---
language: 
  - zh
license: apache-2.0
widget:
- text: "生活的真谛是[MASK]。"
---
# Erlangshen-1.3B model (Chinese)，one model of https://github.com/IDEA-CCNL/Fengshenbang-LM.
Encoder structure-based Bidirection language model, focusing on solving various natural language understanding tasks. The 1.3 billion parameter Erlangshen-1.3B large model, using 280G Chinese data, 32 A100 training for 14 days, is the largest open source Chinese Bert large model. On November 10, 2021, **it reached the top of the FewCLUE** list of the authoritative benchmark for Chinese language understanding(https://mp.weixin.qq.com/s/bA_9n_TlBE9P-UzCn7mKoA). 

Among them, **CHID (Idiom Fill in the Blank) and TNEWS (News Classification) surpass human beings, CHID (Idiom Fill in the Blank), CSLDCP (Subject Document Classification), OCNLI (Natural Language Reasoning) single task first, refreshing few-shot learning records**. The Erlangshen series will continue to be optimized in terms of model scale, knowledge integration, and supervision task assistance.

## Usage
```python
from transformers import BertTokenizer, BertModel,
tokenizer = BertTokenizer.from_pretrained("Langboat/mengzi-bert-base")
model = BertModel.from_pretrained("Langboat/mengzi-bert-base")

from transformers import MegatronBertConfig, MegatronBertModel
from transformers import BertTokenizer

model_pretrained_weight_path = '/home/'  #模型的权重路径
tokenizer = BertTokenizer.from_pretrained("IDEA-CCNL/Erlangshen-1.3B")
config = MegatronBertConfig.from_pretrained("IDEA-CCNL/Erlangshen-1.3B")
model = MegatronBertModel.from_pretrained("IDEA-CCNL/Erlangshen-1.3B")

```
## Scores on downstream chinese tasks (without any data augmentation)
|    Model   | afqmc    |  tnews  | iflytek    |  ocnli  |  cmnli  | wsc  | csl  |
| :--------:    | :-----:  | :----:  | :-----:   | :----: | :----: | :----: | :----: |
| roberta-wwm-ext-large | 0.7514      |   0.5872    | 0.6152      |   0.777    | 0.814    | 0.8914    | 0.86    |
| Erlangshen-1.3B | 0.7608      |   0.5996    | 0.6234      |   0.7917    | 0.81    | 0.9243    | 0.872    |

## Citation
If you find the technical report or resource is useful, please cite the following website in your paper.
```
https://github.com/IDEA-CCNL/Fengshenbang-LM
```