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, 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
>>> 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-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
Training data contains 700,000 Chinese couplets which are 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_dataset.pt --processes_num 16 \
--seq_length 64 --target lm
python3 pretrain.py --dataset_path couplet_dataset.pt \
--vocab_path models/google_zh_vocab.txt \
--output_model_path models/couplet_gpt2_base_model.bin \
--config_path models/bert_base_config.json \
--tie_weight --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 \
--embedding word_pos --remove_embedding_layernorm \
--encoder transformer --mask causal --layernorm_positioning pre \
--target lm --tie_weight
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_base_model.bin-25000 \
--output_model_path pytorch_model.bin \
--layers_num 12
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}
}