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--- |
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thumbnail: https://huggingface.co/front/thumbnails/microsoft.png |
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tags: |
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- text-classification |
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license: mit |
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--- |
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## MiniLM: Small and Fast Pre-trained Models for Language Understanding and Generation |
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MiniLM is a distilled model from the paper "[MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://arxiv.org/abs/2002.10957)". |
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Please find the information about preprocessing, training and full details of the MiniLM in the [original MiniLM repository](https://github.com/microsoft/unilm/blob/master/minilm/). |
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Please note: This checkpoint can be an inplace substitution for BERT and it needs to be fine-tuned before use! |
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### English Pre-trained Models |
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We release the **uncased** **12**-layer model with **384** hidden size distilled from an in-house pre-trained [UniLM v2](/unilm) model in BERT-Base size. |
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- MiniLMv1-L12-H384-uncased: 12-layer, 384-hidden, 12-heads, 33M parameters, 2.7x faster than BERT-Base |
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#### Fine-tuning on NLU tasks |
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We present the dev results on SQuAD 2.0 and several GLUE benchmark tasks. |
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| Model | #Param | SQuAD 2.0 | MNLI-m | SST-2 | QNLI | CoLA | RTE | MRPC | QQP | |
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|---------------------------------------------------|--------|-----------|--------|-------|------|------|------|------|------| |
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| [BERT-Base](https://arxiv.org/pdf/1810.04805.pdf) | 109M | 76.8 | 84.5 | 93.2 | 91.7 | 58.9 | 68.6 | 87.3 | 91.3 | |
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| **MiniLM-L12xH384** | 33M | 81.7 | 85.7 | 93.0 | 91.5 | 58.5 | 73.3 | 89.5 | 91.3 | |
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### Citation |
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If you find MiniLM useful in your research, please cite the following paper: |
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``` latex |
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@misc{wang2020minilm, |
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title={MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers}, |
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author={Wenhui Wang and Furu Wei and Li Dong and Hangbo Bao and Nan Yang and Ming Zhou}, |
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year={2020}, |
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eprint={2002.10957}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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