---
title: ernie-3.0-medium-zh
canonical_url: "https://www.modelscope.cn/models/thomas/ernie-3.0-medium-zh"
md_url: "https://www.modelscope.cn/models/thomas/ernie-3.0-medium-zh.md"
repository: thomas/ernie-3.0-medium-zh
last_updated: 2023-06-05
license: "Apache License 2.0"
pipeline_tag: sentence-embedding
tasks:
  - sentence-embedding
model_type:
  - ernie
architectures:
  - ErnieForMaskedLM
library_name:
  - transformer
  - pytorch
frameworks:
  - Pytorch
downloads: 905
stars: 7
---

# ernie-3.0-medium-zh

> ernie-3.0-medium-zh - thomas 在 ModelScope 开源的模型。ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation More detail: https://arxiv.org/abs/2107.02137

thomas/ernie-3.0-medium-zh 是 ModelScope 魔搭社区上的sentence-embedding模型，采用 Apache License 2.0 许可。

- **Repository**: thomas/ernie-3.0-medium-zh
- **License**: Apache License 2.0
- **Tasks**: sentence-embedding
- **Downloads**: 905
- **Stars**: 7
- **Last updated**: 2023-06-05

Source: https://www.modelscope.cn/models/thomas/ernie-3.0-medium-zh

---

# ERNIE-3.0-medium-zh

## Introduction

ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation
More detail: https://arxiv.org/abs/2107.02137

## Released Model Info

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://paddlenlp.readthedocs.io/zh/latest/model_zoo/transformers/ERNIE/contents.html
- Pytorch Conversion repo:  https://github.com/nghuyong/ERNIE-Pytorch

## How to use
```Python
from transformers import BertTokenizer, ErnieModel

tokenizer = BertTokenizer.from_pretrained("nghuyong/ernie-3.0-medium-zh")
model = ErnieModel.from_pretrained("nghuyong/ernie-3.0-medium-zh")
```

## Citation

```bibtex
@article{sun2021ernie,
  title={Ernie 3.0: Large-scale knowledge enhanced pre-training for language understanding and generation},
  author={Sun, Yu and Wang, Shuohuan and Feng, Shikun and Ding, Siyu and Pang, Chao and Shang, Junyuan and Liu, Jiaxiang and Chen, Xuyi and Zhao, Yanbin and Lu, Yuxiang and others},
  journal={arXiv preprint arXiv:2107.02137},
  year={2021}
}
```