
# BERT-Mini (uncased)
This is one 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}
}
```
{"language": ["en"], "license": "mit"}
transformers pytorch bert feature-extraction en license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #feature-extraction #en #license-mit #endpoints_compatible #region-us 

BERT-Mini (uncased)
===================


This is one of 24 smaller BERT models (English only, uncased, trained with WordPiece masking)
released by 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 or lyeoni/convert-tf-to-pytorch.


Evaluation
----------


Here are the evaluation scores (F1/Accuracy) for the MPRC task.



### References



### References
TAGS
#transformers #pytorch #bert #feature-extraction #en #license-mit #endpoints_compatible #region-us 
 ### References
TAGS
#transformers #pytorch #bert #feature-extraction #en #license-mit #endpoints_compatible #region-us 
### References