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```
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from datasets import load_dataset
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epiclassify = load_dataset("ncats/EpiSet4BinaryClassification")
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---
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annotations_creators:
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- unknown
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language_creators:
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- unknown
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languages:
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- en
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licenses:
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- cc-by-4-0
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multilinguality:
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- monolingual
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size_categories:
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- 10K<n<100K
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source_datasets:
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- unknown
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paperswithcode_id: glue
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pretty_name: GLUE (General Language Understanding Evaluation benchmark)
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---
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# DOCUMENTATION UPDATES IN PROGRESS! IGNORE BELOW
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# Dataset Card for GLUE
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## Table of Contents
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- [Dataset Description](#dataset-description)
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- [Dataset Summary](#dataset-summary)
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
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- [Languages](#languages)
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- [Dataset Structure](#dataset-structure)
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- [Data Instances](#data-instances)
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- [Data Fields](#data-fields)
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- [Data Splits](#data-splits)
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- [Dataset Creation](#dataset-creation)
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- [Curation Rationale](#curation-rationale)
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- [Source Data](#source-data)
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- [Annotations](#annotations)
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- [Personal and Sensitive Information](#personal-and-sensitive-information)
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- [Considerations for Using the Data](#considerations-for-using-the-data)
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- [Social Impact of Dataset](#social-impact-of-dataset)
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- [Discussion of Biases](#discussion-of-biases)
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- [Other Known Limitations](#other-known-limitations)
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- [Additional Information](#additional-information)
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- [Dataset Curators](#dataset-curators)
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- [Licensing Information](#licensing-information)
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- [Citation Information](#citation-information)
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- [Contributions](#contributions)
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## Dataset Description
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- **Homepage:** [https://nyu-mll.github.io/CoLA/](https://nyu-mll.github.io/CoLA/)
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- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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- **Size of downloaded dataset files:** 955.33 MB
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- **Size of the generated dataset:** 229.68 MB
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- **Total amount of disk used:** 1185.01 MB
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### Dataset Summary
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GLUE, the General Language Understanding Evaluation benchmark (https://gluebenchmark.com/) is a collection of resources for training, evaluating, and analyzing natural language understanding systems.
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### Supported Tasks and Leaderboards
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The leaderboard for the GLUE benchmark can be found [at this address](https://gluebenchmark.com/). It comprises the following tasks:
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#### cola
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The Corpus of Linguistic Acceptability consists of English acceptability judgments drawn from books and journal articles on linguistic theory. Each example is a sequence of words annotated with whether it is a grammatical English sentence.
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### Languages
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The language data in is in English
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## Dataset Structure
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### Data Instances
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#### cola
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- **Size of downloaded dataset files:** 0.36 MB
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- **Size of the generated dataset:** 0.58 MB
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- **Total amount of disk used:** 0.94 MB
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An example of 'train' looks as follows.
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```
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{
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"sentence": "Our friends won't buy this analysis, let alone the next one we propose.",
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"label": 1,
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"id": 0
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}
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```
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### Data Fields
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The data fields are the same among all splits.
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#### cola
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- `abstract`: a `string` feature.
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- `label`: a classification label, with possible values including `unacceptable` (0), `acceptable` (1).
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- `idx`: a `int32` feature.
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### Data Splits
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|train|validation|test|
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|----:|---------:|---:|
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| 8551| 1043|1063|
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## Dataset Creation
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### Curation Rationale
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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### Source Data
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#### Initial Data Collection and Normalization
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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#### Who are the source language producers?
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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### Annotations
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#### Annotation process
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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#### Who are the annotators?
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Rare Disease Curators from the [National Institutes of Health (NIH) Genetic and Rare Diseases Information Center (GARD)](https://rarediseases.info.nih.gov/)
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### Personal and Sensitive Information
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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## Considerations for Using the Data
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### Social Impact of Dataset
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+
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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### Discussion of Biases
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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+
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### Other Known Limitations
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+
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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## Additional Information
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### Dataset Curators
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Rare Disease Curators from the [National Institutes of Health (NIH) Genetic and Rare Diseases Information Center (GARD)](https://rarediseases.info.nih.gov/)
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### Licensing Information
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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### Citation Information
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```
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@inproceedings{john2021recurrent,
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title={Recurrent Neural Networks to Automatically Identify Rare Disease Epidemiologic Studies from PubMed},
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author={John, Jennifer N and Sid, Eric and Zhu, Qian},
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booktitle={AMIA Annual Symposium Proceedings},
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volume={2021},
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pages={325},
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year={2021},
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organization={American Medical Informatics Association}
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}
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```
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### Contributions
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Thanks to [@patpizio](https://github.com/patpizio), [@jeswan](https://github.com/jeswan), [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset.
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```
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from datasets import load_dataset
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epiclassify = load_dataset("ncats/EpiSet4BinaryClassification")
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