annotations_creators:
- unknown
language_creators:
- unknown
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- unknown
paperswithcode_id: glue
pretty_name: GLUE (General Language Understanding Evaluation benchmark)
DOCUMENTATION UPDATES IN PROGRESS! IGNORE BELOW
Dataset Card for GLUE
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://nyu-mll.github.io/CoLA/
- Repository: More Information Needed
- Paper: More Information Needed
- Point of Contact: More Information Needed
- Size of downloaded dataset files: 955.33 MB
- Size of the generated dataset: 229.68 MB
- Total amount of disk used: 1185.01 MB
Dataset Summary
GLUE, the General Language Understanding Evaluation benchmark (https://gluebenchmark.com/) is a collection of resources for training, evaluating, and analyzing natural language understanding systems.
Supported Tasks and Leaderboards
The leaderboard for the GLUE benchmark can be found at this address. It comprises the following tasks:
cola
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.
Languages
The language data in is in English
Dataset Structure
Data Instances
cola
- Size of downloaded dataset files: 0.36 MB
- Size of the generated dataset: 0.58 MB
- Total amount of disk used: 0.94 MB
An example of 'train' looks as follows.
{
"sentence": "Our friends won't buy this analysis, let alone the next one we propose.",
"label": 1,
"id": 0
}
Data Fields
The data fields are the same among all splits.
cola
abstract: astringfeature.label: a classification label, with possible values includingunacceptable(0),acceptable(1).idx: aint32feature.
Data Splits
| train | validation | test |
|---|---|---|
| 8551 | 1043 | 1063 |
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Rare Disease Curators from the National Institutes of Health (NIH) Genetic and Rare Diseases Information Center (GARD)
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Rare Disease Curators from the National Institutes of Health (NIH) Genetic and Rare Diseases Information Center (GARD)
Licensing Information
Citation Information
@inproceedings{john2021recurrent,
title={Recurrent Neural Networks to Automatically Identify Rare Disease Epidemiologic Studies from PubMed},
author={John, Jennifer N and Sid, Eric and Zhu, Qian},
booktitle={AMIA Annual Symposium Proceedings},
volume={2021},
pages={325},
year={2021},
organization={American Medical Informatics Association}
}
Contributions
Thanks to @patpizio, @jeswan, @thomwolf, @patrickvonplaten, @mariamabarham for adding this dataset.
from datasets import load_dataset
epiclassify = load_dataset("ncats/EpiSet4BinaryClassification")