CPM

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.

repo: CPM-Generate

The One Thing You Need to Know is this model is not uploaded by official, the conver script is here

Overview

  • Language model: CPM
  • Model size: 2.6B parameters
  • Language: Chinese

How to use

How to use this model directly from the 🤗/transformers library:

from transformers import XLNetTokenizer, TFGPT2LMHeadModel
import jieba

# add spicel process 
class XLNetTokenizer(XLNetTokenizer):
    translator = str.maketrans(" \n", "\u2582\u2583")

    def _tokenize(self, text, *args, **kwargs):
        text = [x.translate(self.translator) for x in jieba.cut(text, cut_all=False)]
        text = " ".join(text)
        return super()._tokenize(text, *args, **kwargs)

    def _decode(self, *args, **kwargs):
        text = super()._decode(*args, **kwargs)
        text = text.replace(' ', '').replace('\u2582', ' ').replace('\u2583', '\n')
        return text


tokenizer = XLNetTokenizer.from_pretrained('mymusise/CPM-GPT2-FP16')
model = TFGPT2LMHeadModel.from_pretrained("mymusise/CPM-GPT2-FP16")

How to generate text

from transformers import TextGenerationPipeline


text_generater = TextGenerationPipeline(model, tokenizer)

texts = [
    '今天天气不错',
    '天下武功, 唯快不',
    """
    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。

    请根据上文,列出一个表格,包含物种名、颜色、腿数量。
    |物种名|颜色|腿数量|
    |亚古兽|金黄|2|
    |海星兽|粉色|5|
    |猫猫兽|橘色|4|
    |蜈蚣兽|漆黑|36|
    """
]

for text in texts:
    token_len = len(tokenizer._tokenize(text))
    print(text_generater(text, max_length=token_len + 15, top_k=1, use_cache=True, prefix='')[0]['generated_text'])
    print(text_generater(text, max_length=token_len + 15, do_sample=True, top_k=5)[0]['generated_text'])

avatar

You can try it on colab

Build
Downloads last month
407
Hosted inference API
Text Generation
This model can be loaded on the Inference API on-demand.