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--- |
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language: zh |
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widget: |
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- text: "今天是下雨天" |
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- text: "走向森林" |
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--- |
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<h1 align="center"> |
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CPM |
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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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# Overview |
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- **Language model**: CPM |
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- **Model size**: 2.6B parameters |
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- **Language**: Chinese |
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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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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-GPT2') |
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model = TFGPT2LMHeadModel.from_pretrained("mymusise/CPM-GPT2") |
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``` |
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How to generate text |
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```python |
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from transformers import TextGenerationPipeline |
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text_generater = TextGenerationPipeline(model, tokenizer) |
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texts = [ |
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'今天天气不错', |
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'天下武功, 唯快不', |
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""" |
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我们在火星上发现了大量的神奇物种。有神奇的海星兽，身上是粉色的，有5条腿；有胆小的猫猫兽，橘色，有4条腿；有令人恐惧的蜈蚣兽，全身漆黑，36条腿；有纯洁的天使兽，全身洁白无瑕，有3条腿；有贪吃的汪汪兽，银色的毛发，有5条腿；有蛋蛋兽，紫色，8条腿。 |
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请根据上文，列出一个表格，包含物种名、颜色、腿数量。 |
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|物种名|颜色|腿数量| |
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|亚古兽|金黄|2| |
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|海星兽|粉色|5| |
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|猫猫兽|橘色|4| |
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|蜈蚣兽|漆黑|36| |
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""" |
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] |
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for text in texts: |
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token_len = len(tokenizer._tokenize(text)) |
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print(text_generater(text, max_length=token_len + 15, top_k=1, use_cache=True, prefix='')[0]['generated_text']) |
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print(text_generater(text, max_length=token_len + 15, do_sample=True, top_k=5)[0]['generated_text']) |
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``` |
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