metadata
language: Chinese
datasets: CLUECorpusSmall
widget:
- text: 这是很久之前的事情了
Chinese GPT2 Model
Model description
The model is used to generate Chinese texts. You can download the model either from the GPT2-Chinese Github page, or via HuggingFace from the link gpt2-chinese-cluecorpussmall.
How to use
You can use the model directly with a pipeline for text generation:
>>> from transformers import BertTokenizer, GPT2LMHeadModel, TextGenerationPipeline
>>> tokenizer = BertTokenizer.from_pretrained("uer/gpt2-chinese-cluecorpussmall")
>>> model = GPT2LMHeadModel.from_pretrained("uer/gpt2-chinese-cluecorpussmall")
>>> text_generator = TextGenerationPipeline(model, tokenizer)
>>> text_generator("这是很久之前的事情了", max_length=100, do_sample=True)
[{'generated_text': '这是很久之前的事情了 ， 我 曾 经 把 这 个 当 做 一 种 思 想 的 传 承 ， 或 者 是 人 生 的 回 顾 ， 当 时 我 们 是 一 个 刚 刚 加 入 的 时 候 就 想 要 加 入 他 们 ， 于 是 我 们 每 天 看 到 他 们 ， 加 上 他 们 的 各 种 不 可 思 议 的 行 为 ， 直 到 现 在 ， 我 们 的 人 生 才 完 整 起 来 。'}]
Training data
CLUECorpusSmall is used as training data.
Training procedure
The model is pre-trained by UER-py on Tencent Cloud. We pre-train 1,000,000 steps with a sequence length of 128 and then pre-train 250,000 additional steps with a sequence length of 1024.
Stage1:
python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \\
--vocab_path models/google_zh_vocab.txt \\
--dataset_path cluecorpussmall_lm_seq128_dataset.pt \\
--seq_length 128 --processes_num 32 --target lm
python3 pretrain.py --dataset_path cluecorpussmall_lm_seq128_dataset.pt \\
--vocab_path models/google_zh_vocab.txt \\
--config_path models/gpt2/config.json \\
--output_model_path models/cluecorpussmall_gpt2_seq128_model.bin \\
--world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \\
--total_steps 1000000 --save_checkpoint_steps 100000 --report_steps 50000 \\
--learning_rate 1e-4 --batch_size 64 \\
--embedding word_pos --remove_embedding_layernorm \\
--encoder transformer --mask causal --layernorm_positioning pre \\
--target lm --tie_weight
Stage2:
python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \\
--vocab_path models/google_zh_vocab.txt \\
--dataset_path cluecorpussmall_lm_seq1024_dataset.pt \\
--seq_length 1024 --processes_num 32 --target lm
python3 pretrain.py --dataset_path cluecorpussmall_lm_seq1024_dataset.pt \\
--pretrained_model_path models/cluecorpussmall_gpt2_seq128_model.bin-1000000 \\
--vocab_path models/google_zh_vocab.txt \\
--config_path models/gpt2/config.json \\
--output_model_path models/cluecorpussmall_gpt2_seq1024_model.bin \\
--world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \\
--total_steps 250000 --save_checkpoint_steps 50000 --report_steps 10000 \\
--learning_rate 5e-5 --batch_size 16 \\
--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 cluecorpussmall_gpt2_seq1024_model.bin-250000 \\
--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}
}