
`bert-large-uncased` fine-tuned on RTE 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).  
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/rte/ce/bert_large_uncased.yaml).  
I submitted prediction files to [the GLUE leaderboard](https://gluebenchmark.com/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)*

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


'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)*

[OpenReview] [Preprint]  


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

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