readme: ---
  language: zh
  widget: 
  - text: "子墨子曰"
  ---
  
  # Ancient chinese GPT2 model 
  
  ## Model description
  This model is a GPT2 model trained to generate ancient Chinese text, with `bert-base-chinese` as tokenizer. 
  
  ## Training data
  It's trained on the classic Chinese texts fetched from ctext.org. Current training data is really small scale -- all training text data together was 19Mb. 
  
  <details>
    <summary> "Reading list" of this model </summary>
    
      * 孟子 "mengzi"
      * 论语 "analects"
      * 商君书 "shang-jun-shu"
      * 礼记 "liji"
      * 孙子兵法 "art-of-war"
      * 墨子 "mozi"
      * 庄子 "zhuangzi"
      * 道德经 "dao-de-jing"
      * 韩非子 "hanfeizi"
      * 史记 "shiji"
      * 战国策 "zhan-guo-ce"
      * 汉书 "han-shu"
      * 后汉书 "hou-han-shu"
      * 三国志 "sanguozhi"
      * 世说新语 "shi-shuo-xin-yu"
      * 颜氏家训 "yan-shi-jia-xun"
      * 金瓶梅 "jinpingmei"
      * 西游记 "xiyouji"
      * 红楼梦 "hongloumeng"
  </details>
  
  
  ## How to use
  You can use the model directly with a pipeline for text generation:
  
  ```python
  from transformers import pipeline, GPT2LMHeadModel
  
  model = GPT2LMHeadModel.from_pretrained("binxu/Ziyue-GPT2")
  generator = pipeline('text-generation', model=model, tokenizer='bert-base-chinese')
  outputs = generator("子墨子曰", max_length=50, num_return_sequences=5, num_beams=10, repetition_penalty=1.5) 
  
  [{'generated_text': '子墨子曰 ： 吾 未 得 见 之 时 ， 知 有 失 得 之 时 ， 有 为 之 者 。 氏 ， 圣 王 之 时 ， 万 乘 之 世 ， 圣 人 不 易 之 道 也 。'}]
  ```