pytorch gpt2 text-generation zh transformers generate license:apache-2.0
---
language: 
  - zh

inference: 
  parameters:
    temperature: 0.7
    top_p: 0.6
    repetition_penalty: 1.1
    max_new_tokens: 128
    num_return_sequences: 3
    do_sample: true

license: apache-2.0
tags:
- generate
- gpt2

widget:
- 北京是中国的
- 西湖的景色

---
# Wenzhong-GPT2-110M model (chinese)，one model of [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM).
Wenzhong-GPT2-110M is one of the Wenzhong series, which has smaller parameters. Wenzhong-GPT2-110M Is the base version of gpt2。

## Usage

### load model

```python 
from transformers import GPT2Tokenizer,GPT2LMHeadModel
hf_model_path = 'IDEA-CCNL/Wenzhong-GPT2-110M'
tokenizer = GPT2Tokenizer.from_pretrained(hf_model_path)
model = GPT2LMHeadModel.from_pretrained(hf_model_path)
```

### generation

```python
question = "北京是中国的"
inputs = tokenizer(question,return_tensors='pt')
generation_output = model.generate(**inputs,
                                return_dict_in_generate=True,
                                output_scores=True,
                                max_length=150,
                                # max_new_tokens=80,
                                do_sample=True,
                                top_p = 0.6,
                                # num_beams=5,
                                eos_token_id=50256,
                                pad_token_id=0,
                                num_return_sequences = 5)

for idx,sentence in enumerate(generation_output.sequences):
    print('next sentence %d:\n'%idx,
    tokenizer.decode(sentence).split('<|endoftext|>')[0])
    print('*'*40)
```

## 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}},
}
```