
Dataset and Training Script offered by the Atticus Project MAUD.

Trained on AWS Sagemaker with 4 A10 GPUs.

Model owned by King & Wood Mallesons Law Firm AI LAB.

Project Member:
- Wuyue(Tracy) Wang @ King & Wood Mallesons
- Anbei Zhao @ Amazon Web Services
- Xiaodong Guo @ Amazon Web Services
- Xiuyu Wu @ Peking University

Reference:
```
@misc{wang2023maud,
      title={MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding}, 
      author={Steven H. Wang and Antoine Scardigli and Leonard Tang and Wei Chen and Dimitry Levkin and Anya Chen and Spencer Ball and Thomas Woodside and Oliver Zhang and Dan Hendrycks},
      year={2023},
      eprint={2301.00876},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```
{"language": ["en"], "license": "mit", "tags": ["legal"], "pipeline_tag": "text-classification"}
transformers pytorch roberta question-answering legal text-classification en arxiv:2301.00876 license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #question-answering #legal #text-classification #en #arxiv-2301.00876 #license-mit #endpoints_compatible #region-us 


Dataset and Training Script offered by the Atticus Project MAUD.

Trained on AWS Sagemaker with 4 A10 GPUs.

Model owned by King & Wood Mallesons Law Firm AI LAB.

Project Member:
- Wuyue(Tracy) Wang @ King & Wood Mallesons
- Anbei Zhao @ Amazon Web Services
- Xiaodong Guo @ Amazon Web Services
- Xiuyu Wu @ Peking University

Reference:

TAGS
#transformers #pytorch #roberta #question-answering #legal #text-classification #en #arxiv-2301.00876 #license-mit #endpoints_compatible #region-us 

TAGS
#transformers #pytorch #roberta #question-answering #legal #text-classification #en #arxiv-2301.00876 #license-mit #endpoints_compatible #region-us 
