 How to use  Our model is trained based on the [fairseq](https://github.com/facebookresearch/fairseq). As a result, the inference and finetuning depend on it.  For inference, we modify some parts of the original fairseq codes. Mainly  > fairseq-0.12.2/fairseq/sequence_generator.py  We integrate the EET with sequence_generator. We replace the eos token to a token unlikely to be sampled to ensure the generated text length. The repetition penalty trick is also modified. You can change the penalty strength by adjusting the value of `self.ban_weight`.    Then, to keep the eos token in the final generated text, we change the line 75 `include_eos=False` to `include_eos=True` in > fairseq-0.12.2/fairseq/data/dictionary.py   Finally, to pass in parameters in python scripts, we remove the line 67 ~ line 69 in >fairseq-0.12.2/fairseq/dataclass/utils.py  Below are the install tutorial.  ``` 