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"The checks failed again even if I didn't make any changes.",
"you just need to rebase from master to fix the CI :)",
"Sorry for the mess, I'm confused by the rebase and thus created a new branch."
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"Replaced by #1126"
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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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https://api.github.com/repos/huggingface/datasets/issues/938 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/938/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/938/comments | https://api.github.com/repos/huggingface/datasets/issues/938/events | https://github.com/huggingface/datasets/pull/938 | 753,940,979 | MDExOlB1bGxSZXF1ZXN0NTI5OTIxNzU5 | 938 | V-1.0.0 of isizulu_ner_corpus | {
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"closing since it's been added in #957 "
] | 1,606,788,272,000 | 1,606,865,676,000 | 1,606,865,676,000 | CONTRIBUTOR | null | 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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https://api.github.com/repos/huggingface/datasets/issues/931 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/931/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/931/comments | https://api.github.com/repos/huggingface/datasets/issues/931/events | https://github.com/huggingface/datasets/pull/931 | 753,818,193 | MDExOlB1bGxSZXF1ZXN0NTI5ODIzMDYz | 931 | [WIP] complex_webqa - Error zipfile.BadZipFile: Bad CRC-32 | {
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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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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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} | A new dataset PEC published in EMNLP 2020. | {
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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..."
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cc @albertvillanova | {
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https://api.github.com/repos/huggingface/datasets/issues/905 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/905/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/905/comments | https://api.github.com/repos/huggingface/datasets/issues/905/events | https://github.com/huggingface/datasets/pull/905 | 752,395,456 | MDExOlB1bGxSZXF1ZXN0NTI4NzI3OTEy | 905 | Disallow backslash in urls | {
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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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"Awesome! Thanks @lhoestq "
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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
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File "/home/sasha/streamlit/lib/streamlit/DeltaGenerator.py", line 120, in marshall_element
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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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} | 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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"@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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} | I started making a small list of various datasets of reference in the documentation.
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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"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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"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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https://api.github.com/repos/huggingface/datasets/issues/886 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/886/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/886/comments | https://api.github.com/repos/huggingface/datasets/issues/886/events | https://github.com/huggingface/datasets/pull/886 | 750,829,314 | MDExOlB1bGxSZXF1ZXN0NTI3NDU1MDU5 | 886 | Fix wikipedia custom config | {
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"I think this issue is still not resolve yet. Please check my comment in the following issue, thanks.\r\n[#577](https://github.com/huggingface/datasets/issues/577#issuecomment-868122769)"
] | 1,606,311,852,000 | 1,624,598,656,000 | 1,606,318,933,000 | MEMBER | null | false | {
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} | It should be possible to use the wikipedia dataset with any `language` and `date`.
However it was not working as noticed in #784 . Indeed the custom wikipedia configurations were not enabled for some reason.
I fixed that and was able to run
```python
from datasets import load_dataset
load_dataset("./datasets/wikipedia", language="zh", date="20201120", beam_runner='DirectRunner')
```
cc @stvhuang @SamuelCahyawijaya
Fix #784 | {
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https://api.github.com/repos/huggingface/datasets/issues/885 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/885/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/885/comments | https://api.github.com/repos/huggingface/datasets/issues/885/events | https://github.com/huggingface/datasets/issues/885 | 750,789,052 | MDU6SXNzdWU3NTA3ODkwNTI= | 885 | Very slow cold-start | {
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"Good point!",
"Yes indeed. We can probably improve that by using lazy imports",
"#1690 added fast start-up of the library "
] | 1,606,308,478,000 | 1,610,537,485,000 | 1,610,537,485,000 | CONTRIBUTOR | null | null | null | Hi,
I expect when importing `datasets` that nothing major happens in the background, and so the import should be insignificant.
When I load a metric, or a dataset, its fine that it takes time.
The following ranges from 3 to 9 seconds:
```
python -m timeit -n 1 -r 1 'from datasets import load_dataset'
```
edit:
sorry for the mis-tag, not sure how I added it. | {
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https://api.github.com/repos/huggingface/datasets/issues/884 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/884/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/884/comments | https://api.github.com/repos/huggingface/datasets/issues/884/events | https://github.com/huggingface/datasets/pull/884 | 749,862,034 | MDExOlB1bGxSZXF1ZXN0NTI2NjA5MDc1 | 884 | Auto generate dummy data | {
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"I took your comments into account.\r\nAlso now after compressing the dummy_data.zip file it runs a dummy data test (=make sure each split has at least 1 example using the dummy data)",
"I just tested the tool with some datasets and found out that it's not working for datasets that download files using `download_... | 1,606,235,494,000 | 1,606,400,327,000 | 1,606,400,326,000 | MEMBER | null | false | {
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} | When adding a new dataset to the library, dummy data creation can take some time.
To make things easier I added a command line tool that automatically generates dummy data when possible.
The tool only supports certain data files types: txt, csv, tsv, jsonl, json and xml.
Here are some examples:
```
python datasets-cli dummy_data ./datasets/snli --auto_generate
python datasets-cli dummy_data ./datasets/squad --auto_generate --json_field data
python datasets-cli dummy_data ./datasets/iwslt2017 --auto_generate --xml_tag seg --match_text_files "train*" --n_lines 15
# --xml_tag seg => each sample corresponds to a "seg" tag in the xml tree
# --match_text_files "train*" => also match text files that don't have a proper text file extension (no suffix like ".txt" for example)
# --n_lines 15 => some text files have headers so we have to use at least 15 lines
```
and here is the command usage:
```
usage: datasets-cli <command> [<args>] dummy_data [-h] [--auto_generate]
[--n_lines N_LINES]
[--json_field JSON_FIELD]
[--xml_tag XML_TAG]
[--match_text_files MATCH_TEXT_FILES]
[--keep_uncompressed]
[--cache_dir CACHE_DIR]
path_to_dataset
positional arguments:
path_to_dataset Path to the dataset (example: ./datasets/squad)
optional arguments:
-h, --help show this help message and exit
--auto_generate Try to automatically generate dummy data
--n_lines N_LINES Number of lines or samples to keep when auto-
generating dummy data
--json_field JSON_FIELD
Optional, json field to read the data from when auto-
generating dummy data. In the json data files, this
field must point to a list of samples as json objects
(ex: the 'data' field for squad-like files)
--xml_tag XML_TAG Optional, xml tag name of the samples inside the xml
files when auto-generating dummy data.
--match_text_files MATCH_TEXT_FILES
Optional, a comma separated list of file patterns that
looks for line-by-line text files other than *.txt or
*.csv. Example: --match_text_files *.label
--keep_uncompressed Don't compress the dummy data folders when auto-
generating dummy data. Useful for debugging for to do
manual adjustements before compressing.
--cache_dir CACHE_DIR
Cache directory to download and cache files when auto-
generating dummy data
```
The command generates all the necessary `dummy_data.zip` files (one per config).
How it works:
- it runs the split_generators() method of the dataset script to download the original data files
- when downloading it records a mapping between the downloaded files and the corresponding expected dummy data files paths
- then for each data file it creates the dummy data file keeping only the first samples (the strategy depends on the type of file)
- finally it compresses the dummy data folders into dummy_zip files ready for dataset tests
Let me know if that makes sense or if you have ideas to improve this tool !
I also added a unit test. | {
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https://api.github.com/repos/huggingface/datasets/issues/883 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/883/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/883/comments | https://api.github.com/repos/huggingface/datasets/issues/883/events | https://github.com/huggingface/datasets/issues/883 | 749,750,801 | MDU6SXNzdWU3NDk3NTA4MDE= | 883 | Downloading/caching only a part of a datasets' dataset. | {
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"Not at the moment but we could likely support this feature.",
"?",
"I think it would be a very helpful feature, because sometimes one only wants to evaluate models on the dev set, and the whole training data may be many times bigger.\r\nThis makes the task impossible with limited memory resources."
] | 1,606,227,918,000 | 1,606,485,115,000 | null | NONE | null | null | null | Hi,
I want to use the validation data *only* (of natural question).
I don't want to have the whole dataset cached in my machine, just the dev set.
Is this possible? I can't find a way to do it in the docs.
Thank you,
Sapir | {
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https://api.github.com/repos/huggingface/datasets/issues/882 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/882/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/882/comments | https://api.github.com/repos/huggingface/datasets/issues/882/events | https://github.com/huggingface/datasets/pull/882 | 749,662,188 | MDExOlB1bGxSZXF1ZXN0NTI2NDQyMjA2 | 882 | Update README.md | {
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https://api.github.com/repos/huggingface/datasets/issues/881 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/881/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/881/comments | https://api.github.com/repos/huggingface/datasets/issues/881/events | https://github.com/huggingface/datasets/pull/881 | 749,548,107 | MDExOlB1bGxSZXF1ZXN0NTI2MzQ5MDM2 | 881 | Use GCP download url instead of tensorflow custom download for boolq | {
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} | BoolQ is a dataset that used tf.io.gfile.copy to download the file from a GCP bucket.
It prevented the dataset to be downloaded twice because of a FileAlreadyExistsError.
Even though the error could be fixed by providing `overwrite=True` to the tf.io.gfile.copy call, I changed the script to use GCP download urls and use regular downloads instead and remove the tensorflow dependency.
Fix #875 | {
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https://api.github.com/repos/huggingface/datasets/issues/880 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/880/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/880/comments | https://api.github.com/repos/huggingface/datasets/issues/880/events | https://github.com/huggingface/datasets/issues/880 | 748,949,606 | MDU6SXNzdWU3NDg5NDk2MDY= | 880 | Add SQA | {
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"I’ll take this one to test the workflow for the sprint next week cc @yjernite @lhoestq ",
"@thomwolf here's a slightly adapted version of the code from the [official Tapas repository](https://github.com/google-research/tapas/blob/master/tapas/utils/interaction_utils.py) that is used to turn the `answer_coordinat... | 1,606,149,115,000 | 1,608,731,904,000 | 1,608,731,903,000 | CONTRIBUTOR | null | null | null | ## Adding a Dataset
- **Name:** SQA (Sequential Question Answering) by Microsoft.
- **Description:** The SQA dataset was created to explore the task of answering sequences of inter-related questions on HTML tables. It has 6,066 sequences with 17,553 questions in total.
- **Paper:** https://www.microsoft.com/en-us/research/publication/search-based-neural-structured-learning-sequential-question-answering/
- **Data:** https://www.microsoft.com/en-us/download/details.aspx?id=54253
- **Motivation:** currently, the [Tapas](https://ai.googleblog.com/2020/04/using-neural-networks-to-find-answers.html) algorithm by Google AI is being added to the Transformers library (see https://github.com/huggingface/transformers/pull/8113). It would be great to use that model in combination with this dataset, on which it achieves SOTA results (average question accuracy of 0.71).
Note 1: this dataset actually consists of 2 types of files:
1) TSV files, containing the questions, answer coordinates and answer texts (for training, dev and test)
2) a folder of csv files, which contain the actual tabular data
Note 2: if you download the dataset straight from the download link above, then you will see that the `answer_coordinates` and `answer_text` columns are string lists of string tuples and strings respectively, which is not ideal. It would be better to make them true Python lists of tuples and strings respectively (using `ast.literal_eval`), before uploading them to the HuggingFace hub.
Adding this would be great! Then we could possibly also add [WTQ (WikiTable Questions)](https://github.com/ppasupat/WikiTableQuestions) and [TabFact (Tabular Fact Checking)](https://github.com/wenhuchen/Table-Fact-Checking) on which TAPAS also achieves state-of-the-art results. Note that the TAPAS algorithm requires these datasets to first be converted into the SQA format.
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
| {
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https://api.github.com/repos/huggingface/datasets/issues/879 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/879/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/879/comments | https://api.github.com/repos/huggingface/datasets/issues/879/events | https://github.com/huggingface/datasets/issues/879 | 748,848,847 | MDU6SXNzdWU3NDg4NDg4NDc= | 879 | boolq does not load | {
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"Hi ! It runs on my side without issues. I tried\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"boolq\")\r\n```\r\n\r\nWhat version of datasets and tensorflow are your runnning ?\r\nAlso if you manage to get a minimal reproducible script (on google colab for example) that would be useful.",
"... | 1,606,141,708,000 | 1,606,485,071,000 | null | CONTRIBUTOR | null | null | null | Hi
I am getting these errors trying to load boolq thanks
Traceback (most recent call last):
File "test.py", line 5, in <module>
data = AutoTask().get("boolq").get_dataset("train", n_obs=10)
File "/remote/idiap.svm/user.active/rkarimi/dev/internship/seq2seq/tasks/tasks.py", line 42, in get_dataset
dataset = self.load_dataset(split=split)
File "/remote/idiap.svm/user.active/rkarimi/dev/internship/seq2seq/tasks/tasks.py", line 38, in load_dataset
return datasets.load_dataset(self.task.name, split=split)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/load.py", line 611, in load_dataset
ignore_verifications=ignore_verifications,
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 476, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 531, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File " /idiap/home/rkarimi/.cache/huggingface/modules/datasets_modules/datasets/boolq/2987db1f15deaa19500ae24de560eabeaf1f8ef51df88c0470beeec72943bf11/boolq.py", line 74, in _split_generators
downloaded_files = dl_manager.download_custom(urls_to_download, tf.io.gfile.copy)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 150, in download_custom
get_from_cache(url, cache_dir=cache_dir, local_files_only=True, use_etag=False)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 472, in get_from_cache
f"Cannot find the requested files in the cached path at {cache_path} and outgoing traffic has been"
FileNotFoundError: Cannot find the requested files in the cached path at /idiap/home/rkarimi/.cache/huggingface/datasets/eaee069e38f6ceaa84de02ad088c34e63ec97671f2cd1910ddb16b10dc60808c and outgoing traffic has been disabled. To enable file online look-ups, set 'local_files_only' to False.
| {
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https://api.github.com/repos/huggingface/datasets/issues/878 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/878/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/878/comments | https://api.github.com/repos/huggingface/datasets/issues/878/events | https://github.com/huggingface/datasets/issues/878 | 748,621,981 | MDU6SXNzdWU3NDg2MjE5ODE= | 878 | Loading Data From S3 Path in Sagemaker | {
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"This would be a neat feature",
"> neat feature\r\n\r\nI dint get these clearly, can you please elaborate like how to work on these ",
"It could maybe work almost out of the box just by using `cached_path` in the text/csv/json scripts, no?",
"Thanks thomwolf and julien-c\r\n\r\nI'm still confusion on what you... | 1,606,123,042,000 | 1,608,717,188,000 | null | NONE | null | null | null | In Sagemaker Im tring to load the data set from S3 path as follows
`train_path = 's3://xxxxxxxxxx/xxxxxxxxxx/train.csv'
valid_path = 's3://xxxxxxxxxx/xxxxxxxxxx/validation.csv'
test_path = 's3://xxxxxxxxxx/xxxxxxxxxx/test.csv'
data_files = {}
data_files["train"] = train_path
data_files["validation"] = valid_path
data_files["test"] = test_path
extension = train_path.split(".")[-1]
datasets = load_dataset(extension, data_files=data_files, s3_enabled=True)
print(datasets)`
I getting an error of
`algo-1-7plil_1 | File "main.py", line 21, in <module>
algo-1-7plil_1 | datasets = load_dataset(extension, data_files=data_files)
algo-1-7plil_1 | File "/opt/conda/lib/python3.6/site-packages/datasets/load.py", line 603, in load_dataset
algo-1-7plil_1 | **config_kwargs,
algo-1-7plil_1 | File "/opt/conda/lib/python3.6/site-packages/datasets/builder.py", line 155, in __init__
algo-1-7plil_1 | **config_kwargs,
algo-1-7plil_1 | File "/opt/conda/lib/python3.6/site-packages/datasets/builder.py", line 305, in _create_builder_config
algo-1-7plil_1 | m.update(str(os.path.getmtime(data_file)))
algo-1-7plil_1 | File "/opt/conda/lib/python3.6/genericpath.py", line 55, in getmtime
algo-1-7plil_1 | return os.stat(filename).st_mtime
algo-1-7plil_1 | FileNotFoundError: [Errno 2] No such file or directory: 's3://lsmv-sagemaker/pubmedbert/test.csv`
But when im trying with pandas , it is able to load from S3
Does the datasets library support S3 path to load | {
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https://api.github.com/repos/huggingface/datasets/issues/877 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/877/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/877/comments | https://api.github.com/repos/huggingface/datasets/issues/877/events | https://github.com/huggingface/datasets/issues/877 | 748,234,438 | MDU6SXNzdWU3NDgyMzQ0Mzg= | 877 | DataLoader(datasets) become more and more slowly within iterations | {
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"Hi ! Thanks for reporting.\r\nDo you have the same slowdown when you iterate through the raw dataset object as well ? (no dataloader)\r\nIt would be nice to know whether it comes from the dataloader or not",
"> Hi ! Thanks for reporting.\r\n> Do you have the same slowdown when you iterate through the raw dataset... | 1,606,048,870,000 | 1,606,664,712,000 | 1,606,664,712,000 | NONE | null | null | null | Hello, when I for loop my dataloader, the loading speed is becoming more and more slowly!
```
dataset = load_from_disk(dataset_path) # around 21,000,000 lines
lineloader = tqdm(DataLoader(dataset, batch_size=1))
for idx, line in enumerate(lineloader):
# do some thing for each line
```
In the begining, the loading speed is around 2000it/s, but after 1 minutes later, the speed is much slower, just around 800it/s.
And when I set `num_workers=4` in DataLoader, the loading speed is much lower, just 130it/s.
Could you please help me with this problem?
Thanks a lot! | {
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https://api.github.com/repos/huggingface/datasets/issues/876 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/876/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/876/comments | https://api.github.com/repos/huggingface/datasets/issues/876/events | https://github.com/huggingface/datasets/issues/876 | 748,195,104 | MDU6SXNzdWU3NDgxOTUxMDQ= | 876 | imdb dataset cannot be loaded | {
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"It looks like there was an issue while building the imdb dataset.\r\nCould you provide more information about your OS and the version of python and `datasets` ?\r\n\r\nAlso could you try again with \r\n```python\r\ndataset = datasets.load_dataset(\"imdb\", split=\"train\", download_mode=\"force_redownload\")\r\n``... | 1,606,033,483,000 | 1,637,924,836,000 | 1,608,831,527,000 | CONTRIBUTOR | null | null | null | Hi
I am trying to load the imdb train dataset
`dataset = datasets.load_dataset("imdb", split="train")`
getting following errors, thanks for your help
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/load.py", line 611, in load_dataset
ignore_verifications=ignore_verifications,
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 476, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 558, in _download_and_prepare
verify_splits(self.info.splits, split_dict)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/info_utils.py", line 73, in verify_splits
raise NonMatchingSplitsSizesError(str(bad_splits))
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='test', num_bytes=32660064, num_examples=25000, dataset_name='imdb'), 'recorded': SplitInfo(name='test', num_bytes=26476338, num_examples=20316, dataset_name='imdb')}, {'expected': SplitInfo(name='train', num_bytes=33442202, num_examples=25000, dataset_name='imdb'), 'recorded': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='imdb')}, {'expected': SplitInfo(name='unsupervised', num_bytes=67125548, num_examples=50000, dataset_name='imdb'), 'recorded': SplitInfo(name='unsupervised', num_bytes=0, num_examples=0, dataset_name='imdb')}]
>>> dataset = datasets.load_dataset("imdb", split="train")
```
| {
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https://api.github.com/repos/huggingface/datasets/issues/875 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/875/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/875/comments | https://api.github.com/repos/huggingface/datasets/issues/875/events | https://github.com/huggingface/datasets/issues/875 | 748,194,311 | MDU6SXNzdWU3NDgxOTQzMTE= | 875 | bug in boolq dataset loading | {
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"I just opened a PR to fix this.\r\nThanks for reporting !"
] | 1,606,033,114,000 | 1,606,212,753,000 | 1,606,212,753,000 | CONTRIBUTOR | null | null | null | Hi
I am trying to load boolq dataset:
```
import datasets
datasets.load_dataset("boolq")
```
I am getting the following errors, thanks for your help
```
>>> import datasets
2020-11-22 09:16:30.070332: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2020-11-22 09:16:30.070389: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
>>> datasets.load_dataset("boolq")
cahce dir /idiap/temp/rkarimi/cache_home/datasets
cahce dir /idiap/temp/rkarimi/cache_home/datasets
Using custom data configuration default
Downloading and preparing dataset boolq/default (download: 8.36 MiB, generated: 7.47 MiB, post-processed: Unknown size, total: 15.83 MiB) to /idiap/temp/rkarimi/cache_home/datasets/boolq/default/0.1.0/2987db1f15deaa19500ae24de560eabeaf1f8ef51df88c0470beeec72943bf11...
cahce dir /idiap/temp/rkarimi/cache_home/datasets
cahce dir /idiap/temp/rkarimi/cache_home/datasets/downloads
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/load.py", line 611, in load_dataset
ignore_verifications=ignore_verifications,
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 476, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 531, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File " /idiap/home/rkarimi/.cache/huggingface/modules/datasets_modules/datasets/boolq/2987db1f15deaa19500ae24de560eabeaf1f8ef51df88c0470beeec72943bf11/boolq.py", line 74, in _split_generators
downloaded_files = dl_manager.download_custom(urls_to_download, tf.io.gfile.copy)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 149, in download_custom
custom_download(url, path)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/tensorflow/python/lib/io/file_io.py", line 516, in copy_v2
compat.path_to_bytes(src), compat.path_to_bytes(dst), overwrite)
tensorflow.python.framework.errors_impl.AlreadyExistsError: file already exists
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/874 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/874/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/874/comments | https://api.github.com/repos/huggingface/datasets/issues/874/events | https://github.com/huggingface/datasets/issues/874 | 748,193,140 | MDU6SXNzdWU3NDgxOTMxNDA= | 874 | trec dataset unavailable | {
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"This was fixed in #740 \r\nCould you try to update `datasets` and try again ?",
"This has been fixed in datasets 1.1.3"
] | 1,606,032,576,000 | 1,606,485,402,000 | 1,606,485,402,000 | CONTRIBUTOR | null | null | null | Hi
when I try to load the trec dataset I am getting these errors, thanks for your help
`datasets.load_dataset("trec", split="train")
`
```
File "<stdin>", line 1, in <module>
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/load.py", line 611, in load_dataset
ignore_verifications=ignore_verifications,
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 476, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 531, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File " /idiap/home/rkarimi/.cache/huggingface/modules/datasets_modules/datasets/trec/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7/trec.py", line 140, in _split_generators
dl_files = dl_manager.download_and_extract(_URLs)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 179, in download
num_proc=download_config.num_proc,
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 225, in map_nested
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 225, in <listcomp>
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 163, in _single_map_nested
return function(data_struct)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 477, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://cogcomp.org/Data/QA/QC/train_5500.label
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/873 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/873/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/873/comments | https://api.github.com/repos/huggingface/datasets/issues/873/events | https://github.com/huggingface/datasets/issues/873 | 747,959,523 | MDU6SXNzdWU3NDc5NTk1MjM= | 873 | load_dataset('cnn_dalymail', '3.0.0') gives a 'Not a directory' error | {
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} | [] | closed | false | null | [] | null | [
"I get the same error. It was fixed some days ago, but again it appears",
"Hi @mrm8488 it's working again today without any fix so I am closing this issue.",
"I see the issue happening again today - \r\n\r\n[nltk_data] Downloading package stopwords to /root/nltk_data...\r\n[nltk_data] Package stopwords is alr... | 1,605,940,245,000 | 1,651,735,199,000 | 1,606,047,485,000 | NONE | null | null | null | ```
from datasets import load_dataset
dataset = load_dataset('cnn_dailymail', '3.0.0')
```
Stack trace:
```
---------------------------------------------------------------------------
NotADirectoryError Traceback (most recent call last)
<ipython-input-6-2e06a8332652> in <module>()
1 from datasets import load_dataset
----> 2 dataset = load_dataset('cnn_dailymail', '3.0.0')
5 frames
/usr/local/lib/python3.6/dist-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)
608 download_config=download_config,
609 download_mode=download_mode,
--> 610 ignore_verifications=ignore_verifications,
611 )
612
/usr/local/lib/python3.6/dist-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)
513 if not downloaded_from_gcs:
514 self._download_and_prepare(
--> 515 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
516 )
517 # Sync info
/usr/local/lib/python3.6/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
568 split_dict = SplitDict(dataset_name=self.name)
569 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 570 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
571
572 # Checksums verification
/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602/cnn_dailymail.py in _split_generators(self, dl_manager)
252 def _split_generators(self, dl_manager):
253 dl_paths = dl_manager.download_and_extract(_DL_URLS)
--> 254 train_files = _subset_filenames(dl_paths, datasets.Split.TRAIN)
255 # Generate shared vocabulary
256
/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602/cnn_dailymail.py in _subset_filenames(dl_paths, split)
153 else:
154 logging.fatal("Unsupported split: %s", split)
--> 155 cnn = _find_files(dl_paths, "cnn", urls)
156 dm = _find_files(dl_paths, "dm", urls)
157 return cnn + dm
/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)
132 else:
133 logging.fatal("Unsupported publisher: %s", publisher)
--> 134 files = sorted(os.listdir(top_dir))
135
136 ret_files = []
NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories'
```
I have ran the code on Google Colab | {
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https://api.github.com/repos/huggingface/datasets/issues/872 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/872/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/872/comments | https://api.github.com/repos/huggingface/datasets/issues/872/events | https://github.com/huggingface/datasets/pull/872 | 747,653,697 | MDExOlB1bGxSZXF1ZXN0NTI0ODM4NjEx | 872 | Add IndicGLUE dataset and Metrics | {
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"thanks ! merging now"
] | 1,605,892,174,000 | 1,606,323,671,000 | 1,606,317,967,000 | CONTRIBUTOR | null | false | {
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} | Added IndicGLUE benchmark for evaluating models on 11 Indian Languages. The descriptions of the tasks and the corresponding paper can be found [here](https://indicnlp.ai4bharat.org/indic-glue/)
- [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/871 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/871/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/871/comments | https://api.github.com/repos/huggingface/datasets/issues/871/events | https://github.com/huggingface/datasets/issues/871 | 747,470,136 | MDU6SXNzdWU3NDc0NzAxMzY= | 871 | terminate called after throwing an instance of 'google::protobuf::FatalException' | {
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"Loading the iwslt2017-en-nl config of iwslt2017 works fine on my side. \r\nMaybe you can open an issue on transformers as well ? And also add more details about your environment (OS, python version, version of transformers and datasets etc.)",
"closing now, figured out this is because the max length of decoder w... | 1,605,876,984,000 | 1,607,807,792,000 | 1,607,807,792,000 | CONTRIBUTOR | null | null | null | Hi
I am using the dataset "iwslt2017-en-nl", and after downloading it I am getting this error when trying to evaluate it on T5-base with seq2seq_trainer.py in the huggingface repo could you assist me please? thanks
100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 63/63 [02:47<00:00, 2.18s/it][libprotobuf FATAL /sentencepiece/src/../third_party/protobuf-lite/google/protobuf/repeated_field.h:1505] CHECK failed: (index) >= (0):
terminate called after throwing an instance of 'google::protobuf::FatalException'
what(): CHECK failed: (index) >= (0):
run_t5_base_eval.sh: line 19: 5795 Aborted | {
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https://api.github.com/repos/huggingface/datasets/issues/870 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/870/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/870/comments | https://api.github.com/repos/huggingface/datasets/issues/870/events | https://github.com/huggingface/datasets/issues/870 | 747,021,996 | MDU6SXNzdWU3NDcwMjE5OTY= | 870 | [Feature Request] Add optional parameter in text loading script to preserve linebreaks | {
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"Hi ! Thanks for your message.\r\nIndeed it's a free feature we can add and that can be useful.\r\nIf you want to contribute, feel free to open a PR to add it to the text dataset script :)"
] | 1,605,829,891,000 | 1,606,484,891,000 | null | NONE | null | null | null | I'm working on a project about rhyming verse using phonetic poetry and song lyrics, and line breaks are a vital part of the data.
I recently switched over to use the datasets library when my various corpora grew larger than my computer's memory. And so far, it is SO great.
But the first time I processed all of my data into a dataset, I hadn't realized the text loader script was processing the source files line-by-line and stripping off the newlines.
Once I caught the issue, I made my own data loader by modifying one line in the default text loader (changing `batch = batch.splitlines()` to `batch = batch.splitlines(True)` inside `_generate_tables`). And so I'm all set as far as my project is concerned.
But if my use case is more general, it seems like it'd be pretty trivial to add a kwarg to the default text loader called keeplinebreaks or something, which would default to False and get passed to `splitlines()`. | {
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https://api.github.com/repos/huggingface/datasets/issues/869 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/869/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/869/comments | https://api.github.com/repos/huggingface/datasets/issues/869/events | https://github.com/huggingface/datasets/pull/869 | 746,495,711 | MDExOlB1bGxSZXF1ZXN0NTIzODc3OTkw | 869 | Update ner datasets infos | {
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":+1: Thanks for fixing it!"
] | 1,605,785,283,000 | 1,605,795,258,000 | 1,605,795,257,000 | MEMBER | null | false | {
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I also fixed the ner types of conll2003 | {
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https://api.github.com/repos/huggingface/datasets/issues/868 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/868/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/868/comments | https://api.github.com/repos/huggingface/datasets/issues/868/events | https://github.com/huggingface/datasets/pull/868 | 745,889,882 | MDExOlB1bGxSZXF1ZXN0NTIzMzc2MzQ3 | 868 | Consistent metric outputs | {
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"I keep this PR in stand-by for next week's datasets sprint. If the next release is 2.0.0 then we can include it given that it's breaking for many metrics"
] | 1,605,722,759,000 | 1,606,411,947,000 | null | MEMBER | null | false | {
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} | To automate the use of metrics, they should return consistent outputs.
In particular I'm working on adding a conversion of metrics to keras metrics.
To achieve this we need two things:
- have each metric return dictionaries of string -> floats since each keras metrics should return one float
- define in the metric info the different fields of the output dictionary
In this PR I'm adding these two features.
I also fixed a few bugs in some metrics
#867 needs to be merged first | {
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https://api.github.com/repos/huggingface/datasets/issues/867 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/867/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/867/comments | https://api.github.com/repos/huggingface/datasets/issues/867/events | https://github.com/huggingface/datasets/pull/867 | 745,773,955 | MDExOlB1bGxSZXF1ZXN0NTIzMjc4MjI4 | 867 | Fix some metrics feature types | {
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- accuracy
- precision
- recall
- f1
I also added the sklearn citation and used keyword arguments to remove future warnings | {
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https://api.github.com/repos/huggingface/datasets/issues/866 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/866/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/866/comments | https://api.github.com/repos/huggingface/datasets/issues/866/events | https://github.com/huggingface/datasets/issues/866 | 745,719,222 | MDU6SXNzdWU3NDU3MTkyMjI= | 866 | OSCAR from Inria group | {
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"PR is already open here : #348 \r\nThe only thing remaining is to compute the metadata of each subdataset (one per language + shuffled/unshuffled).\r\nAs soon as #863 is merged we can start computing them. This will take a bit of time though",
"Grand, thanks for this!"
] | 1,605,710,454,000 | 1,605,711,690,000 | 1,605,711,690,000 | NONE | null | null | null | ## Adding a Dataset
- **Name:** *OSCAR* (Open Super-large Crawled ALMAnaCH coRpus), multilingual parsing of Common Crawl (separate crawls for many different languages), [here](https://oscar-corpus.com/).
- **Description:** *OSCAR or Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture.*
- **Paper:** *[here](https://hal.inria.fr/hal-02148693)*
- **Data:** *[here](https://oscar-corpus.com/)*
- **Motivation:** *useful for unsupervised tasks in separate languages. In an ideal world, your team would be able to obtain the unshuffled version, that could be used to train GPT-2-like models (the shuffled version, I suppose, could be used for translation).*
I am aware that you do offer the "colossal" Common Crawl dataset already, but this has the advantage to be available in many subcorpora for different languages.
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https://api.github.com/repos/huggingface/datasets/issues/865 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/865/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/865/comments | https://api.github.com/repos/huggingface/datasets/issues/865/events | https://github.com/huggingface/datasets/issues/865 | 745,430,497 | MDU6SXNzdWU3NDU0MzA0OTc= | 865 | Have Trouble importing `datasets` | {
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"I'm sorry, this was a problem with my environment.\r\nNow that I have identified the cause of environmental dependency, I would like to fix it and try it.\r\nExcuse me for making a noise."
] | 1,605,686,681,000 | 1,605,687,395,000 | 1,605,687,395,000 | CONTRIBUTOR | null | null | null | I'm failing to import transformers (v4.0.0-dev), and tracing the cause seems to be failing to import datasets.
I cloned the newest version of datasets (master branch), and do `pip install -e .`.
Then, `import datasets` causes the error below.
```
~/workspace/Clone/datasets/src/datasets/utils/file_utils.py in <module>
116 sys.path.append(str(HF_MODULES_CACHE))
117
--> 118 os.makedirs(HF_MODULES_CACHE, exist_ok=True)
119 if not os.path.exists(os.path.join(HF_MODULES_CACHE, "__init__.py")):
120 with open(os.path.join(HF_MODULES_CACHE, "__init__.py"), "w"):
~/.pyenv/versions/anaconda3-2020.07/lib/python3.8/os.py in makedirs(name, mode, exist_ok)
221 return
222 try:
--> 223 mkdir(name, mode)
224 except OSError:
225 # Cannot rely on checking for EEXIST, since the operating system
FileNotFoundError: [Errno 2] No such file or directory: '<MY_HOME_DIRECTORY>/.cache/huggingface/modules'
```
The error occurs in `os.makedirs` in `file_utils.py`, even though `exist_ok = True` option is set.
(I use Python 3.8, so `exist_ok` is expected to work.)
I've checked some environment variables, and they are set as below.
```
*** NameError: name 'HF_MODULES_CACHE' is not defined
*** NameError: name 'hf_cache_home' is not defined
*** NameError: name 'XDG_CACHE_HOME' is not defined
```
Should I set some environment variables before using this library?
And, do you have any idea why "No such file or directory" occurs even though the `exist_ok = True` option is set?
Thank you in advance. | {
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https://api.github.com/repos/huggingface/datasets/issues/864 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/864/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/864/comments | https://api.github.com/repos/huggingface/datasets/issues/864/events | https://github.com/huggingface/datasets/issues/864 | 745,322,357 | MDU6SXNzdWU3NDUzMjIzNTc= | 864 | Unable to download cnn_dailymail dataset | {
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"Same error here!\r\n",
"Same here! My kaggle notebook stopped working like yesterday. It's strange because I have fixed version of datasets==1.1.2",
"I'm looking at it right now",
"I couldn't reproduce unfortunately. I tried\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nload_dataset(\"cnn_dailymai... | 1,605,674,282,000 | 1,605,849,731,000 | 1,605,849,730,000 | NONE | null | null | null | ### Script to reproduce the error
```
from datasets import load_dataset
train_dataset = load_dataset("cnn_dailymail", "3.0.0", split= 'train[:10%')
valid_dataset = load_dataset("cnn_dailymail","3.0.0", split="validation[:5%]")
```
### Error
```
---------------------------------------------------------------------------
NotADirectoryError Traceback (most recent call last)
<ipython-input-8-47c39c228935> in <module>()
1 from datasets import load_dataset
2
----> 3 train_dataset = load_dataset("cnn_dailymail", "3.0.0", split= 'train[:10%')
4 valid_dataset = load_dataset("cnn_dailymail","3.0.0", split="validation[:5%]")
5 frames
/usr/local/lib/python3.6/dist-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)
609 download_config=download_config,
610 download_mode=download_mode,
--> 611 ignore_verifications=ignore_verifications,
612 )
613
/usr/local/lib/python3.6/dist-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)
469 if not downloaded_from_gcs:
470 self._download_and_prepare(
--> 471 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
472 )
473 # Sync info
/usr/local/lib/python3.6/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
524 split_dict = SplitDict(dataset_name=self.name)
525 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 526 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
527
528 # Checksums verification
/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602/cnn_dailymail.py in _split_generators(self, dl_manager)
252 def _split_generators(self, dl_manager):
253 dl_paths = dl_manager.download_and_extract(_DL_URLS)
--> 254 train_files = _subset_filenames(dl_paths, datasets.Split.TRAIN)
255 # Generate shared vocabulary
256
/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602/cnn_dailymail.py in _subset_filenames(dl_paths, split)
153 else:
154 logging.fatal("Unsupported split: %s", split)
--> 155 cnn = _find_files(dl_paths, "cnn", urls)
156 dm = _find_files(dl_paths, "dm", urls)
157 return cnn + dm
/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)
132 else:
133 logging.fatal("Unsupported publisher: %s", publisher)
--> 134 files = sorted(os.listdir(top_dir))
135
136 ret_files = []
NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories'
```
Thanks for any suggestions. | {
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https://api.github.com/repos/huggingface/datasets/issues/863 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/863/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/863/comments | https://api.github.com/repos/huggingface/datasets/issues/863/events | https://github.com/huggingface/datasets/pull/863 | 744,954,534 | MDExOlB1bGxSZXF1ZXN0NTIyNTk0Mjg1 | 863 | Add clear_cache parameter in the test command | {
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} | For certain datasets like OSCAR #348 there are lots of different configurations and each one of them can take a lot of disk space.
I added a `--clear_cache` flag to the `datasets-cli test` command to be able to clear the cache after each configuration test to avoid filling up the disk. It should enable an easier generation for the `dataset_infos.json` file for OSCAR. | {
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"The preprocessing tokenizes the input text. Tokenization outputs `input_ids`, `attention_mask`, `token_type_ids` and `special_tokens_mask`. All those are of length`max_seq_length` because of padding. Therefore for each sample it generate 4 *`max_seq_length` integers. Currently they're all saved as int64. This is w... | 1,605,620,939,000 | 1,617,113,044,000 | 1,616,414,695,000 | NONE | null | null | null | Hey guys,
I was trying to create a new bert model from scratch via _huggingface transformers + tokenizers + dataets_ (actually using this example script by your team: https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm.py). It was supposed to be a first test with a small 5 GB raw text file but I can't even end the preprocessing handled by datasets because this tiny 5 GB text file becomes more than 1 TB when processing. My system was running out of space and crashed prematurely.
I've done training from scratch via Google's bert repo in the past and I can remember that the resulting pretraining data can become quite big. But 5 GB becoming 1 TB was never the case. Is this considered normal or is it a bug?
I've used the following CMD:
`python xla_spawn.py --num_cores=8 run_mlm.py --model_type bert --config_name config.json --tokenizer_name tokenizer.json --train_file dataset_full.txt --do_train --output_dir out --max_steps 500000 --save_steps 2500 --save_total_limit 2 --prediction_loss_only --line_by_line --max_seq_length 128 --pad_to_max_length --preprocessing_num_workers 16 --per_device_train_batch_size 128 --overwrite_output_dir --debug`
| {
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https://api.github.com/repos/huggingface/datasets/issues/860 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/860/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/860/comments | https://api.github.com/repos/huggingface/datasets/issues/860/events | https://github.com/huggingface/datasets/issues/860 | 744,750,691 | MDU6SXNzdWU3NDQ3NTA2OTE= | 860 | wmt16 cs-en does not donwload | {
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] | open | false | null | [] | null | [] | 1,605,620,735,000 | 1,606,484,824,000 | null | CONTRIBUTOR | null | null | null | Hi
I am trying with wmt16, cs-en pair, thanks for the help, perhaps similar to the ro-en issue. thanks
split="train", n_obs=data_args.n_train) for task in data_args.task}
File "finetune_t5_trainer.py", line 109, in <dictcomp>
split="train", n_obs=data_args.n_train) for task in data_args.task}
File "/home/rabeeh/internship/seq2seq/tasks/tasks.py", line 82, in get_dataset
dataset = load_dataset("wmt16", self.pair, split=split)
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/load.py", line 611, in load_dataset
ignore_verifications=ignore_verifications,
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 476, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 531, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/rabeeh/.cache/huggingface/modules/datasets_modules/datasets/wmt16/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308/wmt_utils.py", line 755, in _split_generators
downloaded_files = dl_manager.download_and_extract(urls_to_download)
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 179, in download
num_proc=download_config.num_proc,
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 225, in map_nested
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 225, in <listcomp>
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 181, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 181, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 163, in _single_map_nested
return function(data_struct)
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 475, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz | {
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https://api.github.com/repos/huggingface/datasets/issues/859 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/859/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/859/comments | https://api.github.com/repos/huggingface/datasets/issues/859/events | https://github.com/huggingface/datasets/pull/859 | 743,917,091 | MDExOlB1bGxSZXF1ZXN0NTIxNzI4MDM4 | 859 | Integrate file_lock inside the lib for better logging control | {
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} | Previously the locking system of the lib was based on the file_lock package. However as noticed in #812 there were too many logs printed even when the datasets logging was set to warnings or errors.
For example
```python
import logging
logging.basicConfig(level=logging.INFO)
import datasets
datasets.set_verbosity_warning()
datasets.load_dataset("squad")
```
would still log the file lock events:
```
INFO:filelock:Lock 5737989232 acquired on /Users/quentinlhoest/.cache/huggingface/datasets/44801f118d500eff6114bfc56ab4e6def941f1eb14b70ac1ecc052e15cdac49d.85f43de978b9b25921cb78d7a2f2b350c04acdbaedb9ecb5f7101cd7c0950e68.py.lock
INFO:filelock:Lock 5737989232 released on /Users/quentinlhoest/.cache/huggingface/datasets/44801f118d500eff6114bfc56ab4e6def941f1eb14b70ac1ecc052e15cdac49d.85f43de978b9b25921cb78d7a2f2b350c04acdbaedb9ecb5f7101cd7c0950e68.py.lock
INFO:filelock:Lock 4393489968 acquired on /Users/quentinlhoest/.cache/huggingface/datasets/_Users_quentinlhoest_.cache_huggingface_datasets_squad_plain_text_1.0.0_1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41.lock
INFO:filelock:Lock 4393489968 released on /Users/quentinlhoest/.cache/huggingface/datasets/_Users_quentinlhoest_.cache_huggingface_datasets_squad_plain_text_1.0.0_1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41.lock
INFO:filelock:Lock 4393490808 acquired on /Users/quentinlhoest/.cache/huggingface/datasets/_Users_quentinlhoest_.cache_huggingface_datasets_squad_plain_text_1.0.0_1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41.lock
Reusing dataset squad (/Users/quentinlhoest/.cache/huggingface/datasets/squad/plain_text/1.0.0/1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41)
INFO:filelock:Lock 4393490808 released on /Users/quentinlhoest/.cache/huggingface/datasets/_Users_quentinlhoest_.cache_huggingface_datasets_squad_plain_text_1.0.0_1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41.lock
```
With the integration of file_lock in the library, the ouput is much cleaner:
```
Reusing dataset squad (/Users/quentinlhoest/.cache/huggingface/datasets/squad/plain_text/1.0.0/1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41)
```
Since the file_lock package is only a 450 lines file I think it's fine to have it inside the lib.
Fix #812 | {
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https://api.github.com/repos/huggingface/datasets/issues/858 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/858/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/858/comments | https://api.github.com/repos/huggingface/datasets/issues/858/events | https://github.com/huggingface/datasets/pull/858 | 743,904,516 | MDExOlB1bGxSZXF1ZXN0NTIxNzE3ODQ4 | 858 | Add SemEval-2010 task 8 | {
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"Added dummy data and encoding to open(). Now everything should be fine, hopefully :)"
] | 1,605,538,677,000 | 1,606,411,735,000 | 1,606,411,735,000 | CONTRIBUTOR | null | false | {
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I don't know how to add dummy data, since I create the validation set out of the last 1000 examples of the train set. If you have a suggestion, I am happy to implement it.
Cheers,
Joel | {
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https://api.github.com/repos/huggingface/datasets/issues/857 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/857/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/857/comments | https://api.github.com/repos/huggingface/datasets/issues/857/events | https://github.com/huggingface/datasets/pull/857 | 743,863,214 | MDExOlB1bGxSZXF1ZXN0NTIxNjg0ODIx | 857 | Use pandas reader in csv | {
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} | The pyarrow CSV reader has issues that the pandas one doesn't (see #836 ).
To fix that I switched to the pandas csv reader.
The new reader is compatible with all the pandas parameters to read csv files.
Moreover it reads csv by chunk in order to save RAM, while the pyarrow one loads everything in memory.
Fix #836
Fix #794
Breaking: now all the parameters to read to csv file can be used in the `load_dataset` kwargs when loading csv, and the previous pyarrow objects `pyarrow.csv.ReadOptions`, `pyarrow.csv.ParseOptions` and `pyarrow.csv.ConvertOptions` are not used anymore. | {
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"@lhoestq I fixed issues except for the dummy_data zip file. But I think I know why is it happening. So when unzipping dummy_data.zip it gets save in /tmp directory where glob doesn't pick it up. For regular downloads, the archive gets unzipped in ~/.cache/huggingface. Could that be a reason?",
"Nice thanks :)\r\... | 1,605,529,802,000 | 1,605,701,026,000 | 1,605,626,538,000 | CONTRIBUTOR | null | false | {
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} | Adds book corpus based on Shawn Presser's [work](https://github.com/soskek/bookcorpus/issues/27) @richarddwang, the author of the original BookCorpus dataset, suggested it should be named [OpenBookCorpus](https://github.com/huggingface/datasets/issues/486). I named it BookCorpusOpen to be easily located alphabetically. But, of course, we can rename it if needed.
It contains 17868 dataset items; each item contains two fields: title and text. The title is the name of the book (just the file name) while the text contains unprocessed book text. Note that bookcorpus is pre-segmented into a sentence while this bookcorpus is not. This is intentional (see https://github.com/huggingface/datasets/issues/486) as some users might want to further process the text themselves.
@lhoestq and others please review this PR thoroughly. cc @shawwn | {
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} | The kor_nli dataset had an issue with the csv reader that was not able to parse the lines correctly. Some lines were merged together for some reason.
I fixed that by iterating through the lines directly instead of using a csv reader.
I also changed the feature names to match the other NLI datasets (i.e. use "premise", "hypothesis", "label" features)
Fix #821 | {
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"Hi,I also posted it to the forum, but this is a bug, perhaps it needs to be reported here? thanks ",
"It looks like the official OPUS server for WMT16 doesn't provide the data files anymore (503 error).\r\nI searched a bit and couldn't find a mirror except maybe http://nlp.ffzg.hr/resources/corpora/setimes/ (the... | 1,605,519,111,000 | 1,614,222,909,000 | null | CONTRIBUTOR | null | null | null | Hi, I appreciate your help with the following error, thanks
>>> from datasets import load_dataset
>>> dataset = load_dataset("wmt16", "ro-en", split="train")
Downloading and preparing dataset wmt16/ro-en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /root/.cache/huggingface/datasets/wmt16/ro-en/1.0.0/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308...
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/load.py", line 611, in load_dataset
ignore_verifications=ignore_verifications,
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/builder.py", line 476, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/builder.py", line 531, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/root/.cache/huggingface/modules/datasets_modules/datasets/wmt16/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308/wmt_utils.py", line 755, in _split_generators
downloaded_files = dl_manager.download_and_extract(urls_to_download)
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 179, in download
num_proc=download_config.num_proc,
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in map_nested
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in <listcomp>
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 181, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 181, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 163, in _single_map_nested
return function(data_struct)
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 475, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://opus.nlpl.eu/download.php?f=SETIMES/v2/tmx/en-ro.tmx.gz | {
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"Unfortunately `concatenate_datasets` only supports concatenating the rows, while what you want to achieve is concatenate the columns.\r\nCurrently to add more columns to a dataset, one must use `map`.\r\nWhat you can do is somehting like this:\r\n```python\r\n# suppose you have datasets d1, d2, d3\r\ndef add_colum... | 1,605,494,783,000 | 1,618,848,438,000 | 1,618,848,438,000 | NONE | null | null | null | I want to achieve the following result
| {
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https://api.github.com/repos/huggingface/datasets/issues/852 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/852/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/852/comments | https://api.github.com/repos/huggingface/datasets/issues/852/events | https://github.com/huggingface/datasets/issues/852 | 743,396,240 | MDU6SXNzdWU3NDMzOTYyNDA= | 852 | wmt cannot be downloaded | {
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] | closed | false | null | [] | null | [] | 1,605,488,681,000 | 1,605,519,118,000 | 1,605,519,118,000 | CONTRIBUTOR | null | null | null | Hi, I appreciate your help with the following error, thanks
>>> from datasets import load_dataset
>>> dataset = load_dataset("wmt16", "ro-en", split="train")
Downloading and preparing dataset wmt16/ro-en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /root/.cache/huggingface/datasets/wmt16/ro-en/1.0.0/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308...
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/load.py", line 611, in load_dataset
ignore_verifications=ignore_verifications,
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/builder.py", line 476, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/builder.py", line 531, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/root/.cache/huggingface/modules/datasets_modules/datasets/wmt16/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308/wmt_utils.py", line 755, in _split_generators
downloaded_files = dl_manager.download_and_extract(urls_to_download)
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 179, in download
num_proc=download_config.num_proc,
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in map_nested
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in <listcomp>
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 181, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 181, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 163, in _single_map_nested
return function(data_struct)
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 475, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://opus.nlpl.eu/download.php?f=SETIMES/v2/tmx/en-ro.tmx.gz | {
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https://api.github.com/repos/huggingface/datasets/issues/851 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/851/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/851/comments | https://api.github.com/repos/huggingface/datasets/issues/851/events | https://github.com/huggingface/datasets/issues/851 | 743,343,278 | MDU6SXNzdWU3NDMzNDMyNzg= | 851 | Add support for other languages for rouge | {
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] | open | false | null | [] | null | [
"@alexyalunin \r\n\r\nI did something similar for others languages.\r\n\r\n[Repo: rouge-metric](https://github.com/m3hrdadfi/rouge-metric)"
] | 1,605,473,865,000 | 1,622,970,472,000 | null | NONE | null | null | null | I calculate rouge with
```
from datasets import load_metric
rouge = load_metric("rouge")
rouge_output = rouge.compute(predictions=['тест тест привет'], references=['тест тест пока'], rouge_types=[
"rouge2"])["rouge2"].mid
print(rouge_output)
```
the result is
`Score(precision=0.0, recall=0.0, fmeasure=0.0)`
It seems like the `rouge_score` library that this metric uses filters all non-alphanueric latin characters
in `rouge_scorer/tokenize.py` with `text = re.sub(r"[^a-z0-9]+", " ", six.ensure_str(text))`.
Please add support for other languages. | {
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https://api.github.com/repos/huggingface/datasets/issues/850 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/850/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/850/comments | https://api.github.com/repos/huggingface/datasets/issues/850/events | https://github.com/huggingface/datasets/pull/850 | 742,369,419 | MDExOlB1bGxSZXF1ZXN0NTIwNTE0MDY3 | 850 | Create ClassLabel for labelling tasks datasets | {
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"@lhoestq Better?"
] | 1,605,265,642,000 | 1,605,522,725,000 | 1,605,522,718,000 | CONTRIBUTOR | null | false | {
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"merged_at": 1605522718000
} | This PR adds a specific `ClassLabel` for the datasets that are about a labelling task such as POS, NER or Chunking. | {
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https://api.github.com/repos/huggingface/datasets/issues/849 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/849/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/849/comments | https://api.github.com/repos/huggingface/datasets/issues/849/events | https://github.com/huggingface/datasets/issues/849 | 742,263,333 | MDU6SXNzdWU3NDIyNjMzMzM= | 849 | Load amazon dataset | {
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"Thanks for reporting !\r\nWe plan to show information about the different configs of the datasets on the website, with the corresponding `load_dataset` calls.\r\n\r\nAlso I think the bullet points formatting has been fixed"
] | 1,605,256,464,000 | 1,605,597,779,000 | 1,605,597,779,000 | CONTRIBUTOR | null | null | null | Hi,
I was going through amazon_us_reviews dataset and found that example API usage given on website is different from the API usage while loading dataset.
Eg. what API usage is on the [website](https://huggingface.co/datasets/amazon_us_reviews)
```
from datasets import load_dataset
dataset = load_dataset("amazon_us_reviews")
```
How it is when I tried (the error generated does point me to the right direction though)
```
from datasets import load_dataset
dataset = load_dataset("amazon_us_reviews", 'Books_v1_00')
```
Also, there is some issue with formatting as it's not showing bullet list in description with new line. Can I work on it? | {
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https://api.github.com/repos/huggingface/datasets/issues/848 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/848/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/848/comments | https://api.github.com/repos/huggingface/datasets/issues/848/events | https://github.com/huggingface/datasets/issues/848 | 742,240,942 | MDU6SXNzdWU3NDIyNDA5NDI= | 848 | Error when concatenate_datasets | {
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"As you can see in the error the test checks if `indices_mappings_in_memory` is True or not, which is different from the test you do in your script. In a dataset, both the data and the indices mapping can be either on disk or in memory.\r\n\r\nThe indices mapping correspond to a mapping on top of the data table tha... | 1,605,254,162,000 | 1,605,289,259,000 | 1,605,282,910,000 | NONE | null | null | null | Hello, when I concatenate two dataset loading from disk, I encountered a problem:
```
test_dataset = load_from_disk('data/test_dataset')
trn_dataset = load_from_disk('data/train_dataset')
train_dataset = concatenate_datasets([trn_dataset, test_dataset])
```
And it reported ValueError blow:
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-38-74fa525512ca> in <module>
----> 1 train_dataset = concatenate_datasets([trn_dataset, test_dataset])
/opt/miniconda3/lib/python3.7/site-packages/datasets/arrow_dataset.py in concatenate_datasets(dsets, info, split)
2547 "However datasets' indices {} come from memory and datasets' indices {} come from disk.".format(
2548 [i for i in range(len(dsets)) if indices_mappings_in_memory[i]],
-> 2549 [i for i in range(len(dsets)) if not indices_mappings_in_memory[i]],
2550 )
2551 )
ValueError: Datasets' indices should ALL come from memory, or should ALL come from disk.
However datasets' indices [1] come from memory and datasets' indices [0] come from disk.
```
But it's curious both of my datasets loading from disk, so I check the source code in `arrow_dataset.py` about the Error:
```
trn_dataset._data_files
# output
[{'filename': 'data/train_dataset/csv-train.arrow', 'skip': 0, 'take': 593264}]
test_dataset._data_files
# output
[{'filename': 'data/test_dataset/csv-test.arrow', 'skip': 0, 'take': 424383}]
print([not dset._data_files for dset in [trn_dataset, test_dataset]])
# [False, False]
# And I tested the code the same as arrow_dataset, but nothing happened
dsets = [trn_dataset, test_dataset]
dsets_in_memory = [not dset._data_files for dset in dsets]
if any(dset_in_memory != dsets_in_memory[0] for dset_in_memory in dsets_in_memory):
raise ValueError(
"Datasets should ALL come from memory, or should ALL come from disk.\n"
"However datasets {} come from memory and datasets {} come from disk.".format(
[i for i in range(len(dsets)) if dsets_in_memory[i]],
[i for i in range(len(dsets)) if not dsets_in_memory[i]],
)
)
```
Any suggestions would be greatly appreciated!
Thanks! | {
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