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 pre-trained by UER-py, which is introduced in this paper. Besides, the model could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support models with parameters above one billion, and extends it to a multimodal pre-training framework.
The model is used to generate Chinese couplets. You can download the model from the UER-py Modelzoo page, or 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, 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:
>>> 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:
>>> 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.
Training procedure
The model is pre-trained by UER-py on Tencent Cloud. 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 models/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}
}
@article{zhao2023tencentpretrain,
title={TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities},
author={Zhao, Zhe and Li, Yudong and Hou, Cheng and Zhao, Jing and others},
journal={ACL 2023},
pages={217},
year={2023}
}