README.md

---
license: apache-2.0
language:
- en
base_model:
- mistralai/Mistral-7B-v0.1
tags:
- legal
---

# reglab-rrc/mistral-rrc

**Paper:** [AI for Scaling Legal Reform: Mapping and Redacting Racial Covenants in Santa Clara County]()


## Usage

Here is an example of how to use the model to find racial covenants in a page of text:

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import re

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("reglab/mistral-rrc")
model = AutoModelForCausalLM.from_pretrained("reglab/mistral-rrc")

def format_prompt(document):
    return f"""### Instruction:
Determine whether the property deed contains a racial covenant. A racial covenant is a clause in a document that \
restricts who can reside, own, or occupy a property on the basis of race, ethnicity, national origin, or religion. \
Answer "Yes" or "No". If "Yes", provide the exact text of the relevant passage and then a quotation of the passage \
with spelling and formatting errors fixed.

### Input:
{document}

### Response:"""

def parse_output(output):
    answer_match = re.search(r"\[ANSWER\](.*?)\[/ANSWER\]", output, re.DOTALL)
    raw_passage_match = re.search(r"\[RAW PASSAGE\](.*?)\[/RAW PASSAGE\]", output, re.DOTALL)
    quotation_match = re.search(r"\[CORRECTED QUOTATION\](.*?)\[/CORRECTED QUOTATION\]", output, re.DOTALL)
    
    answer = answer_match.group(1).strip() if answer_match else None
    raw_passage = raw_passage_match.group(1).strip() if raw_passage_match else None
    quotation = quotation_match.group(1).strip() if quotation_match else None
    
    return {
        "answer": answer == "Yes",
        "raw_passage": raw_passage,
        "quotation": quotation
    }

# Example usage
document = "Your property deed text here..."
prompt = format_prompt(document)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=512)
result = tokenizer.decode(outputs[0])
parsed_result = parse_output(result)

print(parsed_result)
```

The model was trained with the given input and output formats, so be sure to use them
when performing inference.

## Intended Use

This model is designed to detect racial covenants in property deeds.

## Training Data


## Performance


## Limitations


## Ethical Considerations



## Citation

```
@article{suranisuzgun2024,
  title={AI for Scaling Legal Reform: Mapping and Redacting Racial Covenants in Santa Clara County},
  author={Surani, Faiz and Suzgun, Mirac and Raman, Vyoma and Manning, Christopher D. and Henderson, Peter and Ho, Daniel E.},
  journal={},
  year={2024}
}
```