MiniLM L12 H384 uncased EU AI Act Compliance Profile

MiniLM: Small and Fast Pre-trained Models for Language Understanding and Generation

MiniLM is a distilled model from the paper "MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers".

Please find the information about preprocessing, training and full details of the MiniLM in the original MiniLM repository.

Please note: This checkpoint can be an inplace substitution for BERT and it needs to be fine-tuned before use!

English Pre-trained Models

We release the uncased 12-layer model with 384 hidden size distilled from an in-house pre-trained UniLM v2 model in BERT-Base size.

• MiniLMv1-L12-H384-uncased: 12-layer, 384-hidden, 12-heads, 33M parameters, 2.7x faster than BERT-Base

Fine-tuning on NLU tasks

We present the dev results on SQuAD 2.0 and several GLUE benchmark tasks.

| Model | #Param | SQuAD 2.0 | MNLI-m | SST-2 | QNLI | CoLA | RTE | MRPC | QQP | |---------------------------------------------------|--------|-----------|--------|-------|------|------|------|------|------| | BERT-Base | 109M | 76.8 | 84.5 | 93.2 | 91.7 | 58.9 | 68.6 | 87.3 | 91.3 | | MiniLM-L12xH384 | 33M | 81.7 | 85.7 | 93.0 | 91.5 | 58.5 | 73.3 | 89.5 | 91.3 |

Citation

If you find MiniLM useful in your research, please cite the following paper:

@misc{wang2020minilm,
    title={MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers},
    author={Wenhui Wang and Furu Wei and Li Dong and Hangbo Bao and Nan Yang and Ming Zhou},
    year={2020},
    eprint={2002.10957},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

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