| --- |
| language: en |
| tags: |
| - bert |
| - wnli |
| - glue |
| - kd |
| - torchdistill |
| license: apache-2.0 |
| datasets: |
| - wnli |
| metrics: |
| - accuracy |
| --- |
| |
| `bert-base-uncased` fine-tuned on WNLI dataset, using fine-tuned `bert-large-uncased` as a teacher model, [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_kd_and_submission.ipynb) for knowledge distillation. |
| The training configuration (including hyperparameters) is available [here](https://github.com/yoshitomo-matsubara/torchdistill/blob/main/configs/sample/glue/wnli/kd/bert_base_uncased_from_bert_large_uncased.yaml). |
| I submitted prediction files to [the GLUE leaderboard](https://gluebenchmark.com/leaderboard), and the overall GLUE score was **78.9**. |
|
|
| 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)* |
|
|
| [[OpenReview](https://openreview.net/forum?id=A5Axeeu1Bo)] [[Preprint](https://arxiv.org/abs/2310.17644)] |
| ```bibtex |
| @article{matsubara2023torchdistill, |
| title={{torchdistill Meets Hugging Face Libraries for Reproducible, Coding-Free Deep Learning Studies: A Case Study on NLP}}, |
| author={Matsubara, Yoshitomo}, |
| journal={arXiv preprint arXiv:2310.17644}, |
| year={2023} |
| } |
| ``` |
|
|
