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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] 大 漠" |
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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 pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the model could also be pre-trained by [TencentPretrain](https://github.com/Tencent/TencentPretrain) introduced in [this paper](https://arxiv.org/abs/2212.06385), which inherits UER-py to support models with parameters above one billion, and extends it to a multimodal pre-training framework. |
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The model is used to generate Chinese ancient poems. You can download the model from the [UER-py Modelzoo page](https://github.com/dbiir/UER-py/wiki/Modelzoo), or [GPT2-Chinese Github page](https://github.com/Morizeyao/GPT2-Chinese), or via HuggingFace from the link [gpt2-chinese-poem](https://huggingface.co/uer/gpt2-chinese-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, the output results of Hosted inference API (right) may not be properly displayed. |
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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 ， 丛 竹 隠 疏 花 。 水 影 落 寒 濑 ， 竹 声 随 暮 鸦 。 茅 茨 数 间 屋 ， 烟 火 两 三 家 。 安 得 携 琴 酒 ， 相 逢 烟 雨 赊 。 向 湖 边 过 ， 偏 怜 雪 里 看 。 浮 峦 如 画 出 ， 远 树 与 天 连 。 月 上 僧 房 静 ， 风 回 萤 火 寒 。 幽 情 何 可 写 ， 赖 有 子 期 弹 。 棠 真'}] |
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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=100, do_sample=True) |
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[{'generated_text': '[CLS]梅 山 如 积 翠 ， 秀 出 何 其 雄 。 矫 矫 云 间 质 ， 映 日 生 玲 珑 。 根 大 乱 石 结 ， 枝 高 青 云 蒙 。 常 因 风 露 晚 ， 隠 映 瑶 台 中 。 忽 闻 山 石 裂 ， 万 里 吹 天 风 。 又 觉 此 身 高 ， 迥 出 凡 境 空 。 清 影 落 潭 水 ， 暗 香 来 逈 峰 。 却 寻 白 太 白 ， 月 影 摇 江 东 。 [SEP] 而 非'}] |
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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](https://cloud.tencent.com/). 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/google_zh_poem_vocab.txt \ |
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--dataset_path poem_dataset.pt --processes_num 16 \ |
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--seq_length 128 --data_processor 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/google_zh_poem_vocab.txt \ |
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--config_path models/gpt2/config.json \ |
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--output_model_path models/poem_gpt2_model.bin \ |
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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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``` |
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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_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{radford2019language, |
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title={Language Models are Unsupervised Multitask Learners}, |
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author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya}, |
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year={2019} |
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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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@article{zhao2023tencentpretrain, |
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title={TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities}, |
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author={Zhao, Zhe and Li, Yudong and Hou, Cheng and Zhao, Jing and others}, |
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journal={ACL 2023}, |
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pages={217}, |
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year={2023} |
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} |
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``` |
