pytorch bert text-classification en dataset:rte transformers rte glue kd torchdistill license:apache-2.0
.gitattributes README.md config.json pytorch_model.bin special_tokens_map.json tokenizer.json tokenizer_config.json training.log vocab.txt
---
language: en
tags:
- bert
- rte
- glue
- kd
- torchdistill
license: apache-2.0
datasets:
- rte
metrics:
- accuracy
---

`bert-base-uncased` fine-tuned on RTE 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/rte/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**.
