language, license, tags, tasks, model_type, domain, frameworks, backbone, widgets

language	license	tags	tasks	model_type	domain	frameworks	backbone	widgets
zh	Apache License 2.0	generate gpt2	text-generation	GPT2	nlp	pytorch	transformer	task examples text-generation name inputs 1 data 西湖的景色 name inputs 2 data 北京是中国的

Wenzhong-GPT2-110M

• Github: Fengshenbang-LM
• Docs: Fengshenbang-Docs

简介 Brief Introduction

善于处理NLG任务，中文版的GPT2-Small。

Focused on handling NLG tasks, Chinese GPT2-Small.

模型分类 Model Taxonomy

需求 Demand	任务 Task	系列 Series	模型 Model	参数 Parameter	额外 Extra
通用 General	自然语言生成 NLG	闻仲 Wenzhong	GPT2	110M	中文 Chinese

模型信息 Model Information

类似于Wenzhong2.0-GPT2-3.5B-chinese，我们实现了一个small版本的12层的Wenzhong-GPT2-110M，并且在悟道（300G版本）上面进行预训练。

Similar to Wenzhong2.0-GPT2-3.5B-chinese, we implement a small size Wenzhong-GPT2-110M with 12 layers, which is pre-trained on Wudao Corpus (300G version).

使用 Usage

# load 模型
from transformers import GPT2Tokenizer,GPT2LMHeadModel
hf_model_path = 'IDEA-CCNL/Wenzhong-GPT2-110M' # 或者此处为modelscope git clone下载后的模型路径
tokenizer = GPT2Tokenizer.from_pretrained(hf_model_path)
model = GPT2LMHeadModel.from_pretrained(hf_model_path)
model.eval()
# 模型推理
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)

引用 Citation

如果您在您的工作中使用了我们的模型，可以引用我们的论文：

If you are using the resource for your work, please cite the our paper:

@article{fengshenbang,
  author    = {Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen and Ruyi Gan and Jiaxing Zhang},
  title     = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence},
  journal   = {CoRR},
  volume    = {abs/2209.02970},
  year      = {2022}
}

也可以引用我们的网站:

You can also cite our website:

@misc{Fengshenbang-LM,
  title={Fengshenbang-LM},
  author={IDEA-CCNL},
  year={2021},
  howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}