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Update glue.py on GitHub
How to load this dataset directly with the
🤗/datasets library:
from datasets import load_dataset dataset = load_dataset("glue")
None yet. Start fine-tuning now =)
Update README.md on GitHub
GLUE, the General Language Understanding Evaluation benchmark (https://gluebenchmark.com/) is a collection of resources for training, evaluating, and analyzing natural language understanding systems.
We show detailed information for up to 5 configurations of the dataset.
An example of 'test' looks as follows.
An example of 'train' looks as follows.
An example of 'train' looks as follows.
An example of 'validation' looks as follows.
An example of 'validation' looks as follows.
The data fields are the same among all splits.
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.sentence: a string feature.label: a classification label, with possible values including unacceptable (0), acceptable (1).idx: a int32 feature.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.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.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.| test | |
|---|---|
| ax | 1104 |
| train | validation | test | |
|---|---|---|---|
| cola | 8551 | 1043 | 1063 |
| train | validation_matched | validation_mismatched | test_matched | test_mismatched | |
|---|---|---|---|---|---|
| mnli | 392702 | 9815 | 9832 | 9796 | 9847 |
| validation | test | |
|---|---|---|
| mnli_matched | 9815 | 9796 |
| validation | test | |
|---|---|---|
| mnli_mismatched | 9832 | 9847 |
@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.