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https://api-inference.huggingface.co/models/microsoft/MiniLM-L12-H384-uncased
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microsoft/MiniLM-L12-H384-uncased microsoft/MiniLM-L12-H384-uncased
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pytorch

tf

Contributed by

Microsoft company
12 team members · 22 models

How to use this model directly from the 🤗/transformers library:

			
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from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("microsoft/MiniLM-L12-H384-uncased") model = AutoModel.from_pretrained("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.

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}
}