README.md

---
language: zh 
widget:
- text: "[CLS] 万 叠 春 山 积 雨 晴 ，"
- text: "[CLS] 青 山 削 芙 蓉 ，"

---

# Chinese GPT2 Language Models

## Model description

This is the set of  two Chinese GPT2 language models pre-trained by [UER-py](https://www.aclweb.org/anthology/D19-3041.pdf).

You can download the two Chinese GPT2 language models via HuggingFace from the links below:

|     Model     |          [gpt2-chinese-poem][poem]           |     [gpt2-chinese-couplet][couplet]     |
| :-----------: | :------------------------------------------: | :-------------------------------------: |
| Training data | Contains about 800,000 chinese ancient poems | Contains about 700,000 chinese couplets |



## How to use

Because the parameter ***skip_special_tokens*** is used in the ***pipelines.py*** , special tokens such as [SEP], [UNK] will be deleted, and the output results may not be neat.

You can use this model directly with a pipeline for text generation:

When the parameter ***skip_special_tokens***  is True:

```python
>>> from transformers import BertTokenizer, GPT2LMHeadModel, TextGenerationPipeline
>>> from transformers import TextGenerationPipeline, 
>>> tokenizer = BertTokenizer.from_pretrained("uer/gpt2-chinese-poem")
>>> model = GPT2LMHeadModel.from_pretrained("uer/gpt2-chinese-poem")
>>> text_generator = TextGenerationPipeline(model, tokenizer)   
>>> text_generator("[CLS]梅 山 如 积 翠 ，", max_length=50, do_sample=True)
	[{'generated_text': '[CLS]梅 山 如 积 翠 ， 的 手 堪 捧 。 遥 遥 仙 人 尉 ， 盘 盘 故 时 陇 。 丹 泉 清 可 鉴 ， 石 乳 甘 于 。 行 将 解 尘 缨 ， 于 焉 蹈 高 踵 。 我'}]
```

When the parameter ***skip_special_tokens***  is Flase:

```python
>>> from transformers import BertTokenizer, GPT2LMHeadModel, TextGenerationPipeline
>>> from transformers import TextGenerationPipeline, 
>>> tokenizer = BertTokenizer.from_pretrained("uer/gpt2-chinese-poem")
>>> model = GPT2LMHeadModel.from_pretrained("uer/gpt2-chinese-poem")
>>> text_generator = TextGenerationPipeline(model, tokenizer)   
>>> text_generator("[CLS]梅 山 如 积 翠 ，", max_length=50, do_sample=True)
	[{'generated_text': '[CLS]梅 山 如 积 翠 ， 的 [UNK] 手 堪 捧 。 遥 遥 仙 人 尉 ， 盘 盘 故 时 陇 。 丹 泉 清 可 鉴 ， 石 乳 甘 可 捧 。 银 汉 迟 不 来 ， 槎 头 欲 谁 揽 。 何'}]
```

## Training data

Contains about 800,000 chinese ancient poems.

## Training procedure

Models are pre-trained by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud TI-ONE](https://cloud.tencent.com/product/tione/). We pre-train 200,000 steps with a sequence length of 128.

```
python3 preprocess.py --corpus_path corpora/poem.txt \
					  --vocab_path models/google_zh_vocab.txt \  
					  --dataset_path poem.pt --processes_num 16 \
					  --seq_length 128 --target lm 
```

```
python3 pretrain.py --dataset_path poem.pt \
				    --vocab_path models/google_zh_vocab.txt \
					--output_model_path models/poem_gpt_base_model.bin \  
					--config_path models/bert_base_config.json --learning_rate 5e-4 \
					--tie_weight --world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \
					--batch_size 64 --report_steps 1000 \
					--save_checkpoint_steps 50000 --total_steps 200000 \
					--embedding gpt --encoder gpt2 --target lm

```

### BibTeX entry and citation info

```
@article{zhao2019uer,
  title={UER: An Open-Source Toolkit for Pre-training Models},
  author={Zhao, Zhe and Chen, Hui and Zhang, Jinbin and Zhao, Xin and Liu, Tao and Lu, Wei and Chen, Xi and Deng, Haotang and Ju, Qi and Du, Xiaoyong},
  journal={EMNLP-IJCNLP 2019},
  pages={241},
  year={2019}
}
```

[poem]: https://huggingface.co/uer/gpt2-chinese-poem
[couplet]: https://huggingface.co/uer/gpt2-chinese-couplet