readme: ---
  language: zh 
  widget:
  - text: "[CLS]国 色 天 香 ， 姹 紫 嫣 红 ， 碧 水 青 云 欣 共 赏 -"
  
  ---
  
  
  # Chinese Couplet GPT2 Model
  
  
  ## Model description
  
  The model is used to generate Chinese couplets. You can download the model either from the [GPT2-Chinese Github page](https://github.com/Morizeyao/GPT2-Chinese), or via HuggingFace from the link [gpt2-chinese-couplet](https://huggingface.co/uer/gpt2-chinese-couplet).
  
  Since the parameter skip_special_tokens is used in the pipelines.py, special tokens such as [SEP], [UNK] will be deleted, the output results of Hosted inference API (right) may not be properly displayed..
  
  ## How to use
  
  You can use the model directly with a pipeline for text generation:
  
  When the parameter skip_special_tokens is True:
  
  ```python
  >>> from transformers import BertTokenizer, GPT2LMHeadModel, TextGenerationPipeline
  >>> tokenizer = BertTokenizer.from_pretrained("uer/gpt2-chinese-couplet")
  >>> model = GPT2LMHeadModel.from_pretrained("uer/gpt2-chinese-couplet")
  >>> text_generator = TextGenerationPipeline(model, tokenizer)   
  >>> text_generator("[CLS]丹 枫 江 冷 人 初 去 -", max_length=25, do_sample=True)
      [{'generated_text': '[CLS]丹 枫 江 冷 人 初 去 - 黄 叶 声 从 天 外 来 阅 旗'}]
  ```
  
  When the parameter skip_special_tokens is False:
  
  ```python
  >>> from transformers import BertTokenizer, GPT2LMHeadModel, TextGenerationPipeline
  >>> tokenizer = BertTokenizer.from_pretrained("uer/gpt2-chinese-couplet")
  >>> model = GPT2LMHeadModel.from_pretrained("uer/gpt2-chinese-couplet")
  >>> text_generator = TextGenerationPipeline(model, tokenizer)   
  >>> text_generator("[CLS]丹 枫 江 冷 人 初 去 -", max_length=25, do_sample=True)
      [{'generated_text': '[CLS]丹 枫 江 冷 人 初 去 - 黄 叶 声 我 酒 不 辞 [SEP] [SEP] [SEP] [SEP] [SEP] [SEP] [SEP] [SEP] [SEP]'}]
  ```
  
  ## Training data
  
  Training data contains 700,000 Chinese couplets which are collected by [couplet-clean-dataset](https://github.com/v-zich/couplet-clean-dataset).
  
  ## Training procedure
  
  The model is pre-trained by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud](https://cloud.tencent.com/). We pre-train 25,000 steps with a sequence length of 64.
  
  ```
  python3 preprocess.py --corpus_path corpora/couplet.txt \
                        --vocab_path models/google_zh_vocab.txt \
                        --dataset_path couplet_dataset.pt --processes_num 16 \
                        --seq_length 64 --data_processor lm 
  ```
  
  ```
  python3 pretrain.py --dataset_path couplet_dataset.pt \
                      --vocab_path models/google_zh_vocab.txt \
                      --config_path models/gpt2/config.json \
                      --output_model_path models/couplet_gpt2_model.bin \
                      --world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \
                      --total_steps 25000 --save_checkpoint_steps 5000 --report_steps 1000 \
                      --learning_rate 5e-4 --batch_size 64
  ```
  
  Finally, we convert the pre-trained model into Huggingface's format:
  ```
  python3 scripts/convert_gpt2_from_uer_to_huggingface.py --input_model_path couplet_gpt2_model.bin-25000 \
                                                          --output_model_path pytorch_model.bin \
                                                          --layers_num 12
  ```
  
  ### BibTeX entry and citation info
  
  ```
  @article{radford2019language,
    title={Language Models are Unsupervised Multitask Learners},
    author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
    year={2019}
  }
  
  @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}
  }
  ```