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--- |
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language: en |
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tags: |
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- bert |
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- mnli |
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- ax |
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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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- mnli |
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- ax |
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metrics: |
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- accuracy |
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--- |
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`bert-large-uncased` fine-tuned on MNLI 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/mnli/ce/bert_large_uncased.yaml). |
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I submitted prediction files to [the GLUE leaderboard](https://gluebenchmark.com/leaderboard), and the overall GLUE score was **80.2**. |
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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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[[Paper](https://aclanthology.org/2023.nlposs-1.18/)] [[OpenReview](https://openreview.net/forum?id=A5Axeeu1Bo)] [[Preprint](https://arxiv.org/abs/2310.17644)] |
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```bibtex |
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@inproceedings{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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booktitle={Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023)}, |
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publisher={Empirical Methods in Natural Language Processing}, |
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pages={153--164}, |
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year={2023} |
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} |
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``` |
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