Update README.md

README.md

@@ -1,3 +1,84 @@
 ---
-license: cc-by-nc-4.0
+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 roberta-large-cuad
+  
+# 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:** RoBERTa 
+- **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/roberta-large-cuad")
+ 
+model = AutoModelForQuestionAnswering.from_pretrained("mgigena/roberta-large-cuad")
+```
+</details>