torchdistill: Reproducing fine-tuned BERT models
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GLUE leaderboard: https://gluebenchmark.com/leaderboard/
Code: https://github.com/yoshitomo-matsubara/torchdistill?tab=readme-ov-file#glue • 18 items • Updated
bert-large-uncased fine-tuned on RTE dataset, using torchdistill and Google Colab.
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.
I submitted prediction files to the GLUE leaderboard, and the overall GLUE score was 80.2.
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)
[Paper] [OpenReview] [Preprint]
@inproceedings{matsubara2023torchdistill,
title={{torchdistill Meets Hugging Face Libraries for Reproducible, Coding-Free Deep Learning Studies: A Case Study on NLP}},
author={Matsubara, Yoshitomo},
booktitle={Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023)},
publisher={Empirical Methods in Natural Language Processing},
pages={153--164},
year={2023}
}
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