File size: 2,104 Bytes
93a9116 f54def3 93a9116 f54def3 93a9116 f54def3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 |
---
language:
- en
license: cc-by-4.0
datasets:
- cuad
pipeline_tag: question-answering
tags:
- legal-contract-review
- roberta
- cuad
library_name: transformers
---
# Model Card for cuad-roberta-base
# Model Details
## Model Description
- **Developed by:** Hendrycks et al.
- **Model type:** Question Answering
- **Language(s) (NLP):** en
- **License:** cc-by-4.0
- **Related Models:**
- **Parent Model:** DeBERTa-v2
- **Resources for more information:**
- GitHub Repo: [TheAtticusProject](https://github.com/TheAtticusProject/cuad)
- Associated Paper: [CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review](https://arxiv.org/abs/2103.06268)
- Project website: [Contract Understanding Atticus Dataset (CUAD)](https://www.atticusprojectai.org/cuad)
# Uses
## Direct Use
This model can be used for the task of Question Answering on Legal Documents.
# Training Details
Read: [CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review](https://arxiv.org/abs/2103.06268)
for detailed information on training procedure, dataset preprocessing and evaluation.
## Training Data, Procedure, Preprocessing, etc.
See [CUAD dataset card](https://huggingface.co/datasets/cuad) for more information.
# Evaluation
## Testing Data, Factors & Metrics
### Testing Data
See [CUAD dataset card](https://huggingface.co/datasets/cuad) for more information.
### Software
Python, Transformers
# Citation
**BibTeX:**
```
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={NeurIPS},
year={2021}
}
```
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
```python
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("mgigena/cuad-deberta-v2-xlarge")
model = AutoModelForQuestionAnswering.from_pretrained("mgigena/cuad-deberta-v2-xlarge")
```
</details> |