🚀 Chinese Couplet GPT2 Model
This is a model for generating Chinese couplets, pre - trained using advanced pre - training frameworks.
🚀 Quick Start
You can quickly start using this model to generate Chinese couplets. The following will introduce the model's description, usage, training data, and training process.
✨ Features
- Powerful Pre - training Frameworks: The model can be pre - trained by [UER - py](https://github.com/dbiir/UER - py/) and TencentPretrain, which are introduced in this paper and this paper respectively.
- Convenient Download Channels: You can download the model from multiple sources, such as the [UER - py Modelzoo page](https://github.com/dbiir/UER - py/wiki/Modelzoo), [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).
📦 Installation
There is no specific installation process described in the original text. You can directly download the model from the provided sources.
💻 Usage Examples
Basic Usage
You can use the model directly with a pipeline for text generation. When the parameter skip_special_tokens
is True
:
>>> from transformers import BertTokenizer, GPT2LMHeadModel, TextGenerationPipeline
>>> tokenizer = BertTokenizer.from_pretrained("uer/gpt2-chinese-couplet")
>>> model = GPT2LMHeadModel.from_pretrained("uer/gpt2-chinese-couplet")
>>> text_generator = TextGenerationPipeline(model, tokenizer)
>>> text_generator("[CLS]丹 枫 江 冷 人 初 去 -", max_length=25, do_sample=True)
[{'generated_text': '[CLS]丹 枫 江 冷 人 初 去 - 黄 叶 声 从 天 外 来 阅 旗'}]
Advanced Usage
When the parameter skip_special_tokens
is False
:
>>> from transformers import BertTokenizer, GPT2LMHeadModel, TextGenerationPipeline
>>> tokenizer = BertTokenizer.from_pretrained("uer/gpt2-chinese-couplet")
>>> model = GPT2LMHeadModel.from_pretrained("uer/gpt2-chinese-couplet")
>>> text_generator = TextGenerationPipeline(model, tokenizer)
>>> text_generator("[CLS]丹 枫 江 冷 人 初 去 -", max_length=25, do_sample=True)
[{'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]'}]
📚 Documentation
Model Description
The model is pre - trained by [UER - py](https://github.com/dbiir/UER - py/), which is introduced in this paper. Besides, the model could also be pre - trained by TencentPretrain introduced in this paper, which inherits UER - py to support models with parameters above one billion, and extends it to a multimodal pre - training framework.
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).
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.
Training Data
Training data contains 700,000 Chinese couplets which are collected by [couplet - clean - dataset](https://github.com/v - zich/couplet - clean - dataset).
Training Procedure
The model is pre - trained by [UER - py](https://github.com/dbiir/UER - py/) on Tencent Cloud. We pre - train 25,000 steps with a sequence length of 64.
python3 preprocess.py --corpus_path corpora/couplet.txt \
--vocab_path models/google_zh_vocab.txt \
--dataset_path couplet_dataset.pt --processes_num 16 \
--seq_length 64 --data_processor lm
python3 pretrain.py --dataset_path couplet_dataset.pt \
--vocab_path models/google_zh_vocab.txt \
--config_path models/gpt2/config.json \
--output_model_path models/couplet_gpt2_model.bin \
--world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \
--total_steps 25000 --save_checkpoint_steps 5000 --report_steps 1000 \
--learning_rate 5e-4 --batch_size 64
Finally, we convert the pre - trained model into Huggingface's format:
python3 scripts/convert_gpt2_from_uer_to_huggingface.py --input_model_path models/couplet_gpt2_model.bin-25000 \
--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}
}
@article{zhao2023tencentpretrain,
title={TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities},
author={Zhao, Zhe and Li, Yudong and Hou, Cheng and Zhao, Jing and others},
journal={ACL 2023},
pages={217},
year={2023}
}
📄 License
The original text does not provide license information, so this section is skipped.
🔧 Technical Details
The original text does not provide in - depth technical details, so this section is skipped.
⚠️ Important Note
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.
Property |
Details |
Model Type |
A model for generating Chinese couplets, pre - trained by UER - py or TencentPretrain |
Training Data |
700,000 Chinese couplets collected by [couplet - clean - dataset](https://github.com/v - zich/couplet - clean - dataset) |