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https://github.com/huggingface/datasets/pull/935 | add PIB dataset | Hi ! you can read the tests by logging in to circleci.
Anyway for information here are the errors :
```
datasets/pib/pib.py:19:1: F401 'csv' imported but unused
datasets/pib/pib.py:20:1: F401 'json' imported but unused
datasets/pib/pib.py:36:84: W291 trailing whitespace
```
and
```
FAILED tests/test_file_encoding.py::TestFileEncoding::test_no_encoding_on_file_open
```
To fix the `test_no_encoding_on_file_open` you just have to specify an encoding while opening a text file. For example `encoding="utf-8"`
| This pull request will add PIB dataset. | 62 | text: add PIB dataset
This pull request will add PIB dataset.
Hi ! you can read the tests by logging in to circleci.
Anyway for information here are the errors :
```
datasets/pib/pib.py:19:1: F401 'csv' imported but unused
datasets/pib/pib.py:20:1: F401 'json' imported but unused
datasets/pib/pib.py:36:84: W291 trailing whitespace
```
and
```
FAILED tests/test_file_encoding.py::TestFileEncoding::test_no_encoding_on_file_open
```
To fix the `test_no_encoding_on_file_open` you just have to specify an encoding while opening a text file. For example `encoding="utf-8"`
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https://github.com/huggingface/datasets/pull/935 | add PIB dataset | Nice ! can you re-generate the dataset_infos.json file to take into account the feature type change ?
```
datasets-cli test ./datasets/pib --save_infos --all_configs --ignore_verifications
```
And also format your code ?
```
make style
``` | This pull request will add PIB dataset. | 35 | text: add PIB dataset
This pull request will add PIB dataset.
Nice ! can you re-generate the dataset_infos.json file to take into account the feature type change ?
```
datasets-cli test ./datasets/pib --save_infos --all_configs --ignore_verifications
```
And also format your code ?
```
make style
``` | [
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https://github.com/huggingface/datasets/pull/932 | adding metooma dataset | This PR adds the #MeToo MA dataset. It presents multi-label data points for tweets mined in the backdrop of the #MeToo movement. The dataset includes data points in the form of Tweet ids and appropriate labels. Please refer to the accompanying paper for detailed information regarding annotation, collection, and guidelines.
Paper: https://ojs.aaai.org/index.php/ICWSM/article/view/7292
Dataset Link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/JN4EYU
YAML tags:
annotations_creators:
- expert-generated
language_creators:
- found
languages:
- en
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
- text-retrieval
task_ids:
- multi-class-classification
- multi-label-classification
# Dataset Card for #MeTooMA dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/JN4EYU
- **Paper:** https://ojs.aaai.org//index.php/ICWSM/article/view/7292
- **Point of Contact:** https://github.com/midas-research/MeTooMA
### Dataset Summary
- The dataset consists of tweets belonging to #MeToo movement on Twitter, labelled into different categories.
- This dataset includes more data points and has more labels than any of the previous datasets in that contain social media
posts about sexual abuse discloures. Please refer to the Related Datasets of the publication for a detailed information about this.
- Due to Twitters development policies, the authors provide only the tweet IDs and corresponding labels,
other data can be fetched via Twitter API.
- The data has been labelled by experts, with the majority taken into the account for deciding the final label.
- The authors provide these labels for each of the tweets.
- Relevance
- Directed Hate
- Generalized Hate
- Sarcasm
- Allegation
- Justification
- Refutation
- Support
- Oppose
- The definitions for each task/label is in the main publication.
- Please refer to the accompanying paper https://aaai.org/ojs/index.php/ICWSM/article/view/7292 for statistical analysis on the textual data
extracted from this dataset.
- The language of all the tweets in this dataset is English
- Time period: October 2018 - December 2018
- Suggested Use Cases of this dataset:
- Evaluating usage of linguistic acts such as: hate-spech and sarcasm in the incontext of public sexual abuse discloures.
- Extracting actionable insights and virtual dynamics of gender roles in sexual abuse revelations.
- Identifying how influential people were potrayed on public platform in the
events of mass social movements.
- Polarization analysis based on graph simulations of social nodes of users involved
in the #MeToo movement.
### Supported Tasks and Leaderboards
Multi Label and Multi-Class Classification
### Languages
English
## Dataset Structure
- The dataset is structured into CSV format with TweetID and accompanying labels.
- Train and Test sets are split into respective files.
### Data Instances
Tweet ID and the appropriatelabels
### Data Fields
Tweet ID and appropriate labels (binary label applicable for a data point) and multiple labels for each Tweet ID
### Data Splits
- Train: 7979
- Test: 1996
## Dataset Creation
### Curation Rationale
- Twitter was the major source of all the public discloures of sexual abuse incidents during the #MeToo movement.
- People expressed their opinions over issues which were previously missing from the social media space.
- This provides an option to study the linguistic behaviours of social media users in an informal setting,
therefore the authors decide to curate this annotated dataset.
- The authors expect this dataset would be of great interest and use to both computational and socio-linguists.
- For computational linguists, it provides an opportunity to model three new complex dialogue acts (allegation, refutation, and justification) and also to study how these acts interact with some of the other linguistic components like stance, hate, and sarcasm. For socio-linguists, it provides an opportunity to explore how a movement manifests in social media.
### Source Data
- Source of all the data points in this dataset is Twitter.
#### Initial Data Collection and Normalization
- All the tweets are mined from Twitter with initial search paramters identified using keywords from the #MeToo movement.
- Redundant keywords were removed based on manual inspection.
- Public streaming APIs of Twitter were used for querying with the selected keywords.
- Based on text de-duplication and cosine similarity score, the set of tweets were pruned.
- Non english tweets were removed.
- The final set was labelled by experts with the majority label taken into the account for deciding the final label.
- Please refer to this paper for detailed information: https://ojs.aaai.org//index.php/ICWSM/article/view/7292
#### Who are the source language producers?
Please refer to this paper for detailed information: https://ojs.aaai.org//index.php/ICWSM/article/view/7292
### Annotations
#### Annotation process
- The authors chose against crowd sourcing for labeling this dataset due to its highly sensitive nature.
- The annotators are domain experts having degress in advanced clinical psychology and gender studies.
- They were provided a guidelines document with instructions about each task and its definitions, labels and examples.
- They studied the document, worked a few examples to get used to this annotation task.
- They also provided feedback for improving the class definitions.
- The annotation process is not mutually exclusive, implying that presence of one label does not mean the
absence of the other one.
#### Who are the annotators?
- The annotators are domain experts having a degree in clinical psychology and gender studies.
- Please refer to the accompnaying paper for a detailed annotation process.
### Personal and Sensitive Information
- Considering Twitters policy for distribution of data, only Tweet ID and applicable labels are shared for the public use.
- It is highly encouraged to use this dataset for scientific purposes only.
- This dataset collection completely follows the Twitter mandated guidelines for distribution and usage.
## Considerations for Using the Data
### Social Impact of Dataset
- The authors of this dataset do not intend to conduct a population centric analysis of #MeToo movement on Twitter.
- The authors acknowledge that findings from this dataset cannot be used as-is for any direct social intervention, these
should be used to assist already existing human intervention tools and therapies.
- Enough care has been taken to ensure that this work comes of as trying to target a specific person for their
personal stance of issues pertaining to the #MeToo movement.
- The authors of this work do not aim to vilify anyone accused in the #MeToo movement in any manner.
- Please refer to the ethics and discussion section of the mentioned publication for appropriate sharing of this dataset
and social impact of this work.
### Discussion of Biases
- The #MeToo movement acted as a catalyst for implementing social policy changes to benefit the members of
community affected by sexual abuse.
- Any work undertaken on this dataset should aim to minimize the bias against minority groups which
might amplified in cases of sudden outburst of public reactions over sensitive social media discussions.
### Other Known Limitations
- Considering privacy concerns, social media practitioners should be aware of making automated interventions
to aid the victims of sexual abuse as some people might not prefer to disclose their notions.
- Concerned social media users might also repeal their social information, if they found out that their
information is being used for computational purposes, hence it is important seek subtle individual consent
before trying to profile authors involved in online discussions to uphold personal privacy.
## Additional Information
Please refer to this link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/JN4EYU
### Dataset Curators
- If you use the corpus in a product or application, then please credit the authors
and [Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi]
(http://midas.iiitd.edu.in) appropriately.
Also, if you send us an email, we will be thrilled to know about how you have used the corpus.
- If interested in commercial use of the corpus, send email to midas@iiitd.ac.in.
- Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi, India
disclaims any responsibility for the use of the corpus and does not provide technical support.
However, the contact listed above will be happy to respond to queries and clarifications
- Please feel free to send us an email:
- with feedback regarding the corpus.
- with information on how you have used the corpus.
- if interested in having us analyze your social media data.
- if interested in a collaborative research project.
### Licensing Information
[More Information Needed]
### Citation Information
Please cite the following publication if you make use of the dataset: https://ojs.aaai.org/index.php/ICWSM/article/view/7292
```
@article{Gautam_Mathur_Gosangi_Mahata_Sawhney_Shah_2020, title={#MeTooMA: Multi-Aspect Annotations of Tweets Related to the MeToo Movement}, volume={14}, url={https://aaai.org/ojs/index.php/ICWSM/article/view/7292}, abstractNote={<p>In this paper, we present a dataset containing 9,973 tweets related to the MeToo movement that were manually annotated for five different linguistic aspects: relevance, stance, hate speech, sarcasm, and dialogue acts. We present a detailed account of the data collection and annotation processes. The annotations have a very high inter-annotator agreement (0.79 to 0.93 k-alpha) due to the domain expertise of the annotators and clear annotation instructions. We analyze the data in terms of geographical distribution, label correlations, and keywords. Lastly, we present some potential use cases of this dataset. We expect this dataset would be of great interest to psycholinguists, socio-linguists, and computational linguists to study the discursive space of digitally mobilized social movements on sensitive issues like sexual harassment.</p&gt;}, number={1}, journal={Proceedings of the International AAAI Conference on Web and Social Media}, author={Gautam, Akash and Mathur, Puneet and Gosangi, Rakesh and Mahata, Debanjan and Sawhney, Ramit and Shah, Rajiv Ratn}, year={2020}, month={May}, pages={209-216} }
```
| 1,611 | text: adding metooma dataset
This PR adds the #MeToo MA dataset. It presents multi-label data points for tweets mined in the backdrop of the #MeToo movement. The dataset includes data points in the form of Tweet ids and appropriate labels. Please refer to the accompanying paper for detailed information regarding annotation, collection, and guidelines.
Paper: https://ojs.aaai.org/index.php/ICWSM/article/view/7292
Dataset Link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/JN4EYU
YAML tags:
annotations_creators:
- expert-generated
language_creators:
- found
languages:
- en
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
- text-retrieval
task_ids:
- multi-class-classification
- multi-label-classification
# Dataset Card for #MeTooMA dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/JN4EYU
- **Paper:** https://ojs.aaai.org//index.php/ICWSM/article/view/7292
- **Point of Contact:** https://github.com/midas-research/MeTooMA
### Dataset Summary
- The dataset consists of tweets belonging to #MeToo movement on Twitter, labelled into different categories.
- This dataset includes more data points and has more labels than any of the previous datasets in that contain social media
posts about sexual abuse discloures. Please refer to the Related Datasets of the publication for a detailed information about this.
- Due to Twitters development policies, the authors provide only the tweet IDs and corresponding labels,
other data can be fetched via Twitter API.
- The data has been labelled by experts, with the majority taken into the account for deciding the final label.
- The authors provide these labels for each of the tweets.
- Relevance
- Directed Hate
- Generalized Hate
- Sarcasm
- Allegation
- Justification
- Refutation
- Support
- Oppose
- The definitions for each task/label is in the main publication.
- Please refer to the accompanying paper https://aaai.org/ojs/index.php/ICWSM/article/view/7292 for statistical analysis on the textual data
extracted from this dataset.
- The language of all the tweets in this dataset is English
- Time period: October 2018 - December 2018
- Suggested Use Cases of this dataset:
- Evaluating usage of linguistic acts such as: hate-spech and sarcasm in the incontext of public sexual abuse discloures.
- Extracting actionable insights and virtual dynamics of gender roles in sexual abuse revelations.
- Identifying how influential people were potrayed on public platform in the
events of mass social movements.
- Polarization analysis based on graph simulations of social nodes of users involved
in the #MeToo movement.
### Supported Tasks and Leaderboards
Multi Label and Multi-Class Classification
### Languages
English
## Dataset Structure
- The dataset is structured into CSV format with TweetID and accompanying labels.
- Train and Test sets are split into respective files.
### Data Instances
Tweet ID and the appropriatelabels
### Data Fields
Tweet ID and appropriate labels (binary label applicable for a data point) and multiple labels for each Tweet ID
### Data Splits
- Train: 7979
- Test: 1996
## Dataset Creation
### Curation Rationale
- Twitter was the major source of all the public discloures of sexual abuse incidents during the #MeToo movement.
- People expressed their opinions over issues which were previously missing from the social media space.
- This provides an option to study the linguistic behaviours of social media users in an informal setting,
therefore the authors decide to curate this annotated dataset.
- The authors expect this dataset would be of great interest and use to both computational and socio-linguists.
- For computational linguists, it provides an opportunity to model three new complex dialogue acts (allegation, refutation, and justification) and also to study how these acts interact with some of the other linguistic components like stance, hate, and sarcasm. For socio-linguists, it provides an opportunity to explore how a movement manifests in social media.
### Source Data
- Source of all the data points in this dataset is Twitter.
#### Initial Data Collection and Normalization
- All the tweets are mined from Twitter with initial search paramters identified using keywords from the #MeToo movement.
- Redundant keywords were removed based on manual inspection.
- Public streaming APIs of Twitter were used for querying with the selected keywords.
- Based on text de-duplication and cosine similarity score, the set of tweets were pruned.
- Non english tweets were removed.
- The final set was labelled by experts with the majority label taken into the account for deciding the final label.
- Please refer to this paper for detailed information: https://ojs.aaai.org//index.php/ICWSM/article/view/7292
#### Who are the source language producers?
Please refer to this paper for detailed information: https://ojs.aaai.org//index.php/ICWSM/article/view/7292
### Annotations
#### Annotation process
- The authors chose against crowd sourcing for labeling this dataset due to its highly sensitive nature.
- The annotators are domain experts having degress in advanced clinical psychology and gender studies.
- They were provided a guidelines document with instructions about each task and its definitions, labels and examples.
- They studied the document, worked a few examples to get used to this annotation task.
- They also provided feedback for improving the class definitions.
- The annotation process is not mutually exclusive, implying that presence of one label does not mean the
absence of the other one.
#### Who are the annotators?
- The annotators are domain experts having a degree in clinical psychology and gender studies.
- Please refer to the accompnaying paper for a detailed annotation process.
### Personal and Sensitive Information
- Considering Twitters policy for distribution of data, only Tweet ID and applicable labels are shared for the public use.
- It is highly encouraged to use this dataset for scientific purposes only.
- This dataset collection completely follows the Twitter mandated guidelines for distribution and usage.
## Considerations for Using the Data
### Social Impact of Dataset
- The authors of this dataset do not intend to conduct a population centric analysis of #MeToo movement on Twitter.
- The authors acknowledge that findings from this dataset cannot be used as-is for any direct social intervention, these
should be used to assist already existing human intervention tools and therapies.
- Enough care has been taken to ensure that this work comes of as trying to target a specific person for their
personal stance of issues pertaining to the #MeToo movement.
- The authors of this work do not aim to vilify anyone accused in the #MeToo movement in any manner.
- Please refer to the ethics and discussion section of the mentioned publication for appropriate sharing of this dataset
and social impact of this work.
### Discussion of Biases
- The #MeToo movement acted as a catalyst for implementing social policy changes to benefit the members of
community affected by sexual abuse.
- Any work undertaken on this dataset should aim to minimize the bias against minority groups which
might amplified in cases of sudden outburst of public reactions over sensitive social media discussions.
### Other Known Limitations
- Considering privacy concerns, social media practitioners should be aware of making automated interventions
to aid the victims of sexual abuse as some people might not prefer to disclose their notions.
- Concerned social media users might also repeal their social information, if they found out that their
information is being used for computational purposes, hence it is important seek subtle individual consent
before trying to profile authors involved in online discussions to uphold personal privacy.
## Additional Information
Please refer to this link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/JN4EYU
### Dataset Curators
- If you use the corpus in a product or application, then please credit the authors
and [Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi]
(http://midas.iiitd.edu.in) appropriately.
Also, if you send us an email, we will be thrilled to know about how you have used the corpus.
- If interested in commercial use of the corpus, send email to midas@iiitd.ac.in.
- Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi, India
disclaims any responsibility for the use of the corpus and does not provide technical support.
However, the contact listed above will be happy to respond to queries and clarifications
- Please feel free to send us an email:
- with feedback regarding the corpus.
- with information on how you have used the corpus.
- if interested in having us analyze your social media data.
- if interested in a collaborative research project.
### Licensing Information
[More Information Needed]
### Citation Information
Please cite the following publication if you make use of the dataset: https://ojs.aaai.org/index.php/ICWSM/article/view/7292
```
@article{Gautam_Mathur_Gosangi_Mahata_Sawhney_Shah_2020, title={#MeTooMA: Multi-Aspect Annotations of Tweets Related to the MeToo Movement}, volume={14}, url={https://aaai.org/ojs/index.php/ICWSM/article/view/7292}, abstractNote={<p>In this paper, we present a dataset containing 9,973 tweets related to the MeToo movement that were manually annotated for five different linguistic aspects: relevance, stance, hate speech, sarcasm, and dialogue acts. We present a detailed account of the data collection and annotation processes. The annotations have a very high inter-annotator agreement (0.79 to 0.93 k-alpha) due to the domain expertise of the annotators and clear annotation instructions. We analyze the data in terms of geographical distribution, label correlations, and keywords. Lastly, we present some potential use cases of this dataset. We expect this dataset would be of great interest to psycholinguists, socio-linguists, and computational linguists to study the discursive space of digitally mobilized social movements on sensitive issues like sexual harassment.</p&gt;}, number={1}, journal={Proceedings of the International AAAI Conference on Web and Social Media}, author={Gautam, Akash and Mathur, Puneet and Gosangi, Rakesh and Mahata, Debanjan and Sawhney, Ramit and Shah, Rajiv Ratn}, year={2020}, month={May}, pages={209-216} }
```
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|
https://github.com/huggingface/datasets/pull/932 | adding metooma dataset | Hi, @lhoestq I have resolved all the comments you have raised. Can you review the PR again? However, I do need assistance on how to remove other files that came along in my PR. Should I manually delete unwanted files from the PR raised? | 44 | text: adding metooma dataset
Hi, @lhoestq I have resolved all the comments you have raised. Can you review the PR again? However, I do need assistance on how to remove other files that came along in my PR. Should I manually delete unwanted files from the PR raised? | [
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|
https://github.com/huggingface/datasets/pull/932 | adding metooma dataset | I am closing this PR, @lhoestq please review this PR instead https://github.com/huggingface/datasets/pull/975 where I have removed the unwanted files of other datasets and addressed each of your points. | 28 | text: adding metooma dataset
I am closing this PR, @lhoestq please review this PR instead https://github.com/huggingface/datasets/pull/975 where I have removed the unwanted files of other datasets and addressed each of your points. | [
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|
https://github.com/huggingface/datasets/pull/926 | add inquisitive | `dummy_data` right now contains all article files, keeping only the required articles for dummy data fails the dummy data test.
Any idea ? | Adding inquisitive qg dataset
More info: https://github.com/wjko2/INQUISITIVE | 23 | text: add inquisitive
Adding inquisitive qg dataset
More info: https://github.com/wjko2/INQUISITIVE
`dummy_data` right now contains all article files, keeping only the required articles for dummy data fails the dummy data test.
Any idea ? | [
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https://github.com/huggingface/datasets/pull/926 | add inquisitive | > `dummy_data` right now contains all article files, keeping only the required articles for dummy data fails the dummy data test.
> Any idea ?
We should definitely find a way to make it work with only a few articles.
If it doesn't work right now for dummy data, I guess it's because it tries to load every single article file ?
If so, then maybe you can use `os.listdir` method to first check all the data files available in the path where the `articles.tgz` file is extracted. Then you can simply iter through the data files and depending on their ID, include them in the train or test set. With this method you should be able to have only a few articles files per split in the dummy data. Does that make sense ? | Adding inquisitive qg dataset
More info: https://github.com/wjko2/INQUISITIVE | 135 | text: add inquisitive
Adding inquisitive qg dataset
More info: https://github.com/wjko2/INQUISITIVE
> `dummy_data` right now contains all article files, keeping only the required articles for dummy data fails the dummy data test.
> Any idea ?
We should definitely find a way to make it work with only a few articles.
If it doesn't work right now for dummy data, I guess it's because it tries to load every single article file ?
If so, then maybe you can use `os.listdir` method to first check all the data files available in the path where the `articles.tgz` file is extracted. Then you can simply iter through the data files and depending on their ID, include them in the train or test set. With this method you should be able to have only a few articles files per split in the dummy data. Does that make sense ? | [
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https://github.com/huggingface/datasets/pull/926 | add inquisitive | fixed! so the issue was, `articles_ids` were prepared based on the number of files in articles dir, so for dummy data questions it was not able to load some articles due to incorrect ids and the test was failing | Adding inquisitive qg dataset
More info: https://github.com/wjko2/INQUISITIVE | 39 | text: add inquisitive
Adding inquisitive qg dataset
More info: https://github.com/wjko2/INQUISITIVE
fixed! so the issue was, `articles_ids` were prepared based on the number of files in articles dir, so for dummy data questions it was not able to load some articles due to incorrect ids and the test was failing | [
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https://github.com/huggingface/datasets/pull/925 | Add Turku NLP Corpus for Finnish NER | > Did you generate the dummy data with the cli or manually ?
It was generated by the cli. Do you want me to make it smaller keep it like this?
| 31 | text: Add Turku NLP Corpus for Finnish NER
> Did you generate the dummy data with the cli or manually ?
It was generated by the cli. Do you want me to make it smaller keep it like this?
| [
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|
https://github.com/huggingface/datasets/pull/923 | Add CC-100 dataset | Hello @lhoestq, I would like just to ask you if it is OK that I include this feature 9f32ba1 in this PR or you would prefer to have it in a separate one.
I was wondering whether include also a test, but I did not find any test for the other file formats... | Add CC-100.
Close #773 | 53 | text: Add CC-100 dataset
Add CC-100.
Close #773
Hello @lhoestq, I would like just to ask you if it is OK that I include this feature 9f32ba1 in this PR or you would prefer to have it in a separate one.
I was wondering whether include also a test, but I did not find any test for the other file formats... | [
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https://github.com/huggingface/datasets/pull/923 | Add CC-100 dataset | Hi ! Sure that would be valuable to support .xz files. Feel free to open a separate PR for this.
And feel free to create the first test case for extracting compressed files if you have some inspiration (maybe create test_file_utils.py ?). We can still spend more time on tests next week when the sprint is over though so don't spend too much time on it. | Add CC-100.
Close #773 | 66 | text: Add CC-100 dataset
Add CC-100.
Close #773
Hi ! Sure that would be valuable to support .xz files. Feel free to open a separate PR for this.
And feel free to create the first test case for extracting compressed files if you have some inspiration (maybe create test_file_utils.py ?). We can still spend more time on tests next week when the sprint is over though so don't spend too much time on it. | [
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https://github.com/huggingface/datasets/pull/923 | Add CC-100 dataset | Thanks for adding support for `.xz` files :)
Feel free to rebase from master to include it in your PR | Add CC-100.
Close #773 | 20 | text: Add CC-100 dataset
Add CC-100.
Close #773
Thanks for adding support for `.xz` files :)
Feel free to rebase from master to include it in your PR | [
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https://github.com/huggingface/datasets/pull/923 | Add CC-100 dataset | @lhoestq DONE; I have merged instead, to avoid changing the history of my public PR ;) | Add CC-100.
Close #773 | 16 | text: Add CC-100 dataset
Add CC-100.
Close #773
@lhoestq DONE; I have merged instead, to avoid changing the history of my public PR ;) | [
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https://github.com/huggingface/datasets/pull/923 | Add CC-100 dataset | Hi @lhoestq, I would need that you generate the dataset_infos.json and the dummy data for this dataset with a bigger computer. Sorry, but my laptop did not succeed... | Add CC-100.
Close #773 | 28 | text: Add CC-100 dataset
Add CC-100.
Close #773
Hi @lhoestq, I would need that you generate the dataset_infos.json and the dummy data for this dataset with a bigger computer. Sorry, but my laptop did not succeed... | [
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https://github.com/huggingface/datasets/pull/923 | Add CC-100 dataset | Looks like #1456 added CC100 already.
The difference with your approach is that this implementation uses the `BuilderConfig` parameters to allow the creation of custom configs for all the languages, without having to specify them in the `BUILDER_CONFIGS` class attribute.
For example even if the dataset doesn't have a config for english already, you can still load the english CC100 with
```python
from datasets import load_dataset
load_dataset("cc100", lang="en")
``` | Add CC-100.
Close #773 | 69 | text: Add CC-100 dataset
Add CC-100.
Close #773
Looks like #1456 added CC100 already.
The difference with your approach is that this implementation uses the `BuilderConfig` parameters to allow the creation of custom configs for all the languages, without having to specify them in the `BUILDER_CONFIGS` class attribute.
For example even if the dataset doesn't have a config for english already, you can still load the english CC100 with
```python
from datasets import load_dataset
load_dataset("cc100", lang="en")
``` | [
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https://github.com/huggingface/datasets/pull/923 | Add CC-100 dataset | @lhoestq, oops!! I remember having assigned this dataset to me in the Google sheet, besides having mentioned the corresponding issue in the Pull Request... Nevermind! :) | Add CC-100.
Close #773 | 26 | text: Add CC-100 dataset
Add CC-100.
Close #773
@lhoestq, oops!! I remember having assigned this dataset to me in the Google sheet, besides having mentioned the corresponding issue in the Pull Request... Nevermind! :) | [
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https://github.com/huggingface/datasets/pull/923 | Add CC-100 dataset | Yes indeed I can see that...
Sorry for noticing that only now
The code of the other PR ended up being pretty close to yours though
If you want to add more details to the cc100 dataset card or in the script feel to do so, any addition is welcome | Add CC-100.
Close #773 | 50 | text: Add CC-100 dataset
Add CC-100.
Close #773
Yes indeed I can see that...
Sorry for noticing that only now
The code of the other PR ended up being pretty close to yours though
If you want to add more details to the cc100 dataset card or in the script feel to do so, any addition is welcome | [
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https://github.com/huggingface/datasets/pull/922 | Add XOR QA Dataset | Hi @sumanthd17
Looks like a good start! You will also need to add a Dataset card, following the instructions given [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#manually-tag-the-dataset-and-write-the-dataset-card) | 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 | 21 | text: Add XOR QA Dataset
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
Hi @sumanthd17
Looks like a good start! You will also need to add a Dataset card, following the instructions given [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#manually-tag-the-dataset-and-write-the-dataset-card) | [
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https://github.com/huggingface/datasets/pull/922 | Add XOR QA Dataset | I followed the instructions mentioned there but my dataset isn't showing up in the dropdown list. Am I missing something here? @yjernite | 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 | 22 | text: Add XOR QA Dataset
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
I followed the instructions mentioned there but my dataset isn't showing up in the dropdown list. Am I missing something here? @yjernite | [
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https://github.com/huggingface/datasets/pull/922 | Add XOR QA Dataset | > I followed the instructions mentioned there but my dataset isn't showing up in the dropdown list. Am I missing something here? @yjernite
The best way is to run the tagging app locally and provide it the location to the `dataset_infos.json` after you've run the CLI:
https://github.com/huggingface/datasets-tagging
| 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 | 47 | text: Add XOR QA Dataset
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
> I followed the instructions mentioned there but my dataset isn't showing up in the dropdown list. Am I missing something here? @yjernite
The best way is to run the tagging app locally and provide it the location to the `dataset_infos.json` after you've run the CLI:
https://github.com/huggingface/datasets-tagging
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] |
https://github.com/huggingface/datasets/pull/922 | Add XOR QA Dataset | This is a really good data card!!
Small changes to make it even better:
- Tags: the dataset has both "original" data and data that is "extended" from a source dataset: TydiQA - you should choose both options in the tagging apps
- The language and annotation creator tags are off: the language here is the questions: I understand it's a mix of crowd-sourced and expert-generated? Is there any machine translation involved? The annotations are the span selections: is that crowd-sourced?
- Personal and sensitive information: there should be a statement there, even if only to say that none could be found or that it only mentions public figures | 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 | 109 | text: Add XOR QA Dataset
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
This is a really good data card!!
Small changes to make it even better:
- Tags: the dataset has both "original" data and data that is "extended" from a source dataset: TydiQA - you should choose both options in the tagging apps
- The language and annotation creator tags are off: the language here is the questions: I understand it's a mix of crowd-sourced and expert-generated? Is there any machine translation involved? The annotations are the span selections: is that crowd-sourced?
- Personal and sensitive information: there should be a statement there, even if only to say that none could be found or that it only mentions public figures | [
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https://github.com/huggingface/datasets/pull/920 | add dream dataset | > Awesome good job !
>
> Could you also add a dataset card using the template guide here : https://github.com/huggingface/datasets/blob/master/templates/README_guide.md
> If you can't fill some fields then just leave `[N/A]`
Quick amendment: `[N/A]` is for fields that are not relevant: if you can't find the information just leave `[More Information Needed]` | Adding Dream: a Dataset and for Dialogue-Based Reading Comprehension
More details:
https://dataset.org/dream/
https://github.com/nlpdata/dream | 53 | text: add dream dataset
Adding Dream: a Dataset and for Dialogue-Based Reading Comprehension
More details:
https://dataset.org/dream/
https://github.com/nlpdata/dream
> Awesome good job !
>
> Could you also add a dataset card using the template guide here : https://github.com/huggingface/datasets/blob/master/templates/README_guide.md
> If you can't fill some fields then just leave `[N/A]`
Quick amendment: `[N/A]` is for fields that are not relevant: if you can't find the information just leave `[More Information Needed]` | [
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https://github.com/huggingface/datasets/pull/920 | add dream dataset | @lhoestq since datset cards are optional for this sprint I'll add those later. Good for merge. | Adding Dream: a Dataset and for Dialogue-Based Reading Comprehension
More details:
https://dataset.org/dream/
https://github.com/nlpdata/dream | 16 | text: add dream dataset
Adding Dream: a Dataset and for Dialogue-Based Reading Comprehension
More details:
https://dataset.org/dream/
https://github.com/nlpdata/dream
@lhoestq since datset cards are optional for this sprint I'll add those later. Good for merge. | [
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https://github.com/huggingface/datasets/pull/920 | add dream dataset | Indeed we only require the tags to be added now (the yaml part at the top of the dataset card).
Could you add them please ?
You can find more infos here : https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#tag-the-dataset-and-write-the-dataset-card | Adding Dream: a Dataset and for Dialogue-Based Reading Comprehension
More details:
https://dataset.org/dream/
https://github.com/nlpdata/dream | 34 | text: add dream dataset
Adding Dream: a Dataset and for Dialogue-Based Reading Comprehension
More details:
https://dataset.org/dream/
https://github.com/nlpdata/dream
Indeed we only require the tags to be added now (the yaml part at the top of the dataset card).
Could you add them please ?
You can find more infos here : https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#tag-the-dataset-and-write-the-dataset-card | [
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https://github.com/huggingface/datasets/pull/920 | add dream dataset | @lhoestq could you tell me why these tests are failing, they don't seem related to this PR. | Adding Dream: a Dataset and for Dialogue-Based Reading Comprehension
More details:
https://dataset.org/dream/
https://github.com/nlpdata/dream | 17 | text: add dream dataset
Adding Dream: a Dataset and for Dialogue-Based Reading Comprehension
More details:
https://dataset.org/dream/
https://github.com/nlpdata/dream
@lhoestq could you tell me why these tests are failing, they don't seem related to this PR. | [
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https://github.com/huggingface/datasets/pull/917 | Addition of Concode Dataset | Testing command doesn't work
###trace
-- Docs: https://docs.pytest.org/en/stable/warnings.html
========================================================= short test summary info ==========================================================
ERROR tests/test_dataset_common.py - absl.testing.parameterized.NoTestsError: parameterized test decorators did not generate any tests. Ma...
====================================================== 2 warnings, 1 error in 54.23s =======================================================
ERROR: not found: G:\Work Related\hf\datasets\tests\test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_concode
(no name 'G:\\Work Related\\hf\\datasets\\tests\\test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_concode' in any of [<Module test_dataset_common.py>])
| ##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 | 49 | text: Addition of Concode Dataset
##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
Testing command doesn't work
###trace
-- Docs: https://docs.pytest.org/en/stable/warnings.html
========================================================= short test summary info ==========================================================
ERROR tests/test_dataset_common.py - absl.testing.parameterized.NoTestsError: parameterized test decorators did not generate any tests. Ma...
====================================================== 2 warnings, 1 error in 54.23s =======================================================
ERROR: not found: G:\Work Related\hf\datasets\tests\test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_concode
(no name 'G:\\Work Related\\hf\\datasets\\tests\\test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_concode' in any of [<Module test_dataset_common.py>])
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https://github.com/huggingface/datasets/pull/917 | Addition of Concode Dataset | Hello @lhoestq Test checks are passing in my local, but the commit fails in ci. Any idea onto why?
#### Dummy Dataset Test
====================================================== 1 passed, 6 warnings in 7.14s =======================================================
#### Real Dataset Test
====================================================== 1 passed, 6 warnings in 25.54s ====================================================== | ##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 | 43 | text: Addition of Concode Dataset
##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
Hello @lhoestq Test checks are passing in my local, but the commit fails in ci. Any idea onto why?
#### Dummy Dataset Test
====================================================== 1 passed, 6 warnings in 7.14s =======================================================
#### Real Dataset Test
====================================================== 1 passed, 6 warnings in 25.54s ====================================================== | [
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https://github.com/huggingface/datasets/pull/917 | Addition of Concode Dataset | @reshinthadithyan that's a great start! You will also need to add a Dataset card, following the instructions given [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#manually-tag-the-dataset-and-write-the-dataset-card) | ##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 | 19 | text: Addition of Concode Dataset
##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
@reshinthadithyan that's a great start! You will also need to add a Dataset card, following the instructions given [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#manually-tag-the-dataset-and-write-the-dataset-card) | [
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https://github.com/huggingface/datasets/pull/917 | Addition of Concode Dataset | > @reshinthadithyan that's a great start! You will also need to add a Dataset card, following the instructions given [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#manually-tag-the-dataset-and-write-the-dataset-card)
Hello @yjernite I'm facing issues in using the datasets-tagger Refer #1 in datasets-tagger. Thanks | ##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 | 34 | text: Addition of Concode Dataset
##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
> @reshinthadithyan that's a great start! You will also need to add a Dataset card, following the instructions given [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#manually-tag-the-dataset-and-write-the-dataset-card)
Hello @yjernite I'm facing issues in using the datasets-tagger Refer #1 in datasets-tagger. Thanks | [
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https://github.com/huggingface/datasets/pull/917 | Addition of Concode Dataset | > > @reshinthadithyan that's a great start! You will also need to add a Dataset card, following the instructions given [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#manually-tag-the-dataset-and-write-the-dataset-card)
>
> Hello @yjernite I'm facing issues in using the datasets-tagger Refer #1 in datasets-tagger. Thanks
Hi @reshinthadithyan ! Did you try with the latest version of the tagger? What issues are you facing?
We're also relaxed the dataset requirement for now, you'll only add to add the tags :) | ##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 | 71 | text: Addition of Concode Dataset
##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
> > @reshinthadithyan that's a great start! You will also need to add a Dataset card, following the instructions given [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#manually-tag-the-dataset-and-write-the-dataset-card)
>
> Hello @yjernite I'm facing issues in using the datasets-tagger Refer #1 in datasets-tagger. Thanks
Hi @reshinthadithyan ! Did you try with the latest version of the tagger? What issues are you facing?
We're also relaxed the dataset requirement for now, you'll only add to add the tags :) | [
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https://github.com/huggingface/datasets/pull/917 | Addition of Concode Dataset | Could you work on another branch when adding different datasets ?
The idea is to have one PR per dataset | ##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 | 20 | text: Addition of Concode Dataset
##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
Could you work on another branch when adding different datasets ?
The idea is to have one PR per dataset | [
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] |
https://github.com/huggingface/datasets/pull/917 | Addition of Concode Dataset | Thanks ! The github diff looks all clean now :)
To fix the CI you just need to rebase from master
Don't forget to add the tags of the dataset card. It's the yaml part at the top of the dataset card
More infor here : https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#tag-the-dataset-and-write-the-dataset-card
The issue you had with the tagger should be fixed now by https://github.com/huggingface/datasets-tagging/pull/5
| ##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 | 60 | text: Addition of Concode Dataset
##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
Thanks ! The github diff looks all clean now :)
To fix the CI you just need to rebase from master
Don't forget to add the tags of the dataset card. It's the yaml part at the top of the dataset card
More infor here : https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#tag-the-dataset-and-write-the-dataset-card
The issue you had with the tagger should be fixed now by https://github.com/huggingface/datasets-tagging/pull/5
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https://github.com/huggingface/datasets/pull/916 | Add Swedish NER Corpus | @abhishekkrthakur we want to keep track of the information that is and isn't in the dataset cards so we're asking everyone to use the full template :) If there is some information in there that you really can't find or don't feel qualified to add, you can just leave the `[More Information Needed]` text | 54 | text: Add Swedish NER Corpus
@abhishekkrthakur we want to keep track of the information that is and isn't in the dataset cards so we're asking everyone to use the full template :) If there is some information in there that you really can't find or don't feel qualified to add, you can just leave the `[More Information Needed]` text | [
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] |
|
https://github.com/huggingface/datasets/pull/914 | Add list_github_datasets api for retrieving dataset name list in github repo | We can look into removing some of the attributes from `GET /api/datasets` to make it smaller/faster, what do you think @lhoestq? | 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 :). | 21 | text: Add list_github_datasets api for retrieving dataset name list in github repo
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 :).
We can look into removing some of the attributes from `GET /api/datasets` to make it smaller/faster, what do you think @lhoestq? | [
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] |
https://github.com/huggingface/datasets/pull/914 | Add list_github_datasets api for retrieving dataset name list in github repo | > We can look into removing some of the attributes from `GET /api/datasets` to make it smaller/faster, what do you think @lhoestq?
yes at least remove all the `dummy_data.zip` | 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 :). | 29 | text: Add list_github_datasets api for retrieving dataset name list in github repo
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 :).
> We can look into removing some of the attributes from `GET /api/datasets` to make it smaller/faster, what do you think @lhoestq?
yes at least remove all the `dummy_data.zip` | [
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] |
https://github.com/huggingface/datasets/pull/914 | Add list_github_datasets api for retrieving dataset name list in github repo | `GET /api/datasets` should now be much faster. @zhuzilin can you check if `list_datasets` is now faster for you? | 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 :). | 18 | text: Add list_github_datasets api for retrieving dataset name list in github repo
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 :).
`GET /api/datasets` should now be much faster. @zhuzilin can you check if `list_datasets` is now faster for you? | [
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https://github.com/huggingface/datasets/pull/914 | Add list_github_datasets api for retrieving dataset name list in github repo | > `GET /api/datasets` should now be much faster. @zhuzilin can you check if `list_datasets` is now faster for you?
Yes, much faster! Thank you! | 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 :). | 24 | text: Add list_github_datasets api for retrieving dataset name list in github repo
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 :).
> `GET /api/datasets` should now be much faster. @zhuzilin can you check if `list_datasets` is now faster for you?
Yes, much faster! Thank you! | [
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] |
https://github.com/huggingface/datasets/pull/913 | My new dataset PEC | Thanks for adding this one :)
To fix the check_code_quality, please run `make style` with the latest version of black, isort, flake8
To fix the test_no_encoding_on_file_open, make sure to specify the encoding each time you call `open()` on a text file.
For example : `encoding="utf-8"`
To fix the test_load_dataset_pec , you must add the dummy_data.zip file. It is used to test the dataset script and make sure it runs fine. To add it, please refer to the steps in https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-add-a-dataset
| A new dataset PEC published in EMNLP 2020. | 80 | text: My new dataset PEC
A new dataset PEC published in EMNLP 2020.
Thanks for adding this one :)
To fix the check_code_quality, please run `make style` with the latest version of black, isort, flake8
To fix the test_no_encoding_on_file_open, make sure to specify the encoding each time you call `open()` on a text file.
For example : `encoding="utf-8"`
To fix the test_load_dataset_pec , you must add the dummy_data.zip file. It is used to test the dataset script and make sure it runs fine. To add it, please refer to the steps in https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-add-a-dataset
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https://github.com/huggingface/datasets/pull/913 | My new dataset PEC | Could you also add a dataset card ? you can find a template here : https://github.com/huggingface/datasets/blob/master/templates/README.md
That would be awesome | A new dataset PEC published in EMNLP 2020. | 20 | text: My new dataset PEC
A new dataset PEC published in EMNLP 2020.
Could you also add a dataset card ? you can find a template here : https://github.com/huggingface/datasets/blob/master/templates/README.md
That would be awesome | [
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https://github.com/huggingface/datasets/pull/913 | My new dataset PEC | > Thanks for adding this one :)
>
> To fix the check_code_quality, please run `make style` with the latest version of black, isort, flake8
> To fix the test_no_encoding_on_file_open, make sure to specify the encoding each time you call `open()` on a text file.
> For example : `encoding="utf-8"`
> To fix the test_load_dataset_pec , you must add the dummy_data.zip file. It is used to test the dataset script and make sure it runs fine. To add it, please refer to the steps in https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-add-a-dataset
Thank you for the detailed suggestion.
I have added dummy_data but it still failed the DistributedDatasetTest check. My dataset has a central file (containing a python dict) that needs to be accessed by each data example. Is it because the central file cannot be distributed (which would lead to a partial dictionary)?
Specifically, the central file contains a dictionary of speakers with their attributes. Each data example is also associated with a speaker. As of now, I keep the central file and data files separately. If I remove the central file by appending the speaker attributes to each data example, then there would be lots of redundancy because there are lots of duplicate speakers in the data files. | A new dataset PEC published in EMNLP 2020. | 204 | text: My new dataset PEC
A new dataset PEC published in EMNLP 2020.
> Thanks for adding this one :)
>
> To fix the check_code_quality, please run `make style` with the latest version of black, isort, flake8
> To fix the test_no_encoding_on_file_open, make sure to specify the encoding each time you call `open()` on a text file.
> For example : `encoding="utf-8"`
> To fix the test_load_dataset_pec , you must add the dummy_data.zip file. It is used to test the dataset script and make sure it runs fine. To add it, please refer to the steps in https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-add-a-dataset
Thank you for the detailed suggestion.
I have added dummy_data but it still failed the DistributedDatasetTest check. My dataset has a central file (containing a python dict) that needs to be accessed by each data example. Is it because the central file cannot be distributed (which would lead to a partial dictionary)?
Specifically, the central file contains a dictionary of speakers with their attributes. Each data example is also associated with a speaker. As of now, I keep the central file and data files separately. If I remove the central file by appending the speaker attributes to each data example, then there would be lots of redundancy because there are lots of duplicate speakers in the data files. | [
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https://github.com/huggingface/datasets/pull/913 | My new dataset PEC | The `DistributedDatasetTest` fail and the changes of this PR are not related, there was just a bug in the CI. You can ignore it | A new dataset PEC published in EMNLP 2020. | 24 | text: My new dataset PEC
A new dataset PEC published in EMNLP 2020.
The `DistributedDatasetTest` fail and the changes of this PR are not related, there was just a bug in the CI. You can ignore it | [
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] |
https://github.com/huggingface/datasets/pull/913 | My new dataset PEC | > Really cool thanks !
>
> Could you make the dummy files smaller ? For example by reducing the size of persona.txt ?
> I also left a comment about the files concatenation. It would be cool to replace that with simple iterations through the different files.
>
> Then once this is done, you can add a dataset card using the template guide here : https://github.com/huggingface/datasets/blob/master/templates/README_guide.md
> If some fields can't be filled, just leave `[N/A]`
Small change: if you don't have the information for a field, please leave `[More Information Needed]` rather than `[N/A]`
The full information can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#manually-tag-the-dataset-and-write-the-dataset-card) | A new dataset PEC published in EMNLP 2020. | 104 | text: My new dataset PEC
A new dataset PEC published in EMNLP 2020.
> Really cool thanks !
>
> Could you make the dummy files smaller ? For example by reducing the size of persona.txt ?
> I also left a comment about the files concatenation. It would be cool to replace that with simple iterations through the different files.
>
> Then once this is done, you can add a dataset card using the template guide here : https://github.com/huggingface/datasets/blob/master/templates/README_guide.md
> If some fields can't be filled, just leave `[N/A]`
Small change: if you don't have the information for a field, please leave `[More Information Needed]` rather than `[N/A]`
The full information can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#manually-tag-the-dataset-and-write-the-dataset-card) | [
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https://github.com/huggingface/datasets/pull/909 | Add FiNER dataset | > That's really cool thank you !
>
> Could you also add a dataset card ?
> You can find a template here : https://github.com/huggingface/datasets/blob/master/templates/README.md
The full information for adding a dataset card can be found here :)
https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#manually-tag-the-dataset-and-write-the-dataset-card
| Hi,
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. | 40 | text: Add FiNER dataset
Hi,
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.
> That's really cool thank you !
>
> Could you also add a dataset card ?
> You can find a template here : https://github.com/huggingface/datasets/blob/master/templates/README.md
The full information for adding a dataset card can be found here :)
https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#manually-tag-the-dataset-and-write-the-dataset-card
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https://github.com/huggingface/datasets/pull/909 | Add FiNER dataset | @yjernite and @lhoestq I will add the dataset card a bit later in a separate PR if that's ok for you! | Hi,
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. | 21 | text: Add FiNER dataset
Hi,
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.
@yjernite and @lhoestq I will add the dataset card a bit later in a separate PR if that's ok for you! | [
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https://github.com/huggingface/datasets/pull/909 | Add FiNER dataset | Yes I want to re-emphasize if it was not clear that dataset cards are optional for the sprint.
Only the tags are required for merging a datasets.
Please try to enforce this rule as well @lhoestq and @yjernite | Hi,
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. | 38 | text: Add FiNER dataset
Hi,
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.
Yes I want to re-emphasize if it was not clear that dataset cards are optional for the sprint.
Only the tags are required for merging a datasets.
Please try to enforce this rule as well @lhoestq and @yjernite | [
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https://github.com/huggingface/datasets/pull/909 | Add FiNER dataset | Yes @stefan-it if you could just add the tags (the yaml part at the top of the dataset card) that'd be perfect :) | Hi,
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. | 23 | text: Add FiNER dataset
Hi,
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.
Yes @stefan-it if you could just add the tags (the yaml part at the top of the dataset card) that'd be perfect :) | [
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https://github.com/huggingface/datasets/pull/908 | Add dependency on black for tests | Sorry, I have just seen that it was already in `QUALITY_REQUIRE`.
For some reason it did not get installed on my virtual environment... | Add package 'black' as an installation requirement for tests. | 23 | text: Add dependency on black for tests
Add package 'black' as an installation requirement for tests.
Sorry, I have just seen that it was already in `QUALITY_REQUIRE`.
For some reason it did not get installed on my virtual environment... | [
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https://github.com/huggingface/datasets/pull/905 | Disallow backslash in urls | Looks like the test doesn't detect all the problems fixed by #907 , I'll fix that | Following #903 @albertvillanova noticed that there are sometimes bad usage of `os.path.join` in datasets scripts to create URLS. However this should be avoided since it doesn't work on windows.
I'm suggesting a test to make sure we that all the urls don't have backslashes in them in the datasets scripts.
The tests works by adding a callback feature to the MockDownloadManager used to test the dataset scripts. In a download callback I just make sure that the url is valid. | 16 | text: Disallow backslash in urls
Following #903 @albertvillanova noticed that there are sometimes bad usage of `os.path.join` in datasets scripts to create URLS. However this should be avoided since it doesn't work on windows.
I'm suggesting a test to make sure we that all the urls don't have backslashes in them in the datasets scripts.
The tests works by adding a callback feature to the MockDownloadManager used to test the dataset scripts. In a download callback I just make sure that the url is valid.
Looks like the test doesn't detect all the problems fixed by #907 , I'll fix that | [
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https://github.com/huggingface/datasets/pull/905 | Disallow backslash in urls | Ok found why it doesn't detect the problems fixed by #907 . That's because for all those datasets the urls are actually fine (no backslash) on windows, even if it uses `os.path.join`.
This is because of the behavior of `os.path.join` on windows when the first path ends with a slash :
```python
import os
os.path.join("https://test.com/foo", "bar.txt")
# 'https://test.com/foo\\bar.txt'
os.path.join("https://test.com/foo/", "bar.txt")
# 'https://test.com/foo/bar.txt'
```
However even though the urls are correct, this is definitely bad practice and we should never use `os.path.join` for urls | Following #903 @albertvillanova noticed that there are sometimes bad usage of `os.path.join` in datasets scripts to create URLS. However this should be avoided since it doesn't work on windows.
I'm suggesting a test to make sure we that all the urls don't have backslashes in them in the datasets scripts.
The tests works by adding a callback feature to the MockDownloadManager used to test the dataset scripts. In a download callback I just make sure that the url is valid. | 83 | text: Disallow backslash in urls
Following #903 @albertvillanova noticed that there are sometimes bad usage of `os.path.join` in datasets scripts to create URLS. However this should be avoided since it doesn't work on windows.
I'm suggesting a test to make sure we that all the urls don't have backslashes in them in the datasets scripts.
The tests works by adding a callback feature to the MockDownloadManager used to test the dataset scripts. In a download callback I just make sure that the url is valid.
Ok found why it doesn't detect the problems fixed by #907 . That's because for all those datasets the urls are actually fine (no backslash) on windows, even if it uses `os.path.join`.
This is because of the behavior of `os.path.join` on windows when the first path ends with a slash :
```python
import os
os.path.join("https://test.com/foo", "bar.txt")
# 'https://test.com/foo\\bar.txt'
os.path.join("https://test.com/foo/", "bar.txt")
# 'https://test.com/foo/bar.txt'
```
However even though the urls are correct, this is definitely bad practice and we should never use `os.path.join` for urls | [
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https://github.com/huggingface/datasets/pull/903 | Fix URL with backslash in Windows | Ah what do you have in mind for the tests ? I was thinking of adding a check in the MockDownloadManager used for tests based on dummy data. I'm creating a PR right now, I'd be happy to have your opinion | In Windows, `os.path.join` generates URLs containing backslashes, when the first "path" does not end with a slash.
In general, `os.path.join` should be avoided to generate URLs. | 41 | text: Fix URL with backslash in Windows
In Windows, `os.path.join` generates URLs containing backslashes, when the first "path" does not end with a slash.
In general, `os.path.join` should be avoided to generate URLs.
Ah what do you have in mind for the tests ? I was thinking of adding a check in the MockDownloadManager used for tests based on dummy data. I'm creating a PR right now, I'd be happy to have your opinion | [
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https://github.com/huggingface/datasets/pull/903 | Fix URL with backslash in Windows | Therefore, if you agree, I am removing all the rest of `os.path.join`, both from the code and the docs... | In Windows, `os.path.join` generates URLs containing backslashes, when the first "path" does not end with a slash.
In general, `os.path.join` should be avoided to generate URLs. | 19 | text: Fix URL with backslash in Windows
In Windows, `os.path.join` generates URLs containing backslashes, when the first "path" does not end with a slash.
In general, `os.path.join` should be avoided to generate URLs.
Therefore, if you agree, I am removing all the rest of `os.path.join`, both from the code and the docs... | [
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https://github.com/huggingface/datasets/pull/903 | Fix URL with backslash in Windows | If you spot other `os.path.join` for urls in dataset scripts or metrics scripts feel free to fix them.
In the library itself (/src/datasets) it should be fine since there are tests and a windows CI, but if you have doubts of some usage of `os.path.join` somewhere, let me know. | In Windows, `os.path.join` generates URLs containing backslashes, when the first "path" does not end with a slash.
In general, `os.path.join` should be avoided to generate URLs. | 49 | text: Fix URL with backslash in Windows
In Windows, `os.path.join` generates URLs containing backslashes, when the first "path" does not end with a slash.
In general, `os.path.join` should be avoided to generate URLs.
If you spot other `os.path.join` for urls in dataset scripts or metrics scripts feel free to fix them.
In the library itself (/src/datasets) it should be fine since there are tests and a windows CI, but if you have doubts of some usage of `os.path.join` somewhere, let me know. | [
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https://github.com/huggingface/datasets/pull/903 | Fix URL with backslash in Windows | Alright create the test in #905 .
The windows CI is failing for all the datasets that have bad usage of `os.path.join` for urls.
There are of course the ones you fixed in this PR (thanks again !) but I found others as well such as pg19 and blimp.
You can check the full list by looking at the CI failures of the commit 1ce3354 | In Windows, `os.path.join` generates URLs containing backslashes, when the first "path" does not end with a slash.
In general, `os.path.join` should be avoided to generate URLs. | 65 | text: Fix URL with backslash in Windows
In Windows, `os.path.join` generates URLs containing backslashes, when the first "path" does not end with a slash.
In general, `os.path.join` should be avoided to generate URLs.
Alright create the test in #905 .
The windows CI is failing for all the datasets that have bad usage of `os.path.join` for urls.
There are of course the ones you fixed in this PR (thanks again !) but I found others as well such as pg19 and blimp.
You can check the full list by looking at the CI failures of the commit 1ce3354 | [
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https://github.com/huggingface/datasets/pull/903 | Fix URL with backslash in Windows | I am merging this one as well as #906 that should fix all of the datasets.
Then I'll rebase #905 which adds the test that checks for bad urls and make sure it' all green now | In Windows, `os.path.join` generates URLs containing backslashes, when the first "path" does not end with a slash.
In general, `os.path.join` should be avoided to generate URLs. | 36 | text: Fix URL with backslash in Windows
In Windows, `os.path.join` generates URLs containing backslashes, when the first "path" does not end with a slash.
In general, `os.path.join` should be avoided to generate URLs.
I am merging this one as well as #906 that should fix all of the datasets.
Then I'll rebase #905 which adds the test that checks for bad urls and make sure it' all green now | [
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https://github.com/huggingface/datasets/pull/901 | Addition of Nl2Bash Dataset | Hello, thanks. I had a talk with the dataset authors, found out that the data now is obsolete and they'll get a stable version soon. So temporality closing the PR.
Although I have a question, What should _id_ be in the return statement? Should that be something like a start index (or) the type of split will do? Thanks. | ## 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
| 59 | text: Addition of Nl2Bash Dataset
## 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
Hello, thanks. I had a talk with the dataset authors, found out that the data now is obsolete and they'll get a stable version soon. So temporality closing the PR.
Although I have a question, What should _id_ be in the return statement? Should that be something like a start index (or) the type of split will do? Thanks. | [
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https://github.com/huggingface/datasets/pull/901 | Addition of Nl2Bash Dataset | @reshinthadithyan we should hold off on this for a couple of weeks till NeurIPS concludes. The [NLC2CMD](http://nlc2cmd.us-east.mybluemix.net/) data will be out then; which includes a cleaner version of this NL2Bash data. The older data is sort of obsolete now. | ## 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
| 39 | text: Addition of Nl2Bash Dataset
## 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
@reshinthadithyan we should hold off on this for a couple of weeks till NeurIPS concludes. The [NLC2CMD](http://nlc2cmd.us-east.mybluemix.net/) data will be out then; which includes a cleaner version of this NL2Bash data. The older data is sort of obsolete now. | [
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https://github.com/huggingface/datasets/pull/898 | Adding SQA dataset | This dataset seems to have around 1000 configs. Therefore when creating the dummy data we end up with hundreds of MB of dummy data which we don't want to add in the repo.
Let's make this PR on hold for now and find a solution after the sprint of next week | As discussed in #880
Seems like automatic dummy-data generation doesn't work if the builder is a `ArrowBasedBuilder`, do you think you could take a look @lhoestq ? | 51 | text: Adding SQA dataset
As discussed in #880
Seems like automatic dummy-data generation doesn't work if the builder is a `ArrowBasedBuilder`, do you think you could take a look @lhoestq ?
This dataset seems to have around 1000 configs. Therefore when creating the dummy data we end up with hundreds of MB of dummy data which we don't want to add in the repo.
Let's make this PR on hold for now and find a solution after the sprint of next week | [
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https://github.com/huggingface/datasets/pull/894 | Allow several tags sets | Closing since we don't need to update the tags of those three datasets (for each one of them there is only one tag set) | 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 | 24 | text: Allow several tags sets
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
Closing since we don't need to update the tags of those three datasets (for each one of them there is only one tag set) | [
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https://github.com/huggingface/datasets/pull/893 | add metrec: arabic poetry dataset | @lhoestq, I want to add other datasets as well. I am not sure if it is possible to do so with the same branch. | 24 | text: add metrec: arabic poetry dataset
@lhoestq, I want to add other datasets as well. I am not sure if it is possible to do so with the same branch. | [
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|
https://github.com/huggingface/datasets/pull/893 | add metrec: arabic poetry dataset | Hi @zaidalyafeai, really excited to get more Arabic coverage in the lib, thanks for your contribution!
Couple of last comments:
- this PR seems to modify some files that are unrelated to your dataset. Could you rebase from master? It should take care of that.
- The dataset card is a good start! Can you describe the task in a few words and add more information in the Data Structure part, including listing and describing the fields? Also, if you don't know how to fill out a paragraph, or if you have some information but think more would be beneficial, please leave `[More Information Needed]` instead of `[N/A]` | 108 | text: add metrec: arabic poetry dataset
Hi @zaidalyafeai, really excited to get more Arabic coverage in the lib, thanks for your contribution!
Couple of last comments:
- this PR seems to modify some files that are unrelated to your dataset. Could you rebase from master? It should take care of that.
- The dataset card is a good start! Can you describe the task in a few words and add more information in the Data Structure part, including listing and describing the fields? Also, if you don't know how to fill out a paragraph, or if you have some information but think more would be beneficial, please leave `[More Information Needed]` instead of `[N/A]` | [
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|
https://github.com/huggingface/datasets/pull/893 | add metrec: arabic poetry dataset | > Hi @zaidalyafeai, really excited to get more Arabic coverage in the lib, thanks for your contribution!
>
> Couple of last comments:
>
> * this PR seems to modify some files that are unrelated to your dataset. Could you rebase from master? It should take care of that.
> * The dataset card is a good start! Can you describe the task in a few words and add more information in the Data Structure part, including listing and describing the fields? Also, if you don't know how to fill out a paragraph, or if you have some information but think more would be beneficial, please leave `[More Information Needed]` instead of `[N/A]`
I have no idea how some other files changed. I tried to rebase and push but this created some errors. I had to run the command
`git push -u --force origin add-metrec-dataset` which might cause some problems. | 151 | text: add metrec: arabic poetry dataset
> Hi @zaidalyafeai, really excited to get more Arabic coverage in the lib, thanks for your contribution!
>
> Couple of last comments:
>
> * this PR seems to modify some files that are unrelated to your dataset. Could you rebase from master? It should take care of that.
> * The dataset card is a good start! Can you describe the task in a few words and add more information in the Data Structure part, including listing and describing the fields? Also, if you don't know how to fill out a paragraph, or if you have some information but think more would be beneficial, please leave `[More Information Needed]` instead of `[N/A]`
I have no idea how some other files changed. I tried to rebase and push but this created some errors. I had to run the command
`git push -u --force origin add-metrec-dataset` which might cause some problems. | [
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|
https://github.com/huggingface/datasets/pull/893 | add metrec: arabic poetry dataset | @yjernite can you explain which other files are changed because of the PR ? https://github.com/huggingface/datasets/pull/893/files only shows files related to the dataset. | 22 | text: add metrec: arabic poetry dataset
@yjernite can you explain which other files are changed because of the PR ? https://github.com/huggingface/datasets/pull/893/files only shows files related to the dataset. | [
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|
https://github.com/huggingface/datasets/pull/893 | add metrec: arabic poetry dataset | @lhoestq thanks for the merge. I am not a GitHub geek. I already have another dataset to add. I'm not sure how to add another given my forked repo. Do I follow the same steps with a different checkout name ? | 41 | text: add metrec: arabic poetry dataset
@lhoestq thanks for the merge. I am not a GitHub geek. I already have another dataset to add. I'm not sure how to add another given my forked repo. Do I follow the same steps with a different checkout name ? | [
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|
https://github.com/huggingface/datasets/pull/893 | add metrec: arabic poetry dataset | If you've followed the instructions in here : https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#start-by-preparing-your-environment
(especially point 2. and the command `git remote add upstream ....`)
Then you can try
```
git checkout master
git fetch upstream
git rebase upstream/master
git checkout -b add-<my-new-dataset-name>
``` | 39 | text: add metrec: arabic poetry dataset
If you've followed the instructions in here : https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#start-by-preparing-your-environment
(especially point 2. and the command `git remote add upstream ....`)
Then you can try
```
git checkout master
git fetch upstream
git rebase upstream/master
git checkout -b add-<my-new-dataset-name>
``` | [
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|
https://github.com/huggingface/datasets/pull/892 | Add a few datasets of reference in the documentation | Looks good to me. Do we also support TSV in this helper (explain if it should be text or CSV) and in the dummy-data creator? | 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. | 25 | text: Add a few datasets of reference in the documentation
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.
Looks good to me. Do we also support TSV in this helper (explain if it should be text or CSV) and in the dummy-data creator? | [
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https://github.com/huggingface/datasets/pull/892 | Add a few datasets of reference in the documentation | snli is basically based on tsv files (but named as .txt) and it is in the list of datasets of reference.
The dummy data creator supports tsv | 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. | 27 | text: Add a few datasets of reference in the documentation
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.
snli is basically based on tsv files (but named as .txt) and it is in the list of datasets of reference.
The dummy data creator supports tsv | [
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https://github.com/huggingface/datasets/pull/892 | Add a few datasets of reference in the documentation | merging this one.
If you think of other datasets of reference to add we can still add them later | 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. | 19 | text: Add a few datasets of reference in the documentation
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.
merging this one.
If you think of other datasets of reference to add we can still add them later | [
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https://github.com/huggingface/datasets/pull/890 | Add LER | Thanks for the comments. I addressed them and pushed again.
When I run "make quality" I get the following error but I don't know how to resolve it or what the problem ist respectively:
would reformat /Users/joelniklaus/NextCloud/PhDJoelNiklaus/Code/datasets/datasets/ler/ler.py
Oh no! 💥 💔 💥
1 file would be reformatted, 257 files would be left unchanged.
make: *** [quality] Error 1
| 58 | text: Add LER
Thanks for the comments. I addressed them and pushed again.
When I run "make quality" I get the following error but I don't know how to resolve it or what the problem ist respectively:
would reformat /Users/joelniklaus/NextCloud/PhDJoelNiklaus/Code/datasets/datasets/ler/ler.py
Oh no! 💥 💔 💥
1 file would be reformatted, 257 files would be left unchanged.
make: *** [quality] Error 1
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|
https://github.com/huggingface/datasets/pull/890 | Add LER | I did that now. But still getting the following error:
black --check --line-length 119 --target-version py36 tests src benchmarks datasets metrics
All done! ✨ 🍰 ✨
258 files would be left unchanged.
isort --check-only tests src benchmarks datasets metrics
flake8 tests src benchmarks datasets metrics
datasets/ler/ler.py:46:96: W291 trailing whitespace
datasets/ler/ler.py:47:68: W291 trailing whitespace
datasets/ler/ler.py:48:102: W291 trailing whitespace
datasets/ler/ler.py:49:112: W291 trailing whitespace
datasets/ler/ler.py:50:92: W291 trailing whitespace
datasets/ler/ler.py:51:116: W291 trailing whitespace
datasets/ler/ler.py:52:84: W291 trailing whitespace
make: *** [quality] Error 1
However: When I look at the file I don't see any trailing whitespace | 91 | text: Add LER
I did that now. But still getting the following error:
black --check --line-length 119 --target-version py36 tests src benchmarks datasets metrics
All done! ✨ 🍰 ✨
258 files would be left unchanged.
isort --check-only tests src benchmarks datasets metrics
flake8 tests src benchmarks datasets metrics
datasets/ler/ler.py:46:96: W291 trailing whitespace
datasets/ler/ler.py:47:68: W291 trailing whitespace
datasets/ler/ler.py:48:102: W291 trailing whitespace
datasets/ler/ler.py:49:112: W291 trailing whitespace
datasets/ler/ler.py:50:92: W291 trailing whitespace
datasets/ler/ler.py:51:116: W291 trailing whitespace
datasets/ler/ler.py:52:84: W291 trailing whitespace
make: *** [quality] Error 1
However: When I look at the file I don't see any trailing whitespace | [
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|
https://github.com/huggingface/datasets/pull/890 | Add LER | maybe a bug with flake8 ? could you try to update it ? which version do you have ? | 19 | text: Add LER
maybe a bug with flake8 ? could you try to update it ? which version do you have ? | [
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|
https://github.com/huggingface/datasets/pull/890 | Add LER | This is my flake8 version: 3.7.9 (mccabe: 0.6.1, pycodestyle: 2.5.0, pyflakes: 2.1.1) CPython 3.8.5 on Darwin
| 16 | text: Add LER
This is my flake8 version: 3.7.9 (mccabe: 0.6.1, pycodestyle: 2.5.0, pyflakes: 2.1.1) CPython 3.8.5 on Darwin
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|
https://github.com/huggingface/datasets/pull/890 | Add LER | Now I updated to: 3.8.4 (mccabe: 0.6.1, pycodestyle: 2.6.0, pyflakes: 2.2.0) CPython 3.8.5 on Darwin
And now I even get additional errors:
black --check --line-length 119 --target-version py36 tests src benchmarks datasets metrics
All done! ✨ 🍰 ✨
258 files would be left unchanged.
isort --check-only tests src benchmarks datasets metrics
flake8 tests src benchmarks datasets metrics
datasets/polyglot_ner/polyglot_ner.py:123:64: F541 f-string is missing placeholders
datasets/ler/ler.py:46:96: W291 trailing whitespace
datasets/ler/ler.py:47:68: W291 trailing whitespace
datasets/ler/ler.py:48:102: W291 trailing whitespace
datasets/ler/ler.py:49:112: W291 trailing whitespace
datasets/ler/ler.py:50:92: W291 trailing whitespace
datasets/ler/ler.py:51:116: W291 trailing whitespace
datasets/ler/ler.py:52:84: W291 trailing whitespace
datasets/math_dataset/math_dataset.py:233:25: E741 ambiguous variable name 'l'
metrics/coval/coval.py:236:31: F541 f-string is missing placeholders
make: *** [quality] Error 1
I do this on macOS Catalina 10.15.7 in case this matters | 119 | text: Add LER
Now I updated to: 3.8.4 (mccabe: 0.6.1, pycodestyle: 2.6.0, pyflakes: 2.2.0) CPython 3.8.5 on Darwin
And now I even get additional errors:
black --check --line-length 119 --target-version py36 tests src benchmarks datasets metrics
All done! ✨ 🍰 ✨
258 files would be left unchanged.
isort --check-only tests src benchmarks datasets metrics
flake8 tests src benchmarks datasets metrics
datasets/polyglot_ner/polyglot_ner.py:123:64: F541 f-string is missing placeholders
datasets/ler/ler.py:46:96: W291 trailing whitespace
datasets/ler/ler.py:47:68: W291 trailing whitespace
datasets/ler/ler.py:48:102: W291 trailing whitespace
datasets/ler/ler.py:49:112: W291 trailing whitespace
datasets/ler/ler.py:50:92: W291 trailing whitespace
datasets/ler/ler.py:51:116: W291 trailing whitespace
datasets/ler/ler.py:52:84: W291 trailing whitespace
datasets/math_dataset/math_dataset.py:233:25: E741 ambiguous variable name 'l'
metrics/coval/coval.py:236:31: F541 f-string is missing placeholders
make: *** [quality] Error 1
I do this on macOS Catalina 10.15.7 in case this matters | [
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|
https://github.com/huggingface/datasets/pull/890 | Add LER | Code quality test now passes, thanks :)
To fix the other tests failing I think you can just rebase from master.
Also make sure that the dummy data test passes with
```python
RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_ler
``` | 36 | text: Add LER
Code quality test now passes, thanks :)
To fix the other tests failing I think you can just rebase from master.
Also make sure that the dummy data test passes with
```python
RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_ler
``` | [
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|
https://github.com/huggingface/datasets/pull/890 | Add LER | Sorry you had to close your PR ! It looks like this week's sprint doesn't always make it easy to see what's being added/what's already added.
Thank you for contributing to the library. You did a great job on adding LER so feel free to add other ones that you would like to see in the library, it will be a pleasure to review | 64 | text: Add LER
Sorry you had to close your PR ! It looks like this week's sprint doesn't always make it easy to see what's being added/what's already added.
Thank you for contributing to the library. You did a great job on adding LER so feel free to add other ones that you would like to see in the library, it will be a pleasure to review | [
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|
https://github.com/huggingface/datasets/pull/889 | Optional per-dataset default config name | I like the idea ! And the approach is right imo
Note that by changing this we will have to add a way for users to get the config lists of a dataset. In the current user workflow, the user could see the list of the config when the missing config error is raised but now it won't be the case because of the default config. | 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. | 66 | text: Optional per-dataset default config name
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.
I like the idea ! And the approach is right imo
Note that by changing this we will have to add a way for users to get the config lists of a dataset. In the current user workflow, the user could see the list of the config when the missing config error is raised but now it won't be the case because of the default config. | [
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https://github.com/huggingface/datasets/pull/889 | Optional per-dataset default config name | @lhoestq Okay great, I added a test as well as two new inspect functions: `get_dataset_config_names` and `get_dataset_infos` (the latter is something I've been wanting anyway). As a quick hack, you can also just pass a random config name (e.g. an empty string) to `load_dataset` to get the config names in the error msg as before. Also added a couple paragraphs to the adding new datasets doc.
I'll send a separate PR incorporating this in existing datasets so we can get this merged before our sprint on Monday.
Any ideas on the failing tests? I'm having trouble making sense of it. **Edit**: nvm, it was master. | 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. | 105 | text: Optional per-dataset default config name
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.
@lhoestq Okay great, I added a test as well as two new inspect functions: `get_dataset_config_names` and `get_dataset_infos` (the latter is something I've been wanting anyway). As a quick hack, you can also just pass a random config name (e.g. an empty string) to `load_dataset` to get the config names in the error msg as before. Also added a couple paragraphs to the adding new datasets doc.
I'll send a separate PR incorporating this in existing datasets so we can get this merged before our sprint on Monday.
Any ideas on the failing tests? I'm having trouble making sense of it. **Edit**: nvm, it was master. | [
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https://github.com/huggingface/datasets/pull/886 | Fix wikipedia custom config | I think this issue is still not resolve yet. Please check my comment in the following issue, thanks.
[#577](https://github.com/huggingface/datasets/issues/577#issuecomment-868122769) | 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 | 19 | text: Fix wikipedia custom config
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
I think this issue is still not resolve yet. Please check my comment in the following issue, thanks.
[#577](https://github.com/huggingface/datasets/issues/577#issuecomment-868122769) | [
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https://github.com/huggingface/datasets/pull/884 | Auto generate dummy data | I took your comments into account.
Also now after compressing the dummy_data.zip file it runs a dummy data test (=make sure each split has at least 1 example using the dummy data) | 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. | 32 | text: Auto generate dummy data
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.
I took your comments into account.
Also now after compressing the dummy_data.zip file it runs a dummy data test (=make sure each split has at least 1 example using the dummy data) | [
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https://github.com/huggingface/datasets/pull/884 | Auto generate dummy data | I just tested the tool with some datasets and found out that it's not working for datasets that download files using `download_and_extract(file_url)` (where file_url is a `str`). That's because in that case the dummy_data.zip is not a folder but a single zipped file.
I think we have to fix that or we can have unexpected behavior when a scripts calls `download_and_extract(file_url)` several times, since it would always point to the same dummy data file.
So I decided to change that to have a folder containing the dummy files instead but it breaks around 90 tests so I need to update 90 dummy data files to follow this scheme. I'll probably fix them tomorrow morning.
What do you guys think ? Also cc @patrickvonplaten to make sure I understand things correctly | 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. | 130 | text: Auto generate dummy data
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.
I just tested the tool with some datasets and found out that it's not working for datasets that download files using `download_and_extract(file_url)` (where file_url is a `str`). That's because in that case the dummy_data.zip is not a folder but a single zipped file.
I think we have to fix that or we can have unexpected behavior when a scripts calls `download_and_extract(file_url)` several times, since it would always point to the same dummy data file.
So I decided to change that to have a folder containing the dummy files instead but it breaks around 90 tests so I need to update 90 dummy data files to follow this scheme. I'll probably fix them tomorrow morning.
What do you guys think ? Also cc @patrickvonplaten to make sure I understand things correctly | [
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https://github.com/huggingface/datasets/pull/884 | Auto generate dummy data | Ok I changed to use the dummy_data.zip content to be a folder even for single url calls to `dl_manager.download_and_extract`. Therefore the automatic dummy data generation tool works for most datasets now.
To avoid having to change all the old dummy_data.zip files I added backward compatiblity.
The only test failing is `tests/test_dataset_common.py::RemoteDatasetTest::test_load_dataset_xcopa`
It is expected to fail since I had modify its dummy data structure that was wrong. It was causing issue with backward compatibility. It will be fixed as soon as this PR is merged | 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. | 85 | text: Auto generate dummy data
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.
Ok I changed to use the dummy_data.zip content to be a folder even for single url calls to `dl_manager.download_and_extract`. Therefore the automatic dummy data generation tool works for most datasets now.
To avoid having to change all the old dummy_data.zip files I added backward compatiblity.
The only test failing is `tests/test_dataset_common.py::RemoteDatasetTest::test_load_dataset_xcopa`
It is expected to fail since I had modify its dummy data structure that was wrong. It was causing issue with backward compatibility. It will be fixed as soon as this PR is merged | [
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https://github.com/huggingface/datasets/pull/868 | Consistent metric outputs | I keep this PR in stand-by for next week's datasets sprint. If the next release is 2.0.0 then we can include it given that it's breaking for many metrics | To automate the use of metrics, they should return consistent outputs.
In particular I'm working on adding a conversion of metrics to keras metrics.
To achieve this we need two things:
- have each metric return dictionaries of string -> floats since each keras metrics should return one float
- define in the metric info the different fields of the output dictionary
In this PR I'm adding these two features.
I also fixed a few bugs in some metrics
#867 needs to be merged first | 29 | text: Consistent metric outputs
To automate the use of metrics, they should return consistent outputs.
In particular I'm working on adding a conversion of metrics to keras metrics.
To achieve this we need two things:
- have each metric return dictionaries of string -> floats since each keras metrics should return one float
- define in the metric info the different fields of the output dictionary
In this PR I'm adding these two features.
I also fixed a few bugs in some metrics
#867 needs to be merged first
I keep this PR in stand-by for next week's datasets sprint. If the next release is 2.0.0 then we can include it given that it's breaking for many metrics | [
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https://github.com/huggingface/datasets/pull/856 | Add open book corpus | @lhoestq I fixed issues except for the dummy_data zip file. But I think I know why is it happening. So when unzipping dummy_data.zip it gets save in /tmp directory where glob doesn't pick it up. For regular downloads, the archive gets unzipped in ~/.cache/huggingface. Could that be a reason? | Adds book corpus based on Shawn Presser's [work](https://github.com/soskek/bookcorpus/issues/27) @richarddwang, the author of the original BookCorpus dataset, suggested it should be named [OpenBookCorpus](https://github.com/huggingface/datasets/issues/486). I named it BookCorpusOpen to be easily located alphabetically. But, of course, we can rename it if needed.
It contains 17868 dataset items; each item contains two fields: title and text. The title is the name of the book (just the file name) while the text contains unprocessed book text. Note that bookcorpus is pre-segmented into a sentence while this bookcorpus is not. This is intentional (see https://github.com/huggingface/datasets/issues/486) as some users might want to further process the text themselves.
@lhoestq and others please review this PR thoroughly. cc @shawwn | 49 | text: Add open book corpus
Adds book corpus based on Shawn Presser's [work](https://github.com/soskek/bookcorpus/issues/27) @richarddwang, the author of the original BookCorpus dataset, suggested it should be named [OpenBookCorpus](https://github.com/huggingface/datasets/issues/486). I named it BookCorpusOpen to be easily located alphabetically. But, of course, we can rename it if needed.
It contains 17868 dataset items; each item contains two fields: title and text. The title is the name of the book (just the file name) while the text contains unprocessed book text. Note that bookcorpus is pre-segmented into a sentence while this bookcorpus is not. This is intentional (see https://github.com/huggingface/datasets/issues/486) as some users might want to further process the text themselves.
@lhoestq and others please review this PR thoroughly. cc @shawwn
@lhoestq I fixed issues except for the dummy_data zip file. But I think I know why is it happening. So when unzipping dummy_data.zip it gets save in /tmp directory where glob doesn't pick it up. For regular downloads, the archive gets unzipped in ~/.cache/huggingface. Could that be a reason? | [
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https://github.com/huggingface/datasets/pull/856 | Add open book corpus | Nice thanks :)
When testing with the dummy data, the `download_manager.download_and_extract()` call returns the path to the unzipped dummy_data.zip archive. Therefore glob should be able to find your dummy .epub.txt file | Adds book corpus based on Shawn Presser's [work](https://github.com/soskek/bookcorpus/issues/27) @richarddwang, the author of the original BookCorpus dataset, suggested it should be named [OpenBookCorpus](https://github.com/huggingface/datasets/issues/486). I named it BookCorpusOpen to be easily located alphabetically. But, of course, we can rename it if needed.
It contains 17868 dataset items; each item contains two fields: title and text. The title is the name of the book (just the file name) while the text contains unprocessed book text. Note that bookcorpus is pre-segmented into a sentence while this bookcorpus is not. This is intentional (see https://github.com/huggingface/datasets/issues/486) as some users might want to further process the text themselves.
@lhoestq and others please review this PR thoroughly. cc @shawwn | 31 | text: Add open book corpus
Adds book corpus based on Shawn Presser's [work](https://github.com/soskek/bookcorpus/issues/27) @richarddwang, the author of the original BookCorpus dataset, suggested it should be named [OpenBookCorpus](https://github.com/huggingface/datasets/issues/486). I named it BookCorpusOpen to be easily located alphabetically. But, of course, we can rename it if needed.
It contains 17868 dataset items; each item contains two fields: title and text. The title is the name of the book (just the file name) while the text contains unprocessed book text. Note that bookcorpus is pre-segmented into a sentence while this bookcorpus is not. This is intentional (see https://github.com/huggingface/datasets/issues/486) as some users might want to further process the text themselves.
@lhoestq and others please review this PR thoroughly. cc @shawwn
Nice thanks :)
When testing with the dummy data, the `download_manager.download_and_extract()` call returns the path to the unzipped dummy_data.zip archive. Therefore glob should be able to find your dummy .epub.txt file | [
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https://github.com/huggingface/datasets/pull/856 | Add open book corpus | @lhoestq I understand but for some reason, it is not happening. I added logs to see where dummy_data.zip gets unzipped in /tmp but I suppose when the test process finishes that tmp is gone. I also tried to glob anything in _generate_examples from that directory using /* instead of **/*.epub.txt and nothing is being returned. Always an empty array. | Adds book corpus based on Shawn Presser's [work](https://github.com/soskek/bookcorpus/issues/27) @richarddwang, the author of the original BookCorpus dataset, suggested it should be named [OpenBookCorpus](https://github.com/huggingface/datasets/issues/486). I named it BookCorpusOpen to be easily located alphabetically. But, of course, we can rename it if needed.
It contains 17868 dataset items; each item contains two fields: title and text. The title is the name of the book (just the file name) while the text contains unprocessed book text. Note that bookcorpus is pre-segmented into a sentence while this bookcorpus is not. This is intentional (see https://github.com/huggingface/datasets/issues/486) as some users might want to further process the text themselves.
@lhoestq and others please review this PR thoroughly. cc @shawwn | 59 | text: Add open book corpus
Adds book corpus based on Shawn Presser's [work](https://github.com/soskek/bookcorpus/issues/27) @richarddwang, the author of the original BookCorpus dataset, suggested it should be named [OpenBookCorpus](https://github.com/huggingface/datasets/issues/486). I named it BookCorpusOpen to be easily located alphabetically. But, of course, we can rename it if needed.
It contains 17868 dataset items; each item contains two fields: title and text. The title is the name of the book (just the file name) while the text contains unprocessed book text. Note that bookcorpus is pre-segmented into a sentence while this bookcorpus is not. This is intentional (see https://github.com/huggingface/datasets/issues/486) as some users might want to further process the text themselves.
@lhoestq and others please review this PR thoroughly. cc @shawwn
@lhoestq I understand but for some reason, it is not happening. I added logs to see where dummy_data.zip gets unzipped in /tmp but I suppose when the test process finishes that tmp is gone. I also tried to glob anything in _generate_examples from that directory using /* instead of **/*.epub.txt and nothing is being returned. Always an empty array. | [
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https://github.com/huggingface/datasets/pull/856 | Add open book corpus | In _generate_examples_ I wrote the following:
```
glob_target = os.path.join(directory, "**/*.epub.txt")
print(f"Glob target {glob_target }")
```
And here is the test failure:
========================================================================================== FAILURES ===========================================================================================
________________________________________________________________ LocalDatasetTest.test_load_dataset_all_configs_bookcorpusopen ________________________________________________________________
self = <tests.test_dataset_common.LocalDatasetTest testMethod=test_load_dataset_all_configs_bookcorpusopen>, dataset_name = 'bookcorpusopen'
@slow
def test_load_dataset_all_configs(self, dataset_name):
configs = self.dataset_tester.load_all_configs(dataset_name, is_local=True)
> self.dataset_tester.check_load_dataset(dataset_name, configs, is_local=True)
tests/test_dataset_common.py:232:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/test_dataset_common.py:193: in check_load_dataset
self.parent.assertTrue(len(dataset[split]) > 0)
E AssertionError: False is not true
------------------------------------------------------------------------------------ Captured stdout call -------------------------------------------------------------------------------------
Downloading and preparing dataset book_corpus_open/plain_text (download: 1.00 MiB, generated: 1.00 MiB, post-processed: Unknown size, total: 2.00 MiB) to /var/folders/y_/6k6zhblx0k9dsdz5nd_z9x5c0000gp/T/tmpmuu0_ln2/book_corpus_open/plain_text/1.0.0...
Glob target /var/folders/y_/6k6zhblx0k9dsdz5nd_z9x5c0000gp/T/tmpm6tpvb3f/extracted/d953b414cceb4fe3985eeaf68aec2f4435f166b2edf66863d805e3825b7d336b/dummy_data/**/*.epub.txt
Dataset book_corpus_open downloaded and prepared to /var/folders/y_/6k6zhblx0k9dsdz5nd_z9x5c0000gp/T/tmpmuu0_ln2/book_corpus_open/plain_text/1.0.0. Subsequent calls will reuse this data.
------------------------------------------------------------------------------------ Captured stderr call -------------------------------------------------------------------------------------
| Adds book corpus based on Shawn Presser's [work](https://github.com/soskek/bookcorpus/issues/27) @richarddwang, the author of the original BookCorpus dataset, suggested it should be named [OpenBookCorpus](https://github.com/huggingface/datasets/issues/486). I named it BookCorpusOpen to be easily located alphabetically. But, of course, we can rename it if needed.
It contains 17868 dataset items; each item contains two fields: title and text. The title is the name of the book (just the file name) while the text contains unprocessed book text. Note that bookcorpus is pre-segmented into a sentence while this bookcorpus is not. This is intentional (see https://github.com/huggingface/datasets/issues/486) as some users might want to further process the text themselves.
@lhoestq and others please review this PR thoroughly. cc @shawwn | 201 | text: Add open book corpus
Adds book corpus based on Shawn Presser's [work](https://github.com/soskek/bookcorpus/issues/27) @richarddwang, the author of the original BookCorpus dataset, suggested it should be named [OpenBookCorpus](https://github.com/huggingface/datasets/issues/486). I named it BookCorpusOpen to be easily located alphabetically. But, of course, we can rename it if needed.
It contains 17868 dataset items; each item contains two fields: title and text. The title is the name of the book (just the file name) while the text contains unprocessed book text. Note that bookcorpus is pre-segmented into a sentence while this bookcorpus is not. This is intentional (see https://github.com/huggingface/datasets/issues/486) as some users might want to further process the text themselves.
@lhoestq and others please review this PR thoroughly. cc @shawwn
In _generate_examples_ I wrote the following:
```
glob_target = os.path.join(directory, "**/*.epub.txt")
print(f"Glob target {glob_target }")
```
And here is the test failure:
========================================================================================== FAILURES ===========================================================================================
________________________________________________________________ LocalDatasetTest.test_load_dataset_all_configs_bookcorpusopen ________________________________________________________________
self = <tests.test_dataset_common.LocalDatasetTest testMethod=test_load_dataset_all_configs_bookcorpusopen>, dataset_name = 'bookcorpusopen'
@slow
def test_load_dataset_all_configs(self, dataset_name):
configs = self.dataset_tester.load_all_configs(dataset_name, is_local=True)
> self.dataset_tester.check_load_dataset(dataset_name, configs, is_local=True)
tests/test_dataset_common.py:232:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
tests/test_dataset_common.py:193: in check_load_dataset
self.parent.assertTrue(len(dataset[split]) > 0)
E AssertionError: False is not true
------------------------------------------------------------------------------------ Captured stdout call -------------------------------------------------------------------------------------
Downloading and preparing dataset book_corpus_open/plain_text (download: 1.00 MiB, generated: 1.00 MiB, post-processed: Unknown size, total: 2.00 MiB) to /var/folders/y_/6k6zhblx0k9dsdz5nd_z9x5c0000gp/T/tmpmuu0_ln2/book_corpus_open/plain_text/1.0.0...
Glob target /var/folders/y_/6k6zhblx0k9dsdz5nd_z9x5c0000gp/T/tmpm6tpvb3f/extracted/d953b414cceb4fe3985eeaf68aec2f4435f166b2edf66863d805e3825b7d336b/dummy_data/**/*.epub.txt
Dataset book_corpus_open downloaded and prepared to /var/folders/y_/6k6zhblx0k9dsdz5nd_z9x5c0000gp/T/tmpmuu0_ln2/book_corpus_open/plain_text/1.0.0. Subsequent calls will reuse this data.
------------------------------------------------------------------------------------ Captured stderr call -------------------------------------------------------------------------------------
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https://github.com/huggingface/datasets/pull/856 | Add open book corpus | And when I do os.listdir on the given directory I get:
glob_target = os.path.join(directory, "**/*.epub.txt")
print(f"Glob target {glob_target }")
> print(os.listdir(path=directory))
E FileNotFoundError: [Errno 2] No such file or directory: '/var/folders/y_/6k6zhblx0k9dsdz5nd_z9x5c0000gp/T/tmpbu_aom5q/extracted/d953b414cceb4fe3985eeaf68aec2f4435f166b2edf66863d805e3825b7d336b/dummy_data'
| Adds book corpus based on Shawn Presser's [work](https://github.com/soskek/bookcorpus/issues/27) @richarddwang, the author of the original BookCorpus dataset, suggested it should be named [OpenBookCorpus](https://github.com/huggingface/datasets/issues/486). I named it BookCorpusOpen to be easily located alphabetically. But, of course, we can rename it if needed.
It contains 17868 dataset items; each item contains two fields: title and text. The title is the name of the book (just the file name) while the text contains unprocessed book text. Note that bookcorpus is pre-segmented into a sentence while this bookcorpus is not. This is intentional (see https://github.com/huggingface/datasets/issues/486) as some users might want to further process the text themselves.
@lhoestq and others please review this PR thoroughly. cc @shawwn | 31 | text: Add open book corpus
Adds book corpus based on Shawn Presser's [work](https://github.com/soskek/bookcorpus/issues/27) @richarddwang, the author of the original BookCorpus dataset, suggested it should be named [OpenBookCorpus](https://github.com/huggingface/datasets/issues/486). I named it BookCorpusOpen to be easily located alphabetically. But, of course, we can rename it if needed.
It contains 17868 dataset items; each item contains two fields: title and text. The title is the name of the book (just the file name) while the text contains unprocessed book text. Note that bookcorpus is pre-segmented into a sentence while this bookcorpus is not. This is intentional (see https://github.com/huggingface/datasets/issues/486) as some users might want to further process the text themselves.
@lhoestq and others please review this PR thoroughly. cc @shawwn
And when I do os.listdir on the given directory I get:
glob_target = os.path.join(directory, "**/*.epub.txt")
print(f"Glob target {glob_target }")
> print(os.listdir(path=directory))
E FileNotFoundError: [Errno 2] No such file or directory: '/var/folders/y_/6k6zhblx0k9dsdz5nd_z9x5c0000gp/T/tmpbu_aom5q/extracted/d953b414cceb4fe3985eeaf68aec2f4435f166b2edf66863d805e3825b7d336b/dummy_data'
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https://github.com/huggingface/datasets/pull/856 | Add open book corpus | Ok found the issue !
The dummy_data.zip file must be an archive of a folder named dummy_data. Currently the dummy_data.zip is an archive of a folder named book1. In order to have a valid dummy_data.zip file you must first take the dummy book1 folder, place it inside a folder named dummy_data and then compress the dummy_data folder to get dummy_data.zip | Adds book corpus based on Shawn Presser's [work](https://github.com/soskek/bookcorpus/issues/27) @richarddwang, the author of the original BookCorpus dataset, suggested it should be named [OpenBookCorpus](https://github.com/huggingface/datasets/issues/486). I named it BookCorpusOpen to be easily located alphabetically. But, of course, we can rename it if needed.
It contains 17868 dataset items; each item contains two fields: title and text. The title is the name of the book (just the file name) while the text contains unprocessed book text. Note that bookcorpus is pre-segmented into a sentence while this bookcorpus is not. This is intentional (see https://github.com/huggingface/datasets/issues/486) as some users might want to further process the text themselves.
@lhoestq and others please review this PR thoroughly. cc @shawwn | 60 | text: Add open book corpus
Adds book corpus based on Shawn Presser's [work](https://github.com/soskek/bookcorpus/issues/27) @richarddwang, the author of the original BookCorpus dataset, suggested it should be named [OpenBookCorpus](https://github.com/huggingface/datasets/issues/486). I named it BookCorpusOpen to be easily located alphabetically. But, of course, we can rename it if needed.
It contains 17868 dataset items; each item contains two fields: title and text. The title is the name of the book (just the file name) while the text contains unprocessed book text. Note that bookcorpus is pre-segmented into a sentence while this bookcorpus is not. This is intentional (see https://github.com/huggingface/datasets/issues/486) as some users might want to further process the text themselves.
@lhoestq and others please review this PR thoroughly. cc @shawwn
Ok found the issue !
The dummy_data.zip file must be an archive of a folder named dummy_data. Currently the dummy_data.zip is an archive of a folder named book1. In order to have a valid dummy_data.zip file you must first take the dummy book1 folder, place it inside a folder named dummy_data and then compress the dummy_data folder to get dummy_data.zip | [
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https://github.com/huggingface/datasets/pull/856 | Add open book corpus | > Awesome thank you so much for adding it :)
You're welcome, ok all tests are green now! I needed it asap as well. Thanks for your help @lhoestq . | Adds book corpus based on Shawn Presser's [work](https://github.com/soskek/bookcorpus/issues/27) @richarddwang, the author of the original BookCorpus dataset, suggested it should be named [OpenBookCorpus](https://github.com/huggingface/datasets/issues/486). I named it BookCorpusOpen to be easily located alphabetically. But, of course, we can rename it if needed.
It contains 17868 dataset items; each item contains two fields: title and text. The title is the name of the book (just the file name) while the text contains unprocessed book text. Note that bookcorpus is pre-segmented into a sentence while this bookcorpus is not. This is intentional (see https://github.com/huggingface/datasets/issues/486) as some users might want to further process the text themselves.
@lhoestq and others please review this PR thoroughly. cc @shawwn | 30 | text: Add open book corpus
Adds book corpus based on Shawn Presser's [work](https://github.com/soskek/bookcorpus/issues/27) @richarddwang, the author of the original BookCorpus dataset, suggested it should be named [OpenBookCorpus](https://github.com/huggingface/datasets/issues/486). I named it BookCorpusOpen to be easily located alphabetically. But, of course, we can rename it if needed.
It contains 17868 dataset items; each item contains two fields: title and text. The title is the name of the book (just the file name) while the text contains unprocessed book text. Note that bookcorpus is pre-segmented into a sentence while this bookcorpus is not. This is intentional (see https://github.com/huggingface/datasets/issues/486) as some users might want to further process the text themselves.
@lhoestq and others please review this PR thoroughly. cc @shawwn
> Awesome thank you so much for adding it :)
You're welcome, ok all tests are green now! I needed it asap as well. Thanks for your help @lhoestq . | [
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https://github.com/huggingface/datasets/pull/856 | Add open book corpus | I just wanted to say thank you to everyone involved in making this happen! I was certain that I would have to add bookcorpusnew myself, but then @vblagoje came along and did it, and @lhoestq gave some great support in a timely fashion.
By the way @vblagoje, are you on Twitter? I'm https://twitter.com/theshawwn if you'd like to DM and say hello. Once again, thanks for doing this!
I'll mention over at https://github.com/soskek/bookcorpus/issues/27 that this was merged. | Adds book corpus based on Shawn Presser's [work](https://github.com/soskek/bookcorpus/issues/27) @richarddwang, the author of the original BookCorpus dataset, suggested it should be named [OpenBookCorpus](https://github.com/huggingface/datasets/issues/486). I named it BookCorpusOpen to be easily located alphabetically. But, of course, we can rename it if needed.
It contains 17868 dataset items; each item contains two fields: title and text. The title is the name of the book (just the file name) while the text contains unprocessed book text. Note that bookcorpus is pre-segmented into a sentence while this bookcorpus is not. This is intentional (see https://github.com/huggingface/datasets/issues/486) as some users might want to further process the text themselves.
@lhoestq and others please review this PR thoroughly. cc @shawwn | 76 | text: Add open book corpus
Adds book corpus based on Shawn Presser's [work](https://github.com/soskek/bookcorpus/issues/27) @richarddwang, the author of the original BookCorpus dataset, suggested it should be named [OpenBookCorpus](https://github.com/huggingface/datasets/issues/486). I named it BookCorpusOpen to be easily located alphabetically. But, of course, we can rename it if needed.
It contains 17868 dataset items; each item contains two fields: title and text. The title is the name of the book (just the file name) while the text contains unprocessed book text. Note that bookcorpus is pre-segmented into a sentence while this bookcorpus is not. This is intentional (see https://github.com/huggingface/datasets/issues/486) as some users might want to further process the text themselves.
@lhoestq and others please review this PR thoroughly. cc @shawwn
I just wanted to say thank you to everyone involved in making this happen! I was certain that I would have to add bookcorpusnew myself, but then @vblagoje came along and did it, and @lhoestq gave some great support in a timely fashion.
By the way @vblagoje, are you on Twitter? I'm https://twitter.com/theshawwn if you'd like to DM and say hello. Once again, thanks for doing this!
I'll mention over at https://github.com/soskek/bookcorpus/issues/27 that this was merged. | [
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https://github.com/huggingface/datasets/pull/856 | Add open book corpus | @vblagoje Would you be interested in adding books3 as well? https://twitter.com/theshawwn/status/1320282149329784833
Huggingface is interested and asked me to add it, but I had a bit of trouble during setup (https://github.com/huggingface/datasets/issues/790) and never got around to it. At this point you have much more experience than I do with the datasets lib.
It *seems* like it might simply be a matter of copy-pasting this PR, changing books1 to books3, and possibly trimming off the leading paths -- each book is at e.g. the-eye/Books/Bibliotok/J/Jurassic Park.epub.txt, which is rather lengthy compared to just the filename -- but the full path is probably fine, so feel free to do the least amount of work that gets the job done. Otherwise I suppose I'll get around to it eventually; thanks again! | Adds book corpus based on Shawn Presser's [work](https://github.com/soskek/bookcorpus/issues/27) @richarddwang, the author of the original BookCorpus dataset, suggested it should be named [OpenBookCorpus](https://github.com/huggingface/datasets/issues/486). I named it BookCorpusOpen to be easily located alphabetically. But, of course, we can rename it if needed.
It contains 17868 dataset items; each item contains two fields: title and text. The title is the name of the book (just the file name) while the text contains unprocessed book text. Note that bookcorpus is pre-segmented into a sentence while this bookcorpus is not. This is intentional (see https://github.com/huggingface/datasets/issues/486) as some users might want to further process the text themselves.
@lhoestq and others please review this PR thoroughly. cc @shawwn | 126 | text: Add open book corpus
Adds book corpus based on Shawn Presser's [work](https://github.com/soskek/bookcorpus/issues/27) @richarddwang, the author of the original BookCorpus dataset, suggested it should be named [OpenBookCorpus](https://github.com/huggingface/datasets/issues/486). I named it BookCorpusOpen to be easily located alphabetically. But, of course, we can rename it if needed.
It contains 17868 dataset items; each item contains two fields: title and text. The title is the name of the book (just the file name) while the text contains unprocessed book text. Note that bookcorpus is pre-segmented into a sentence while this bookcorpus is not. This is intentional (see https://github.com/huggingface/datasets/issues/486) as some users might want to further process the text themselves.
@lhoestq and others please review this PR thoroughly. cc @shawwn
@vblagoje Would you be interested in adding books3 as well? https://twitter.com/theshawwn/status/1320282149329784833
Huggingface is interested and asked me to add it, but I had a bit of trouble during setup (https://github.com/huggingface/datasets/issues/790) and never got around to it. At this point you have much more experience than I do with the datasets lib.
It *seems* like it might simply be a matter of copy-pasting this PR, changing books1 to books3, and possibly trimming off the leading paths -- each book is at e.g. the-eye/Books/Bibliotok/J/Jurassic Park.epub.txt, which is rather lengthy compared to just the filename -- but the full path is probably fine, so feel free to do the least amount of work that gets the job done. Otherwise I suppose I'll get around to it eventually; thanks again! | [
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https://github.com/huggingface/datasets/pull/856 | Add open book corpus | @shawwn I'll take a look as soon as I clear my work queue. TBH, I would likely work on making sure HF datasets has all the datasets used to train https://github.com/alexa/bort/ and these are: Wikipedia, Wiktionary, OpenWebText (Gokaslan and Cohen, 2019), UrbanDictionary, Onel Billion Words (Chelba et al., 2014), the news subset of Common Crawl (Nagel, 2016)10, and Bookcorpus. cc @lhoestq | Adds book corpus based on Shawn Presser's [work](https://github.com/soskek/bookcorpus/issues/27) @richarddwang, the author of the original BookCorpus dataset, suggested it should be named [OpenBookCorpus](https://github.com/huggingface/datasets/issues/486). I named it BookCorpusOpen to be easily located alphabetically. But, of course, we can rename it if needed.
It contains 17868 dataset items; each item contains two fields: title and text. The title is the name of the book (just the file name) while the text contains unprocessed book text. Note that bookcorpus is pre-segmented into a sentence while this bookcorpus is not. This is intentional (see https://github.com/huggingface/datasets/issues/486) as some users might want to further process the text themselves.
@lhoestq and others please review this PR thoroughly. cc @shawwn | 61 | text: Add open book corpus
Adds book corpus based on Shawn Presser's [work](https://github.com/soskek/bookcorpus/issues/27) @richarddwang, the author of the original BookCorpus dataset, suggested it should be named [OpenBookCorpus](https://github.com/huggingface/datasets/issues/486). I named it BookCorpusOpen to be easily located alphabetically. But, of course, we can rename it if needed.
It contains 17868 dataset items; each item contains two fields: title and text. The title is the name of the book (just the file name) while the text contains unprocessed book text. Note that bookcorpus is pre-segmented into a sentence while this bookcorpus is not. This is intentional (see https://github.com/huggingface/datasets/issues/486) as some users might want to further process the text themselves.
@lhoestq and others please review this PR thoroughly. cc @shawwn
@shawwn I'll take a look as soon as I clear my work queue. TBH, I would likely work on making sure HF datasets has all the datasets used to train https://github.com/alexa/bort/ and these are: Wikipedia, Wiktionary, OpenWebText (Gokaslan and Cohen, 2019), UrbanDictionary, Onel Billion Words (Chelba et al., 2014), the news subset of Common Crawl (Nagel, 2016)10, and Bookcorpus. cc @lhoestq | [
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https://github.com/huggingface/datasets/pull/808 | dataset(dgs): initial dataset loading script | I did not.
With all the limitations this repo currently has, I had to create a repo of my own using tfds to mitigate them.
https://github.com/sign-language-processing/datasets/tree/master/sign_language_datasets/datasets/dgs_corpus
Closing as I don't know how to support this PR further | When trying to create dummy data I get:
> Dataset datasets with config None seems to already open files in the method `_split_generators(...)`. You might consider to instead only open files in the method `_generate_examples(...)` instead. If this is not possible the dummy data has t o be created with less guidance. Make sure you create the file dummy_data.
I am not sure how to manually create the dummy_data (what exactly it should contain)
Also note, this library says:
> ImportError: To be able to use this dataset, you need to install the following dependencies['pympi'] using 'pip install pympi' for instance'
When you actually need to `pip install pympi-ling`
| 37 | text: dataset(dgs): initial dataset loading script
When trying to create dummy data I get:
> Dataset datasets with config None seems to already open files in the method `_split_generators(...)`. You might consider to instead only open files in the method `_generate_examples(...)` instead. If this is not possible the dummy data has t o be created with less guidance. Make sure you create the file dummy_data.
I am not sure how to manually create the dummy_data (what exactly it should contain)
Also note, this library says:
> ImportError: To be able to use this dataset, you need to install the following dependencies['pympi'] using 'pip install pympi' for instance'
When you actually need to `pip install pympi-ling`
I did not.
With all the limitations this repo currently has, I had to create a repo of my own using tfds to mitigate them.
https://github.com/sign-language-processing/datasets/tree/master/sign_language_datasets/datasets/dgs_corpus
Closing as I don't know how to support this PR further | [
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] |
https://github.com/huggingface/datasets/pull/802 | Add XGlue | Really cool to add XGlue, this will be a nice addition !
Splits shouldn't depend on the language. There must be configurations for each language, as we're doing for xnli, xtreme, etc.
For example for XGlue we'll have these configurations: NER.de, NER.en etc. | Dataset is ready to merge. An important feature of this dataset is that for each config the train data is in English, while dev and test data are in multiple languages. Therefore, @lhoestq and I decided offline that we will give the dataset the following API, *e.g.* for
```python
load_dataset("xglue", "ner") # would give the splits 'train', 'validation.en', 'test.en', 'validation.es', 'test.es', ...
```
=> therefore one can load a single language test via
```python
load_dataset("xglue", "ner", split="test.es")
``` | 43 | text: Add XGlue
Dataset is ready to merge. An important feature of this dataset is that for each config the train data is in English, while dev and test data are in multiple languages. Therefore, @lhoestq and I decided offline that we will give the dataset the following API, *e.g.* for
```python
load_dataset("xglue", "ner") # would give the splits 'train', 'validation.en', 'test.en', 'validation.es', 'test.es', ...
```
=> therefore one can load a single language test via
```python
load_dataset("xglue", "ner", split="test.es")
```
Really cool to add XGlue, this will be a nice addition !
Splits shouldn't depend on the language. There must be configurations for each language, as we're doing for xnli, xtreme, etc.
For example for XGlue we'll have these configurations: NER.de, NER.en etc. | [
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https://github.com/huggingface/datasets/pull/791 | add amazon reviews | @patrickvonplaten Yeah this is adapted from tfds so a lot is just how they wrote the code. Addressed your comments and also simplified the weird `AmazonUSReviewsConfig` definition. Will merge once tests pass. | Adds the Amazon US Reviews dataset as requested in #353. Converted from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/amazon_us_reviews). cc @clmnt @sshleifer | 32 | text: add amazon reviews
Adds the Amazon US Reviews dataset as requested in #353. Converted from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/amazon_us_reviews). cc @clmnt @sshleifer
@patrickvonplaten Yeah this is adapted from tfds so a lot is just how they wrote the code. Addressed your comments and also simplified the weird `AmazonUSReviewsConfig` definition. Will merge once tests pass. | [
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https://github.com/huggingface/datasets/pull/791 | add amazon reviews | Thanks for checking this one :)
Looks good to me
Just one question : is there a particular reason to use `names=["Y", "N"]` in this order ? Usually the positive label is at index 1 and the negative one at index 0 for binary classification | Adds the Amazon US Reviews dataset as requested in #353. Converted from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/amazon_us_reviews). cc @clmnt @sshleifer | 45 | text: add amazon reviews
Adds the Amazon US Reviews dataset as requested in #353. Converted from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/amazon_us_reviews). cc @clmnt @sshleifer
Thanks for checking this one :)
Looks good to me
Just one question : is there a particular reason to use `names=["Y", "N"]` in this order ? Usually the positive label is at index 1 and the negative one at index 0 for binary classification | [
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https://github.com/huggingface/datasets/pull/791 | add amazon reviews | > is there a particular reason to use `names=["Y", "N"]` in this order ? Usually the positive label is at index 1 and the negative one at index 0 for binary classification
Hmm that's a good point. I'll submit a quick fix.
| Adds the Amazon US Reviews dataset as requested in #353. Converted from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/amazon_us_reviews). cc @clmnt @sshleifer | 42 | text: add amazon reviews
Adds the Amazon US Reviews dataset as requested in #353. Converted from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/amazon_us_reviews). cc @clmnt @sshleifer
> is there a particular reason to use `names=["Y", "N"]` in this order ? Usually the positive label is at index 1 and the negative one at index 0 for binary classification
Hmm that's a good point. I'll submit a quick fix.
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https://github.com/huggingface/datasets/pull/789 | dataset(ncslgr): add initial loading script | Hi @AmitMY, sorry for leaving you hanging for a minute :)
We've developed a new pipeline for adding datasets with a few extra steps, including adding a dataset card. You can find the full process [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md)
Would you be up for adding the tags and description in the README.md so we can merge this cool dataset? | Its a small dataset, but its heavily annotated
https://www.bu.edu/asllrp/ncslgr.html
| 56 | text: dataset(ncslgr): add initial loading script
Its a small dataset, but its heavily annotated
https://www.bu.edu/asllrp/ncslgr.html
Hi @AmitMY, sorry for leaving you hanging for a minute :)
We've developed a new pipeline for adding datasets with a few extra steps, including adding a dataset card. You can find the full process [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md)
Would you be up for adding the tags and description in the README.md so we can merge this cool dataset? | [
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https://github.com/huggingface/datasets/pull/789 | dataset(ncslgr): add initial loading script | Awesome thank you !
It looks like the PR now includes changes from other PR that were previously merged.
Feel free to create another branch and another PR so that we can have a clean diff.
| Its a small dataset, but its heavily annotated
https://www.bu.edu/asllrp/ncslgr.html
| 36 | text: dataset(ncslgr): add initial loading script
Its a small dataset, but its heavily annotated
https://www.bu.edu/asllrp/ncslgr.html
Awesome thank you !
It looks like the PR now includes changes from other PR that were previously merged.
Feel free to create another branch and another PR so that we can have a clean diff.
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https://github.com/huggingface/datasets/pull/787 | Adding nli_tr dataset | Thank you @lhoestq for the time you take to review our pull request. We appreciate your help.
We've made the changes you described. Hope that it is ready for being merged. Please let me know if you have any additional requests for revisions. | Hello,
In this pull request, we have implemented the necessary interface to add our recent dataset [NLI-TR](https://github.com/boun-tabi/NLI-TR). The datasets will be presented on a full paper at EMNLP 2020 this month. [[arXiv link] ](https://arxiv.org/pdf/2004.14963.pdf)
The dataset is the neural machine translation of SNLI and MultiNLI datasets into Turkish. So, we followed a similar format with the original datasets hosted in the HuggingFace datasets hub.
Our dataset is designed to be accessed as follows by following the interface of the GLUE dataset that provides multiple datasets in a single interface over the HuggingFace datasets hub.
```
from datasets import load_dataset
multinli_tr = load_dataset("nli_tr", "multinli_tr")
snli_tr = load_dataset("nli_tr", "snli_tr")
```
Thanks for your help in reviewing our pull request. | 43 | text: Adding nli_tr dataset
Hello,
In this pull request, we have implemented the necessary interface to add our recent dataset [NLI-TR](https://github.com/boun-tabi/NLI-TR). The datasets will be presented on a full paper at EMNLP 2020 this month. [[arXiv link] ](https://arxiv.org/pdf/2004.14963.pdf)
The dataset is the neural machine translation of SNLI and MultiNLI datasets into Turkish. So, we followed a similar format with the original datasets hosted in the HuggingFace datasets hub.
Our dataset is designed to be accessed as follows by following the interface of the GLUE dataset that provides multiple datasets in a single interface over the HuggingFace datasets hub.
```
from datasets import load_dataset
multinli_tr = load_dataset("nli_tr", "multinli_tr")
snli_tr = load_dataset("nli_tr", "snli_tr")
```
Thanks for your help in reviewing our pull request.
Thank you @lhoestq for the time you take to review our pull request. We appreciate your help.
We've made the changes you described. Hope that it is ready for being merged. Please let me know if you have any additional requests for revisions. | [
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] |
https://github.com/huggingface/datasets/pull/785 | feat(aslg_pc12): add dev and test data splits | Hi ! I'm not sure we should make this split decision arbitrarily on our side. Users can split it afterwards to whatever they want using `dataset.train_test_split` for example.
Moreover it looks like there's already papers that use this dataset and propose their own splits ([here](http://xanthippi.ceid.upatras.gr/HealthSign/resources/Publications/sitis_paper_25_10.pdf) 80-20)
What do you think ? | For reproducibility sake, it's best if there are defined dev and test splits.
The original paper author did not define splits for the entire dataset, not for the sample loaded via this library, so I decided to define:
- 5/7th for train
- 1/7th for dev
- 1/7th for test
| 51 | text: feat(aslg_pc12): add dev and test data splits
For reproducibility sake, it's best if there are defined dev and test splits.
The original paper author did not define splits for the entire dataset, not for the sample loaded via this library, so I decided to define:
- 5/7th for train
- 1/7th for dev
- 1/7th for test
Hi ! I'm not sure we should make this split decision arbitrarily on our side. Users can split it afterwards to whatever they want using `dataset.train_test_split` for example.
Moreover it looks like there's already papers that use this dataset and propose their own splits ([here](http://xanthippi.ceid.upatras.gr/HealthSign/resources/Publications/sitis_paper_25_10.pdf) 80-20)
What do you think ? | [
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