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--- |
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language: zh |
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widget: |
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- text: "[CLS]国 色 天 香 ， 姹 紫 嫣 红 ， 碧 水 青 云 欣 共 赏 -" |
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--- |
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# Chinese Couplet 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 couplets. 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-couplet](https://huggingface.co/uer/gpt2-chinese-couplet). |
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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-couplet") |
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>>> model = GPT2LMHeadModel.from_pretrained("uer/gpt2-chinese-couplet") |
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>>> text_generator = TextGenerationPipeline(model, tokenizer) |
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>>> text_generator("[CLS]丹 枫 江 冷 人 初 去 -", max_length=25, do_sample=True) |
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[{'generated_text': '[CLS]丹 枫 江 冷 人 初 去 - 黄 叶 声 从 天 外 来 阅 旗'}] |
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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-couplet") |
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>>> model = GPT2LMHeadModel.from_pretrained("uer/gpt2-chinese-couplet") |
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>>> text_generator = TextGenerationPipeline(model, tokenizer) |
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>>> text_generator("[CLS]丹 枫 江 冷 人 初 去 -", max_length=25, do_sample=True) |
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[{'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]'}] |
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``` |
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## Training data |
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Training data contains 700,000 Chinese couplets which are collected by [couplet-clean-dataset](https://github.com/v-zich/couplet-clean-dataset). |
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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 25,000 steps with a sequence length of 64. |
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``` |
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python3 preprocess.py --corpus_path corpora/couplet.txt \ |
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--vocab_path models/google_zh_vocab.txt \ |
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--dataset_path couplet_dataset.pt --processes_num 16 \ |
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--seq_length 64 --data_processor lm |
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``` |
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``` |
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python3 pretrain.py --dataset_path couplet_dataset.pt \ |
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--vocab_path models/google_zh_vocab.txt \ |
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--config_path models/gpt2/config.json \ |
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--output_model_path models/couplet_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 25000 --save_checkpoint_steps 5000 --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 couplet_gpt2_model.bin-25000 \ |
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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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``` |
