
`bert-base-uncased` fine-tuned on SST-2 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/sst2/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}
}
```

{"language": "en", "license": "apache-2.0", "tags": ["bert", "sst2", "glue", "kd", "torchdistill"], "datasets": ["sst2"], "metrics": ["accuracy"]}
transformers pytorch bert text-classification sst2 glue kd torchdistill en dataset:sst2 arxiv:2310.17644 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #sst2 #glue #kd #torchdistill #en #dataset-sst2 #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


'bert-base-uncased' fine-tuned on SST-2 dataset, using fine-tuned 'bert-large-uncased' as a teacher model, *torchdistill* and Google Colab for knowledge distillation.  
The training configuration (including hyperparameters) is available here.  
I submitted prediction files to the GLUE 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] [Preprint]  


TAGS
#transformers #pytorch #bert #text-classification #sst2 #glue #kd #torchdistill #en #dataset-sst2 #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #bert #text-classification #sst2 #glue #kd #torchdistill #en #dataset-sst2 #arxiv-2310.17644 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
