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huawei-noah/TinyBERT_General_4L_312D
last 30 days

pytorch

tf

#### Contributed by

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


from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("huawei-noah/TinyBERT_General_4L_312D")

model = AutoModel.from_pretrained("huawei-noah/TinyBERT_General_4L_312D")


Update on GitHub

# TinyBERT: Distilling BERT for Natural Language Understanding

TinyBERT is 7.5x smaller and 9.4x faster on inference than BERT-base and achieves competitive performances in the tasks of natural language understanding. It performs a novel transformer distillation at both the pre-training and task-specific learning stages. In general distillation, we use the original BERT-base without fine-tuning as the teacher and a large-scale text corpus as the learning data. By performing the Transformer distillation on the text from general domain, we obtain a general TinyBERT which provides a good initialization for the task-specific distillation. We here provide the general TinyBERT for your tasks at hand.

For more details about the techniques of TinyBERT, refer to our paper: TinyBERT: Distilling BERT for Natural Language Understanding

# Citation

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

@article{jiao2019tinybert,
title={Tinybert: Distilling bert for natural language understanding},
author={Jiao, Xiaoqi and Yin, Yichun and Shang, Lifeng and Jiang, Xin and Chen, Xiao and Li, Linlin and Wang, Fang and Liu, Qun},
journal={arXiv preprint arXiv:1909.10351},
year={2019}
}