T5 for Chinese Couplet(t5-chinese-couplet) Model

T5中文对联生成模型

t5-chinese-couplet evaluate couplet test data：

The overall performance of T5 on couplet test :

在Couplet测试集上生成结果满足字数相同、词性对齐、词面对齐、形似要求，而语义对仗工整和平仄合律还不满足。

T5的网络结构(原生T5)：

Usage

本项目开源在文本生成项目： textgen ，可支持T5模型，通过如下命令调用：

Install package:

pip install -U textgen

from textgen import T5Model
model = T5Model("t5", "shibing624/t5-chinese-couplet")
r = model.predict(["对联：丹枫江冷人初去"])
print(r) # ['白石矶寒客不归']

Usage (HuggingFace Transformers)

Without textgen , you can use the model like this:

First, you pass your input through the transformer model, then you get the generated sentence.

Install package:

pip install transformers 

from transformers import T5ForConditionalGeneration, T5Tokenizer

tokenizer = T5Tokenizer.from_pretrained("shibing624/t5-chinese-couplet")
model = T5ForConditionalGeneration.from_pretrained("shibing624/t5-chinese-couplet")


def batch_generate(input_texts, max_length=64):
    features = tokenizer(input_texts, return_tensors='pt')
    outputs = model.generate(input_ids=features['input_ids'],
                             attention_mask=features['attention_mask'],
                             max_length=max_length)
    return tokenizer.batch_decode(outputs, skip_special_tokens=True)


r = batch_generate(["对联：丹枫江冷人初去"])
print(r)

output:

['白石矶寒客不归']

模型文件组成：

t5-chinese-couplet
    ├── config.json
    ├── model_args.json
    ├── pytorch_model.bin
    ├── special_tokens_map.json
    ├── tokenizer_config.json
    ├── spiece.model
    └── vocab.txt

训练数据集

• 数据： 对联github 、 清洗过的对联github
• 相关内容
  • Huggingface
  • LangZhou Chinese MengZi T5 pretrained Model and paper
  • textgen

数据格式：

head -n 1 couplet_files/couplet/train/in.txt
晚 风 摇 树 树 还 挺 

head -n 1 couplet_files/couplet/train/out.txt
晨 露 润 花 花 更 红 

如果需要训练T5模型，请参考 https://github.com/shibing624/textgen/blob/main/docs/%E5%AF%B9%E8%81%94%E7%94%9F%E6%88%90%E6%A8%A1%E5%9E%8B%E5%AF%B9%E6%AF%94.md

Citation

@software{textgen,
  author = {Xu Ming},
  title = {textgen: Implementation of Text Generation models},
  year = {2022},
  url = {https://github.com/shibing624/textgen},
}