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This is also a prospective GEM task, hence the README.md | {
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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
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More information can be found [here](https://webnlg-challenge.loria.fr/)
Unfortunately, the data itself comes from a pretty large number of small XML files, so the dummy data ends up being quite large (8.4 MB even keeping only one example per file). | {
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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
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More info:
https://github.com/Websail-NU/CODAH | {
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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 1,606,832,402,000 | 1,606,872,764,000 | null | CONTRIBUTOR | null | null | null | Hi
I am dealing with multiple datasets, I need to have a dataloader over them with a condition that in each batch data samples are coming from one of the datasets. My main question is:
- I need to have a way to sample the datasets first with some weights, lets say 2x dataset1 1x dataset2, could you point me how I can do it
sub-questions:
- I want to concat sampled datasets and define one dataloader on it, then I need a way to make sure batches come from 1 dataset in each iteration, could you assist me how I can do?
- I use iterative-type of datasets, but I need a method of shuffling still since it brings accuracy performance issues if not doing it, thanks for the help. | {
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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 1,606,827,062,000 | 1,606,899,191,000 | 1,606,846,161,000 | CONTRIBUTOR | null | false | {
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I have added the `conllu` package as a test dependency. This is required to properly parse the `.conllu` files. | {
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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 1,606,826,955,000 | 1,607,078,612,000 | 1,606,988,207,000 | CONTRIBUTOR | null | false | {
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] | 1,606,826,455,000 | 1,606,885,931,000 | 1,606,884,232,000 | CONTRIBUTOR | null | false | {
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} | `prachathai-67k`: News Article Corpus and Multi-label Text Classificdation from Prachathai.com
The prachathai-67k dataset was scraped from the news site Prachathai.
We filtered out those articles with less than 500 characters of body text, mostly images and cartoons.
It contains 67,889 articles wtih 12 curated tags from August 24, 2004 to November 15, 2018.
The dataset was originally scraped by @lukkiddd and cleaned by @cstorm125.
You can also see preliminary exploration at https://github.com/PyThaiNLP/prachathai-67k/blob/master/exploration.ipynb | {
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] | 1,606,826,264,000 | 1,606,864,293,000 | 1,606,864,293,000 | CONTRIBUTOR | null | false | {
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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 1,606,826,014,000 | 1,606,837,440,000 | 1,606,837,440,000 | CONTRIBUTOR | null | false | {
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} | Add OrangeSum a french abstractive summarization dataset.
Paper: [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321) | {
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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 1,606,825,312,000 | 1,606,825,793,000 | 1,606,825,706,000 | CONTRIBUTOR | null | false | {
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} | Add `prachathai-67k`: News Article Corpus and Multi-label Text Classificdation from Prachathai.com
The `prachathai-67k` dataset was scraped from the news site [Prachathai](prachathai.com). We filtered out those articles with less than 500 characters of body text, mostly images and cartoons. It contains 67,889 articles wtih 12 curated tags from August 24, 2004 to November 15, 2018. The dataset was originally scraped by [@lukkiddd](https://github.com/lukkiddd) and cleaned by [@cstorm125](https://github.com/cstorm125). Download the dataset [here](https://www.dropbox.com/s/fsxepdka4l2pr45/prachathai-67k.zip?dl=1). You can also see preliminary exploration in [exploration.ipynb](https://github.com/PyThaiNLP/prachathai-67k/blob/master/exploration.ipynb).
This dataset is a part of [pyThaiNLP](https://github.com/PyThaiNLP/) Thai text [classification-benchmarks](https://github.com/PyThaiNLP/classification-benchmarks). For the benchmark, we selected the following tags with substantial volume that resemble **classifying types of articles**:
* `การเมือง` - politics
* `สิทธิมนุษยชน` - human_rights
* `คุณภาพชีวิต` - quality_of_life
* `ต่างประเทศ` - international
* `สังคม` - social
* `สิ่งแวดล้อม` - environment
* `เศรษฐกิจ` - economics
* `วัฒนธรรม` - culture
* `แรงงาน` - labor
* `ความมั่นคง` - national_security
* `ไอซีที` - ict
* `การศึกษา` - education | {
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https://api.github.com/repos/huggingface/datasets/issues/948 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/948/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/948/comments | https://api.github.com/repos/huggingface/datasets/issues/948/events | https://github.com/huggingface/datasets/pull/948 | 754,306,260 | MDExOlB1bGxSZXF1ZXN0NTMwMjI4NjQz | 948 | docs(ADD_NEW_DATASET): correct indentation for script | {
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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 1,606,819,938,000 | 1,606,902,155,000 | 1,606,902,129,000 | CONTRIBUTOR | null | false | {
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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 1,606,819,301,000 | 1,606,963,634,000 | 1,606,963,634,000 | CONTRIBUTOR | null | false | {
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Samples are taken from ParlAI for consistency with the main users at the moment. | {
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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 1,606,813,250,000 | 1,606,836,278,000 | 1,606,836,270,000 | CONTRIBUTOR | null | false | {
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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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] | 1,606,810,630,000 | 1,607,013,773,000 | 1,607,013,773,000 | NONE | null | null | null | ## Adding a Dataset
- **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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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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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
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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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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 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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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 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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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 1,606,776,550,000 | 1,606,798,582,000 | 1,606,778,040,000 | MEMBER | null | false | {
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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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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 1,606,775,793,000 | 1,606,854,350,000 | 1,606,852,316,000 | CONTRIBUTOR | null | false | {
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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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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
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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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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 1,606,771,821,000 | 1,606,771,821,000 | null | MEMBER | null | true | {
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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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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
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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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] | 1,606,762,686,000 | 1,606,838,670,000 | 1,606,838,667,000 | CONTRIBUTOR | null | false | {
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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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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
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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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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
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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()`
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- [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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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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] | 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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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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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 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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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 1,606,735,775,000 | 1,606,761,270,000 | 1,606,761,269,000 | MEMBER | null | false | {
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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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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 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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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
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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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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 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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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 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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] | 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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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 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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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 1,606,599,383,000 | 1,606,644,711,000 | 1,606,644,711,000 | NONE | null | null | null | ## Adding a Dataset
- **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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https://api.github.com/repos/huggingface/datasets/issues/909 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/909/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/909/comments | https://api.github.com/repos/huggingface/datasets/issues/909/events | https://github.com/huggingface/datasets/pull/909 | 752,508,299 | MDExOlB1bGxSZXF1ZXN0NTI4ODE1NDYz | 909 | Add FiNER dataset | {
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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 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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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 1,606,503,330,000 | 1,606,690,100,000 | 1,606,690,099,000 | MEMBER | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/906 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/906/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/906/comments | https://api.github.com/repos/huggingface/datasets/issues/906/events | https://github.com/huggingface/datasets/pull/906 | 752,403,395 | MDExOlB1bGxSZXF1ZXN0NTI4NzM0MDY0 | 906 | Fix url with backslash in windows for blimp and pg19 | {
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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
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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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I'm suggesting a test to make sure we that all the urls don't have backslashes in them in the datasets scripts.
The tests works by adding a callback feature to the MockDownloadManager used to test the dataset scripts. In a download callback I just make sure that the url is valid. | {
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https://api.github.com/repos/huggingface/datasets/issues/904 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/904/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/904/comments | https://api.github.com/repos/huggingface/datasets/issues/904/events | https://github.com/huggingface/datasets/pull/904 | 752,372,743 | MDExOlB1bGxSZXF1ZXN0NTI4NzA5NTUx | 904 | Very detailed step-by-step on how to add a dataset | {
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https://api.github.com/repos/huggingface/datasets/issues/903 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/903/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/903/comments | https://api.github.com/repos/huggingface/datasets/issues/903/events | https://github.com/huggingface/datasets/pull/903 | 752,360,614 | MDExOlB1bGxSZXF1ZXN0NTI4Njk5NDQ3 | 903 | Fix URL with backslash in Windows | {
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In general, `os.path.join` should be avoided to generate URLs. | {
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Fix #900 | {
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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 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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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 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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https://api.github.com/repos/huggingface/datasets/issues/899 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/899/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/899/comments | https://api.github.com/repos/huggingface/datasets/issues/899/events | https://github.com/huggingface/datasets/pull/899 | 752,191,227 | MDExOlB1bGxSZXF1ZXN0NTI4NTYzNzYz | 899 | Allow arrow based builder in auto dummy data generation | {
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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
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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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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 1,606,468,474,000 | 1,635,671,521,000 | 1,635,671,521,000 | CONTRIBUTOR | null | null | null | I was looking through the dataset viewer and I like it a lot. Version numbers, citation information, everything's there! I've spotted a few issues/bugs though:
- the URL is still under `nlp`, perhaps an alias for `datasets` can be made
- when I remove a **feature** (and the feature list is empty), I get an error. This is probably expected, but perhaps a better error message can be shown to the user
```bash
IndexError: list index out of range
Traceback:
File "/home/sasha/streamlit/lib/streamlit/ScriptRunner.py", line 322, in _run_script
exec(code, module.__dict__)
File "/home/sasha/nlp-viewer/run.py", line 316, in <module>
st.table(style)
File "/home/sasha/streamlit/lib/streamlit/DeltaGenerator.py", line 122, in wrapped_method
return dg._enqueue_new_element_delta(marshall_element, delta_type, last_index)
File "/home/sasha/streamlit/lib/streamlit/DeltaGenerator.py", line 367, in _enqueue_new_element_delta
rv = marshall_element(msg.delta.new_element)
File "/home/sasha/streamlit/lib/streamlit/DeltaGenerator.py", line 120, in marshall_element
return method(dg, element, *args, **kwargs)
File "/home/sasha/streamlit/lib/streamlit/DeltaGenerator.py", line 2944, in table
data_frame_proto.marshall_data_frame(data, element.table)
File "/home/sasha/streamlit/lib/streamlit/elements/data_frame_proto.py", line 54, in marshall_data_frame
_marshall_styles(proto_df.style, df, styler)
File "/home/sasha/streamlit/lib/streamlit/elements/data_frame_proto.py", line 73, in _marshall_styles
translated_style = styler._translate()
File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/pandas/io/formats/style.py", line 351, in _translate
* (len(clabels[0]) - len(hidden_columns))
```
- there seems to be **an encoding issue** in the default view, the dataset examples are shown as raw monospace text, without a decent encoding. That makes it hard to read for languages that use a lot of special characters. Take for instance the [cs-en WMT19 set](https://huggingface.co/nlp/viewer/?dataset=wmt19&config=cs-en). This problem goes away when you enable "List view", because then some syntax highlighteris used, and the special characters are coded correctly.
| {
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https://api.github.com/repos/huggingface/datasets/issues/896 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/896/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/896/comments | https://api.github.com/repos/huggingface/datasets/issues/896/events | https://github.com/huggingface/datasets/pull/896 | 751,834,265 | MDExOlB1bGxSZXF1ZXN0NTI4MjcyMjc0 | 896 | Add template and documentation for dataset card | {
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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 1,606,426,225,000 | 1,606,525,815,000 | 1,606,525,815,000 | MEMBER | null | false | {
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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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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 1,606,416,946,000 | 1,606,483,860,000 | 1,606,483,859,000 | MEMBER | null | false | {
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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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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 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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] | 1,606,407,016,000 | 1,606,839,895,000 | 1,606,835,707,000 | CONTRIBUTOR | null | false | {
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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 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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] | 1,606,395,958,000 | 1,606,397,307,000 | 1,606,397,306,000 | MEMBER | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/890 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/890/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/890/comments | https://api.github.com/repos/huggingface/datasets/issues/890/events | https://github.com/huggingface/datasets/pull/890 | 751,534,050 | MDExOlB1bGxSZXF1ZXN0NTI4MDI5NjA3 | 890 | Add LER | {
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https://api.github.com/repos/huggingface/datasets/issues/889 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/889/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/889/comments | https://api.github.com/repos/huggingface/datasets/issues/889/events | https://github.com/huggingface/datasets/pull/889 | 751,115,691 | MDExOlB1bGxSZXF1ZXN0NTI3NjkwODE2 | 889 | Optional per-dataset default config name | {
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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 1,606,338,150,000 | 1,606,757,253,000 | 1,606,757,247,000 | CONTRIBUTOR | null | false | {
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} | This PR adds a `DEFAULT_CONFIG_NAME` class attribute to `DatasetBuilder`. This allows a dataset to have a specified default config name when a dataset has more than one config but the user does not specify it. For example, after defining `DEFAULT_CONFIG_NAME = "combined"` in PolyglotNER, a user can now do the following:
```python
ds = load_dataset("polyglot_ner")
```
which is equivalent to,
```python
ds = load_dataset("polyglot_ner", "combined")
```
In effect (for this particular dataset configuration), this means that if the user doesn't specify a language, they are given the combined dataset including all languages.
Since it doesn't always make sense to have a default config, this feature is opt-in. If `DEFAULT_CONFIG_NAME` is not defined and a user does not pass a config for a dataset with multiple configs available, a ValueError is raised like usual.
Let me know what you think about this approach @lhoestq @thomwolf and I'll add some documentation and define a default for some of our existing datasets. | {
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https://api.github.com/repos/huggingface/datasets/issues/888 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/888/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/888/comments | https://api.github.com/repos/huggingface/datasets/issues/888/events | https://github.com/huggingface/datasets/issues/888 | 750,944,422 | MDU6SXNzdWU3NTA5NDQ0MjI= | 888 | Nested lists are zipped unexpectedly | {
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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 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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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 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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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 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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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 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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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 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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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 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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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 1,606,220,632,000 | 1,611,916,867,000 | 1,611,916,867,000 | CONTRIBUTOR | null | false | {
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} | "no label" is "-" in the original dataset but "-1" in Huggingface distribution. | {
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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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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 1,606,211,231,000 | 1,606,212,754,000 | 1,606,212,753,000 | MEMBER | null | false | {
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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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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 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 @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 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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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 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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} | [] | closed | false | null | [] | null | [
"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 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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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 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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} | [] | closed | false | null | [] | null | [
"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 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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} | [] | closed | false | null | [] | null | [
"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 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 | [
"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 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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} | [] | closed | false | null | [] | null | [
"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 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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"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 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 @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype)."
] | 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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