| CPM | |
| Overview | |
| The CPM model was proposed in CPM: A Large-scale Generative Chinese Pre-trained Language Model by Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin, | |
| Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen, | |
| Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang, Juanzi Li, Xiaoyan Zhu, Maosong Sun. | |
| The abstract from the paper is the following: | |
| Pre-trained Language Models (PLMs) have proven to be beneficial for various downstream NLP tasks. Recently, GPT-3, | |
| with 175 billion parameters and 570GB training data, drew a lot of attention due to the capacity of few-shot (even | |
| zero-shot) learning. However, applying GPT-3 to address Chinese NLP tasks is still challenging, as the training corpus | |
| of GPT-3 is primarily English, and the parameters are not publicly available. In this technical report, we release the | |
| Chinese Pre-trained Language Model (CPM) with generative pre-training on large-scale Chinese training data. To the best | |
| of our knowledge, CPM, with 2.6 billion parameters and 100GB Chinese training data, is the largest Chinese pre-trained | |
| language model, which could facilitate several downstream Chinese NLP tasks, such as conversation, essay generation, | |
| cloze test, and language understanding. Extensive experiments demonstrate that CPM achieves strong performance on many | |
| NLP tasks in the settings of few-shot (even zero-shot) learning. | |
| This model was contributed by canwenxu. The original implementation can be found | |
| here: https://github.com/TsinghuaAI/CPM-Generate | |
| CPM's architecture is the same as GPT-2, except for tokenization method. Refer to GPT-2 documentation for | |
| API reference information. | |
| CpmTokenizer | |
| [[autodoc]] CpmTokenizer | |
| CpmTokenizerFast | |
| [[autodoc]] CpmTokenizerFast |