SAILER is a structure-aware pre-trained language model. It is highlighted in the following three aspects: 

- SAILER fully utilizes the structural information contained in legal case documents and pays more attention to key legal elements, similar to how legal experts browse legal case documents. 

- SAILER employs an asymmetric encoder-decoder architecture to integrate several different pre-training objectives. In this way, rich semantic information across tasks is encoded into dense vectors. 

- SAILER has powerful discriminative ability, even without any legal annotation data. It can distinguish legal cases with different charges accurately. 


SAILER_zh pre-training on Chinese criminal law legal case documents


If you find our work useful, please do not save your star and cite our work:

```
@misc{SAILER,
      title={SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval}, 
      author={Haitao Li and Qingyao Ai and Jia Chen and Qian Dong and Yueyue Wu and Yiqun Liu and Chong Chen and Qi Tian},
      year={2023},
      eprint={2304.11370},
      archivePrefix={arXiv},
      primaryClass={cs.IR}
}
```

{"license": "mit"}
transformers pytorch bert arxiv:2304.11370 license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #arxiv-2304.11370 #license-mit #endpoints_compatible #region-us 

SAILER is a structure-aware pre-trained language model. It is highlighted in the following three aspects: 

- SAILER fully utilizes the structural information contained in legal case documents and pays more attention to key legal elements, similar to how legal experts browse legal case documents. 

- SAILER employs an asymmetric encoder-decoder architecture to integrate several different pre-training objectives. In this way, rich semantic information across tasks is encoded into dense vectors. 

- SAILER has powerful discriminative ability, even without any legal annotation data. It can distinguish legal cases with different charges accurately. 


SAILER_zh pre-training on Chinese criminal law legal case documents


If you find our work useful, please do not save your star and cite our work:



TAGS
#transformers #pytorch #bert #arxiv-2304.11370 #license-mit #endpoints_compatible #region-us 
