 Usage (HuggingFace Transformers) Without [textgen](https://github.com/shibing624/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  ```  ```python 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: ```shell ['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 ```   