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--- |
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language: zh |
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widget: |
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- text: "天下熙熙，" |
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- text: "天气不错，" |
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--- |
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<h1 align="center"> |
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CPM-Generate-distill |
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</h1> |
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CPM(Chinese Pre-Trained Language Models), which has 2.6B parameters, made by the research team of Beijing Zhiyuan Institute of artificial intelligence and Tsinghua University @TsinghuaAI. |
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[repo: CPM-Generate](https://github.com/TsinghuaAI/CPM-Generate) |
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The One Thing You Need to Know is this model is not uploaded by official, the conver script is [here](https://github.com/mymusise/CPM-TF2Transformer/blob/main/transfor_CMP.ipynb) |
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And the `CPM-Generate-distill` is the distill model of `CPM`. |
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# How to use |
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How to use this model directly from the 🤗/transformers library: |
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```python |
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from transformers import XLNetTokenizer, TFGPT2LMHeadModel |
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from transformers import TextGenerationPipeline |
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import jieba |
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# add spicel process |
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class XLNetTokenizer(XLNetTokenizer): |
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translator = str.maketrans(" \n", "\u2582\u2583") |
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def _tokenize(self, text, *args, **kwargs): |
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text = [x.translate(self.translator) for x in jieba.cut(text, cut_all=False)] |
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text = " ".join(text) |
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return super()._tokenize(text, *args, **kwargs) |
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def _decode(self, *args, **kwargs): |
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text = super()._decode(*args, **kwargs) |
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text = text.replace(' ', '').replace('\u2582', ' ').replace('\u2583', '\n') |
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return text |
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tokenizer = XLNetTokenizer.from_pretrained('mymusise/CPM-Generate-distill') |
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model = TFGPT2LMHeadModel.from_pretrained("mymusise/CPM-Generate-distill") |
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text_generater = TextGenerationPipeline(model, tokenizer) |
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print(text_generater("天下熙熙，", max_length=15, top_k=1, use_cache=True, prefix='')) |
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``` |
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