pytorch tf jax bert en transformers
.gitattributes README.md config.json flax_model.msgpack pytorch_model.bin special_tokens_map.json tf_model.h5 tokenizer_config.json vocab.txt
---
language: en
---

# ERNIE-tiny

## Introduction
ERNIE-tiny is a compressed model from [ERNIE 2.0](../ernie-2.0-en) base model through model structure compression and model distillation.
Through compression, the performance of the ERNIE-tiny only decreases by an average of 2.37% compared to ERNIE 2.0 base, 
but it outperforms Google BERT by 8.35%, and the speed increases by 4.3 times.

More details: https://github.com/PaddlePaddle/ERNIE/blob/develop/distill/README.md

## Released Model Info

|Model Name|Language|Model Structure|
|:---:|:---:|:---:|
|ernie-tiny| English |Layer:3, Hidden:1024, Heads:16|

This released pytorch model is converted from the officially released PaddlePaddle ERNIE model and 
a series of experiments have been conducted to check the accuracy of the conversion.

- Official PaddlePaddle ERNIE repo: https://github.com/PaddlePaddle/ERNIE
- Pytorch Conversion repo:  https://github.com/nghuyong/ERNIE-Pytorch

## How to use
```Python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("nghuyong/ernie-tiny")
model = AutoModel.from_pretrained("nghuyong/ernie-tiny")
```

## Citation

```bibtex
@article{sun2019ernie20,
  title={ERNIE 2.0: A Continual Pre-training Framework for Language Understanding},
  author={Sun, Yu and Wang, Shuohuan and Li, Yukun and Feng, Shikun and Tian, Hao and Wu, Hua and Wang, Haifeng},
  journal={arXiv preprint arXiv:1907.12412},
  year={2019} 
}
```
