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--- |
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language: zh |
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widget: |
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- text: "[CLS] 万 叠 春 山 积 雨 晴 ，" |
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- text: "[CLS] 青 山 削 芙 蓉 ，" |
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--- |
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# Chinese Poem GPT2 Model |
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## Model description |
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The model is used to generate Chinese ancient poems. 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-poem][poem]. |
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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. |
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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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When the parameter skip_special_tokens is True: |
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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-poem") |
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>>> model = GPT2LMHeadModel.from_pretrained("uer/gpt2-chinese-poem") |
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>>> text_generator = TextGenerationPipeline(model, tokenizer) |
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>>> text_generator("[CLS]梅 山 如 积 翠 ，", max_length=50, do_sample=True) |
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[{'generated_text': '[CLS]梅 山 如 积 翠 ， 的 手 堪 捧 。 遥 遥 仙 人 尉 ， 盘 盘 故 时 陇 。 丹 泉 清 可 鉴 ， 石 乳 甘 于 。 行 将 解 尘 缨 ， 于 焉 蹈 高 踵 。 我'}] |
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``` |
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When the parameter skip_special_tokens is False: |
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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-poem") |
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>>> model = GPT2LMHeadModel.from_pretrained("uer/gpt2-chinese-poem") |
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>>> text_generator = TextGenerationPipeline(model, tokenizer) |
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>>> text_generator("[CLS]梅 山 如 积 翠 ，", max_length=50, do_sample=True) |
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[{'generated_text': '[CLS]梅 山 如 积 翠 ， 的 [UNK] 手 堪 捧 。 遥 遥 仙 人 尉 ， 盘 盘 故 时 陇 。 丹 泉 清 可 鉴 ， 石 乳 甘 可 捧 。 银 汉 迟 不 来 ， 槎 头 欲 谁 揽 。 何'}] |
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``` |
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## Training data |
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Training data contains 800,000 Chinese ancient poems which are collected by [chinese-poetry](https://github.com/chinese-poetry/chinese-poetry) and [Poetry](https://github.com/Werneror/Poetry) projects. |
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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 200,000 steps with a sequence length of 128. We use extended vocabulary to handle out-of-vocabulary words. The Chinese character that occurs greater than or equal to 100 in poem corpus is added to the vocabulary. |
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``` |
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python3 preprocess.py --corpus_path corpora/poem.txt \ |
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--vocab_path models/poem_zh_vocab.txt \ |
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--dataset_path poem_dataset.pt --processes_num 16 \ |
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--seq_length 128 --target lm |
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``` |
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``` |
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python3 pretrain.py --dataset_path poem_dataset.pt \ |
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--vocab_path models/poem_zh_vocab.txt \ |
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--output_model_path models/poem_gpt2_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 200000 --save_checkpoint_steps 50000 --report_steps 1000 \ |
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--learning_rate 5e-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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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 poem_gpt2_base_model.bin-200000 \ |
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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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``` |
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[poem]: https://huggingface.co/uer/gpt2-chinese-poem |
