transformers/model_cards/uer/gpt2-chinese-couplet/README.md

language	widget
zh	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, or via HuggingFace from the link gpt2-chinese-couplet.

Since 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.

How to use

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

When the parameter skip_special_tokens is True:

>>> from transformers import BertTokenizer, GPT2LMHeadModel, TextGenerationPipeline
>>> from transformers import 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:

>>> 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]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

Contains 700,000 Chinese couplets collected by couplet-clean-dataset.

Training procedure

Models are pre-trained by UER-py on Tencent Cloud TI-ONE. 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.pt --processes_num 16 \
	              --seq_length 64 --target lm 

python3 pretrain.py --dataset_path couplet.pt \
	            --vocab_path models/google_zh_vocab.txt \
		    --output_model_path models/couplet_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 5000 --total_steps 25000 \
		    --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}
}