pytorch bert text-classification zh transformers NLU Sentiment Chinese license:apache-2.0
---
language: 
  - zh
license: apache-2.0

tags:
- bert
- NLU
- Sentiment
- Chinese

inference: true

widget:
- text: "今天心情不好"

---
# Erlangshen-Roberta-110M-Semtiment, model (Chinese)，one model of [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM).
We collect 8  sentiment datasets in the Chinese domain for finetune, with a total of 227347 samples. Our model is mainly based on [roberta](https://huggingface.co/hfl/chinese-roberta-wwm-ext)

## Usage
```python
from transformers import BertForSequenceClassification
from transformers import BertTokenizer
import torch

tokenizer=BertTokenizer.from_pretrained('IDEA-CCNL/Erlangshen-Roberta-110M-Sentiment')
model=BertForSequenceClassification.from_pretrained('IDEA-CCNL/Erlangshen-Roberta-110M-Sentiment')

text='今天心情不好'

output=model(torch.tensor([tokenizer.encode(text)]))
print(torch.nn.functional.softmax(output.logits,dim=-1))

```
## Scores on downstream chinese tasks
|    Model   | ASAP-SENT    |  ASAP-ASPECT  | ChnSentiCorp    |
| :--------:    | :-----:  | :----:  | :-----:   | 
| Erlangshen-Roberta-110M-Sentiment | 97.77     |   97.31    | 96.61     |
| Erlangshen-Roberta-330M-Sentiment | 97.9      |   97.51    | 96.66      |  
| Erlangshen-MegatronBert-1.3B-Sentiment | 98.1     |   97.8    | 97      |
## Citation
If you find the resource is useful, please cite the following website in your paper.
```
@misc{Fengshenbang-LM,
  title={Fengshenbang-LM},
  author={IDEA-CCNL},
  year={2021},
  howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}
```