| --- |
| license: mit |
| language: |
| - en |
| --- |
| |
| # BERT-Tiny (uncased) |
| This is the smallest version of 24 smaller BERT models (English only, uncased, trained with WordPiece masking) |
| released by [google-research/bert](https://github.com/google-research/bert). |
|
|
| These BERT models was released as TensorFlow checkpoints, however, this is the converted version to PyTorch. |
| More information can be found in [google-research/bert](https://github.com/google-research/bert) or [lyeoni/convert-tf-to-pytorch](https://github.com/lyeoni/convert-tf-to-pytorch). |
|
|
| ## Evaluation |
| Here are the evaluation scores (F1/Accuracy) for the MPRC task. |
| |Model|MRPC| |
| |-|:-:| |
| |BERT-Tiny|81.22/68.38| |
| |BERT-Mini|81.43/69.36| |
| |BERT-Small|81.41/70.34| |
| |BERT-Medium|83.33/73.53| |
| |BERT-Base|85.62/78.19| |
|
|
| ### References |
| ``` |
| @article{turc2019, |
| title={Well-Read Students Learn Better: On the Importance of Pre-training Compact Models}, |
| author={Turc, Iulia and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina}, |
| journal={arXiv preprint arXiv:1908.08962v2 }, |
| year={2019} |
| } |
| ``` |
