Back to all datasets
Dataset: glue 🏷
Update glue.py on GitHub

How to load this dataset directly with the πŸ€—/datasets library:

				
Copy to clipboard
from datasets import load_dataset dataset = load_dataset("glue")

Tags  

None yet.

You can create or edit a tag set using our tagging app.

Models trained or fine-tuned on glue

None yet. Start fine-tuning now =)



Dataset Card for "glue"

Table of Contents

Dataset Description

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

More Information Needed

Languages

More Information Needed

Dataset Structure

We show detailed information for up to 5 configurations of the dataset.

Data Instances

ax

  • Size of downloaded dataset files: 0.21 MB
  • Size of the generated dataset: 0.23 MB
  • Total amount of disk used: 0.44 MB

An example of 'test' looks as follows.

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.

mnli

  • Size of downloaded dataset files: 298.29 MB
  • Size of the generated dataset: 78.65 MB
  • Total amount of disk used: 376.95 MB

An example of 'train' looks as follows.

mnli_matched

  • Size of downloaded dataset files: 298.29 MB
  • Size of the generated dataset: 3.52 MB
  • Total amount of disk used: 301.82 MB

An example of 'validation' looks as follows.

mnli_mismatched

  • Size of downloaded dataset files: 298.29 MB
  • Size of the generated dataset: 3.73 MB
  • Total amount of disk used: 302.02 MB

An example of 'validation' looks as follows.

Data Fields

The data fields are the same among all splits.

ax

  • premise: a string feature.
  • hypothesis: a string feature.
  • label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
  • idx: a int32 feature.

cola

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

mnli

  • premise: a string feature.
  • hypothesis: a string feature.
  • label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
  • idx: a int32 feature.

mnli_matched

  • premise: a string feature.
  • hypothesis: a string feature.
  • label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
  • idx: a int32 feature.

mnli_mismatched

  • premise: a string feature.
  • hypothesis: a string feature.
  • label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
  • idx: a int32 feature.

Data Splits Sample Size

ax

test
ax 1104

cola

train validation test
cola 8551 1043 1063

mnli

train validation_matched validation_mismatched test_matched test_mismatched
mnli 392702 9815 9832 9796 9847

mnli_matched

validation test
mnli_matched 9815 9796

mnli_mismatched

validation test
mnli_mismatched 9832 9847

Dataset Creation

Curation Rationale

More Information Needed

Source Data

More Information Needed

Annotations

More Information Needed

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

More Information Needed

Licensing Information

More Information Needed

Citation Information

@article{warstadt2018neural,
  title={Neural Network Acceptability Judgments},
  author={Warstadt, Alex and Singh, Amanpreet and Bowman, Samuel R},
  journal={arXiv preprint arXiv:1805.12471},
  year={2018}
}
@inproceedings{wang2019glue,
  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
  note={In the Proceedings of ICLR.},
  year={2019}
}

Note that each GLUE dataset has its own citation. Please see the source to see
the correct citation for each contained dataset.