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| --- | |
| thumbnail: https://huggingface.co/front/thumbnails/google.png | |
| license: apache-2.0 | |
| --- | |
| BERT Miniatures | |
| === | |
| This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with WordPiece masking). | |
| We have shown that the standard BERT recipe (including model architecture and training objective) is effective on a wide range of model sizes, beyond BERT-Base and BERT-Large. The smaller BERT models are intended for environments with restricted computational resources. They can be fine-tuned in the same manner as the original BERT models. However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher. | |
| Our goal is to enable research in institutions with fewer computational resources and encourage the community to seek directions of innovation alternative to increasing model capacity. | |
| You can download the 24 BERT miniatures either from the [official BERT Github page](https://github.com/google-research/bert/), or via HuggingFace from the links below: | |
| | |H=128|H=256|H=512|H=768| | |
| |---|:---:|:---:|:---:|:---:| | |
| | **L=2** |[**2/128 (BERT-Tiny)**][2_128]|[2/256][2_256]|[2/512][2_512]|[2/768][2_768]| | |
| | **L=4** |[4/128][4_128]|[**4/256 (BERT-Mini)**][4_256]|[**4/512 (BERT-Small)**][4_512]|[4/768][4_768]| | |
| | **L=6** |[6/128][6_128]|[6/256][6_256]|[6/512][6_512]|[6/768][6_768]| | |
| | **L=8** |[8/128][8_128]|[8/256][8_256]|[**8/512 (BERT-Medium)**][8_512]|[8/768][8_768]| | |
| | **L=10** |[10/128][10_128]|[10/256][10_256]|[10/512][10_512]|[10/768][10_768]| | |
| | **L=12** |[12/128][12_128]|[12/256][12_256]|[12/512][12_512]|[**12/768 (BERT-Base)**][12_768]| | |
| Note that the BERT-Base model in this release is included for completeness only; it was re-trained under the same regime as the original model. | |
| Here are the corresponding GLUE scores on the test set: | |
| |Model|Score|CoLA|SST-2|MRPC|STS-B|QQP|MNLI-m|MNLI-mm|QNLI(v2)|RTE|WNLI|AX| | |
| |---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | |
| |BERT-Tiny|64.2|0.0|83.2|81.1/71.1|74.3/73.6|62.2/83.4|70.2|70.3|81.5|57.2|62.3|21.0| | |
| |BERT-Mini|65.8|0.0|85.9|81.1/71.8|75.4/73.3|66.4/86.2|74.8|74.3|84.1|57.9|62.3|26.1| | |
| |BERT-Small|71.2|27.8|89.7|83.4/76.2|78.8/77.0|68.1/87.0|77.6|77.0|86.4|61.8|62.3|28.6| | |
| |BERT-Medium|73.5|38.0|89.6|86.6/81.6|80.4/78.4|69.6/87.9|80.0|79.1|87.7|62.2|62.3|30.5| | |
| For each task, we selected the best fine-tuning hyperparameters from the lists below, and trained for 4 epochs: | |
| - batch sizes: 8, 16, 32, 64, 128 | |
| - learning rates: 3e-4, 1e-4, 5e-5, 3e-5 | |
| If you use these models, please cite the following paper: | |
| ``` | |
| @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} | |
| } | |
| ``` | |
| [2_128]: https://huggingface.co/google/bert_uncased_L-2_H-128_A-2 | |
| [2_256]: https://huggingface.co/google/bert_uncased_L-2_H-256_A-4 | |
| [2_512]: https://huggingface.co/google/bert_uncased_L-2_H-512_A-8 | |
| [2_768]: https://huggingface.co/google/bert_uncased_L-2_H-768_A-12 | |
| [4_128]: https://huggingface.co/google/bert_uncased_L-4_H-128_A-2 | |
| [4_256]: https://huggingface.co/google/bert_uncased_L-4_H-256_A-4 | |
| [4_512]: https://huggingface.co/google/bert_uncased_L-4_H-512_A-8 | |
| [4_768]: https://huggingface.co/google/bert_uncased_L-4_H-768_A-12 | |
| [6_128]: https://huggingface.co/google/bert_uncased_L-6_H-128_A-2 | |
| [6_256]: https://huggingface.co/google/bert_uncased_L-6_H-256_A-4 | |
| [6_512]: https://huggingface.co/google/bert_uncased_L-6_H-512_A-8 | |
| [6_768]: https://huggingface.co/google/bert_uncased_L-6_H-768_A-12 | |
| [8_128]: https://huggingface.co/google/bert_uncased_L-8_H-128_A-2 | |
| [8_256]: https://huggingface.co/google/bert_uncased_L-8_H-256_A-4 | |
| [8_512]: https://huggingface.co/google/bert_uncased_L-8_H-512_A-8 | |
| [8_768]: https://huggingface.co/google/bert_uncased_L-8_H-768_A-12 | |
| [10_128]: https://huggingface.co/google/bert_uncased_L-10_H-128_A-2 | |
| [10_256]: https://huggingface.co/google/bert_uncased_L-10_H-256_A-4 | |
| [10_512]: https://huggingface.co/google/bert_uncased_L-10_H-512_A-8 | |
| [10_768]: https://huggingface.co/google/bert_uncased_L-10_H-768_A-12 | |
| [12_128]: https://huggingface.co/google/bert_uncased_L-12_H-128_A-2 | |
| [12_256]: https://huggingface.co/google/bert_uncased_L-12_H-256_A-4 | |
| [12_512]: https://huggingface.co/google/bert_uncased_L-12_H-512_A-8 | |
| [12_768]: https://huggingface.co/google/bert_uncased_L-12_H-768_A-12 | |