
We will then work with @mcmillanmajora to involve the data authors more directly in filling out the remaining information.
Direct links to:
- [Documentation](https://github.com/yjernite/datasets/blob/add_dataset_card_doc/templates/README_guide.md)
- [Empty template](https://github.com/yjernite/datasets/blob/add_dataset_card_doc/templates/README.md)
- [ELI5 example](https://github.com/yjernite/datasets/blob/add_dataset_card_doc/datasets/eli5/README.md) | {
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https://api.github.com/repos/huggingface/datasets/issues/895 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/895/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/895/comments | https://api.github.com/repos/huggingface/datasets/issues/895/events | https://github.com/huggingface/datasets/pull/895 | 751,782,295 | MDExOlB1bGxSZXF1ZXN0NTI4MjMyMjU3 | 895 | Better messages regarding split naming | {
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} | I made explicit the error message when a bad split name is used.
Also I wanted to allow the `-` symbol for split names but actually this symbol is used to name the arrow files `{dataset_name}-{dataset_split}.arrow` so we should probably keep it this way, i.e. not allowing the `-` symbol in split names. Moreover in the future we might want to use `{dataset_name}-{dataset_split}-{shard_id}_of_{n_shards}.arrow` and reuse the `-` symbol. | {
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https://api.github.com/repos/huggingface/datasets/issues/894 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/894/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/894/comments | https://api.github.com/repos/huggingface/datasets/issues/894/events | https://github.com/huggingface/datasets/pull/894 | 751,734,905 | MDExOlB1bGxSZXF1ZXN0NTI4MTkzNzQy | 894 | Allow several tags sets | {
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"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)"
] | 1,606,410,253,000 | 1,620,239,057,000 | 1,606,508,149,000 | MEMBER | null | false | {
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} | Hi !
Currently we have three dataset cards : snli, cnn_dailymail and allocine.
For each one of those datasets a set of tag is defined. The set of tags contains fields like `multilinguality`, `task_ids`, `licenses` etc.
For certain datasets like `glue` for example, there exist several configurations: `sst2`, `mnli` etc. Therefore we should define one set of tags per configuration. However the current format used for tags only supports one set of tags per dataset.
In this PR I propose a simple change in the yaml format used for tags to allow for several sets of tags.
Let me know what you think, especially @julien-c let me know if it's good for you since it's going to be parsed by moon-landing | {
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https://api.github.com/repos/huggingface/datasets/issues/893 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/893/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/893/comments | https://api.github.com/repos/huggingface/datasets/issues/893/events | https://github.com/huggingface/datasets/pull/893 | 751,703,696 | MDExOlB1bGxSZXF1ZXN0NTI4MTY4NDgx | 893 | add metrec: arabic poetry dataset | {
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"@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- ... | 1,606,407,016,000 | 1,606,839,895,000 | 1,606,835,707,000 | CONTRIBUTOR | null | false | {
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"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... | 1,606,402,959,000 | 1,606,500,525,000 | 1,606,500,524,000 | MEMBER | null | false | {
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Since many datasets share a lot in common I think it's good to have a list of datasets scripts to get some inspiration from.
Let me know what you think, and if you have ideas of other datasets that we may add to this list, please let me know. | {
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"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 ... | 1,606,391,903,000 | 1,606,829,615,000 | 1,606,829,176,000 | CONTRIBUTOR | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/889 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/889/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/889/comments | https://api.github.com/repos/huggingface/datasets/issues/889/events | https://github.com/huggingface/datasets/pull/889 | 751,115,691 | MDExOlB1bGxSZXF1ZXN0NTI3NjkwODE2 | 889 | Optional per-dataset default config name | {
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"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 def... | 1,606,338,150,000 | 1,606,757,253,000 | 1,606,757,247,000 | CONTRIBUTOR | null | false | {
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} | This PR adds a `DEFAULT_CONFIG_NAME` class attribute to `DatasetBuilder`. This allows a dataset to have a specified default config name when a dataset has more than one config but the user does not specify it. For example, after defining `DEFAULT_CONFIG_NAME = "combined"` in PolyglotNER, a user can now do the following:
```python
ds = load_dataset("polyglot_ner")
```
which is equivalent to,
```python
ds = load_dataset("polyglot_ner", "combined")
```
In effect (for this particular dataset configuration), this means that if the user doesn't specify a language, they are given the combined dataset including all languages.
Since it doesn't always make sense to have a default config, this feature is opt-in. If `DEFAULT_CONFIG_NAME` is not defined and a user does not pass a config for a dataset with multiple configs available, a ValueError is raised like usual.
Let me know what you think about this approach @lhoestq @thomwolf and I'll add some documentation and define a default for some of our existing datasets. | {
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https://api.github.com/repos/huggingface/datasets/issues/888 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/888/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/888/comments | https://api.github.com/repos/huggingface/datasets/issues/888/events | https://github.com/huggingface/datasets/issues/888 | 750,944,422 | MDU6SXNzdWU3NTA5NDQ0MjI= | 888 | Nested lists are zipped unexpectedly | {
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"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 ... | 1,606,320,466,000 | 1,606,325,439,000 | 1,606,325,439,000 | CONTRIBUTOR | null | null | null | I might misunderstand something, but I expect that if I define:
```python
"top": datasets.features.Sequence({
"middle": datasets.features.Sequence({
"bottom": datasets.Value("int32")
})
})
```
And I then create an example:
```python
yield 1, {
"top": [{
"middle": [
{"bottom": 1},
{"bottom": 2}
]
}]
}
```
I then load my dataset:
```python
train = load_dataset("my dataset")["train"]
```
and expect to be able to access `data[0]["top"][0]["middle"][0]`.
That is not the case. Here is `data[0]` as JSON:
```json
{"top": {"middle": [{"bottom": [1, 2]}]}}
```
Clearly different than the thing I inputted.
```json
{"top": [{"middle": [{"bottom": 1},{"bottom": 2}]}]}
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/887 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/887/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/887/comments | https://api.github.com/repos/huggingface/datasets/issues/887/events | https://github.com/huggingface/datasets/issues/887 | 750,868,831 | MDU6SXNzdWU3NTA4Njg4MzE= | 887 | pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> | {
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"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 ... | 1,606,314,741,000 | 1,631,207,020,000 | null | CONTRIBUTOR | null | null | null | I set up a new dataset, with a sequence of arrays (really, I want to have an array of (None, 137, 2), and the first dimension is dynamic)
```python
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=datasets.Features(
{
"pose": datasets.features.Sequence(datasets.features.Array2D(shape=(137, 2), dtype="float32"))
}
),
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _generate_examples(self):
""" Yields examples. """
yield 1, {
"pose": [np.zeros(shape=(137, 2), dtype=np.float32)]
}
```
But this doesn't work -
> pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> | {
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