Datasets:

Languages: en
Multilinguality: monolingual
Size Categories: 10K<n<100K
Licenses: cc-by-4-0
Language Creators: unknown
Annotations Creators: unknown
Source Datasets: unknown
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Dataset Card for GLUE

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: a string feature.
  • label: a classification label, with possible values including unacceptable (0), acceptable (1).
  • idx: a int32 feature.

Data Splits

train validation test
8551 1043 1063

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

More Information Needed

Annotations

Annotation process

More Information Needed

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

More Information Needed

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

More Information Needed

Other Known Limitations

More Information Needed

Additional Information

Dataset Curators

Rare Disease Curators from the National Institutes of Health (NIH) Genetic and Rare Diseases Information Center (GARD)

Licensing Information

More Information Needed

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")