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--- |
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language: zh |
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widget: |
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- text: "子墨子曰" |
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--- |
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# Ancient chinese GPT2 model |
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## Model description |
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This model is a GPT2 model trained to generate ancient Chinese text, with `bert-base-chinese` as tokenizer. |
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## Training data |
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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. |
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<details> |
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<summary> "Reading list" of this model </summary> |
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* 孟子 "mengzi" |
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* 论语 "analects" |
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* 商君书 "shang-jun-shu" |
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* 礼记 "liji" |
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* 孙子兵法 "art-of-war" |
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* 墨子 "mozi" |
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* 庄子 "zhuangzi" |
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* 道德经 "dao-de-jing" |
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* 韩非子 "hanfeizi" |
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* 史记 "shiji" |
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* 战国策 "zhan-guo-ce" |
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* 汉书 "han-shu" |
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* 后汉书 "hou-han-shu" |
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* 三国志 "sanguozhi" |
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* 世说新语 "shi-shuo-xin-yu" |
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* 颜氏家训 "yan-shi-jia-xun" |
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* 金瓶梅 "jinpingmei" |
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* 西游记 "xiyouji" |
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* 红楼梦 "hongloumeng" |
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</details> |
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## How to use |
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You can use the model directly with a pipeline for text generation: |
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```python |
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from transformers import pipeline, GPT2LMHeadModel |
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model = GPT2LMHeadModel.from_pretrained("binxu/Ziyue-GPT2") |
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generator = pipeline('text-generation', model=model, tokenizer='bert-base-chinese') |
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outputs = generator("子墨子曰", max_length=50, num_return_sequences=5, num_beams=10, repetition_penalty=1.5) |
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[{'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 。'}] |
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``` |
