microsoft/MiniLM-L12-H384-uncased

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.

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}
}