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--- |
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language: en |
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tags: |
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- bert |
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- wnli |
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- glue |
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- torchdistill |
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license: apache-2.0 |
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datasets: |
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- wnli |
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metrics: |
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- accuracy |
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--- |
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`bert-base-uncased` fine-tuned on WNLI dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_finetuning_and_submission.ipynb). |
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The hyperparameters are the same as those in Hugging Face's example and/or the paper of BERT, and the training configuration (including hyperparameters) is available [here](https://github.com/yoshitomo-matsubara/torchdistill/blob/main/configs/sample/glue/wnli/ce/bert_base_uncased.yaml). |
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I submitted prediction files to [the GLUE leaderboard](https://gluebenchmark.com/leaderboard), and the overall GLUE score was **77.9**. |
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Yoshitomo Matsubara: **"torchdistill Meets Hugging Face Libraries for Reproducible, Coding-Free Deep Learning Studies: A Case Study on NLP"** at *EMNLP 2023 Workshop for Natural Language Processing Open Source Software (NLP-OSS)* |
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[[OpenReview](https://openreview.net/forum?id=A5Axeeu1Bo)] [[Preprint](https://arxiv.org/abs/2310.17644)] |
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```bibtex |
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@article{matsubara2023torchdistill, |
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title={{torchdistill Meets Hugging Face Libraries for Reproducible, Coding-Free Deep Learning Studies: A Case Study on NLP}}, |
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author={Matsubara, Yoshitomo}, |
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journal={arXiv preprint arXiv:2310.17644}, |
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year={2023} |
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} |
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``` |
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