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--- |
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language: Chinese |
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datasets: CLUECorpusSmall |
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widget: |
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- text: "这是很久之前的事情了" |
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--- |
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# Chinese GPT2 Model |
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## Model description |
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The model is used to generate Chinese texts. 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-cluecorpussmall](https://huggingface.co/uer/gpt2-chinese-cluecorpussmall). |
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## How to use |
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You can use the model directly with a pipeline for text generation: |
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```python |
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>>> from transformers import BertTokenizer, GPT2LMHeadModel, TextGenerationPipeline |
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>>> tokenizer = BertTokenizer.from_pretrained("uer/gpt2-chinese-cluecorpussmall") |
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>>> model = GPT2LMHeadModel.from_pretrained("uer/gpt2-chinese-cluecorpussmall") |
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>>> text_generator = TextGenerationPipeline(model, tokenizer) |
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>>> text_generator("这是很久之前的事情了", max_length=100, do_sample=True) |
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[{'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 。'}] |
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``` |
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## Training data |
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[CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020/) is used as training data. |
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## Training procedure |
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The model is pre-trained by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud TI-ONE](https://cloud.tencent.com/product/tione/). 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. |
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Stage1: |
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``` |
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python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \ |
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--vocab_path models/google_zh_vocab.txt \ |
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--dataset_path cluecorpussmall_lm_seq128_dataset.pt \ |
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--seq_length 128 --processes_num 32 --target lm |
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``` |
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``` |
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python3 pretrain.py --dataset_path cluecorpussmall_lm_seq128_dataset.pt \ |
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--vocab_path models/google_zh_vocab.txt \ |
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--output_model_path models/cluecorpussmall_gpt2_seq128_model.bin \ |
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--config_path models/gpt2/config.json \ |
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--world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \ |
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--total_steps 1000000 --save_checkpoint_steps 100000 --report_steps 50000 \ |
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--learning_rate 1e-4 --batch_size 64 \ |
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--embedding word_pos --remove_embedding_layernorm \ |
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--encoder transformer --mask causal --layernorm_positioning pre \ |
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--target lm --tie_weight |
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``` |
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Stage2: |
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``` |
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python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \ |
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--vocab_path models/google_zh_vocab.txt \ |
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--dataset_path cluecorpussmall_lm_seq1024_dataset.pt \ |
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--seq_length 1024 --processes_num 32 --target lm |
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``` |
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``` |
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python3 pretrain.py --dataset_path cluecorpussmall_lm_seq1024_dataset.pt \ |
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--pretrained_model_path models/cluecorpussmall_gpt2_seq128_model.bin-1000000 \ |
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--vocab_path models/google_zh_vocab.txt \ |
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--output_model_path models/cluecorpussmall_gpt2_seq1024_model.bin \ |
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--config_path models/gpt2/config.json \ |
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--world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \ |
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--total_steps 250000 --save_checkpoint_steps 50000 --report_steps 10000 \ |
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--learning_rate 5e-5 --batch_size 16 \ |
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--embedding word_pos --remove_embedding_layernorm \ |
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--encoder transformer --mask causal --layernorm_positioning pre \ |
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--target lm --tie_weight |
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``` |
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Finally, we convert the pre-trained model into Huggingface's format: |
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``` |
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python3 scripts/convert_gpt2_from_uer_to_huggingface.py --input_model_path cluecorpussmall_gpt2_seq1024_model.bin-250000 \ |
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--output_model_path pytorch_model.bin \ |
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--layers_num 12 |
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``` |
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### BibTeX entry and citation info |
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``` |
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@article{zhao2019uer, |
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title={UER: An Open-Source Toolkit for Pre-training Models}, |
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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}, |
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journal={EMNLP-IJCNLP 2019}, |
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pages={241}, |
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year={2019} |
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} |
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``` |