Bge M3 Legal Retrieval

🚀 Quintu/bge-m3-legal_retrieval

This repository houses the Quintu/bge-m3-legal_retrieval, a fine - tuned variant of the bge-m3 model. It's optimized for legal document retrieval tasks, adept at handling the subtleties of legal language to retrieve relevant legal documents accurately and efficiently based on semantic similarity.

🚀 Quick Start

This repository contains the Quintu/bge-m3-legal_retrieval, a fine-tuned version of the bge-m3 model optimized for legal document retrieval tasks. The model is specifically designed to handle the nuances of legal language, enabling accurate and efficient retrieval of relevant legal documents based on semantic similarity.

✨ Features

• Legal Language Understanding: Optimized to understand legal terms, context, and phrases.
• Semantic Search: Retrieves documents based on meaning, not just keywords.
• High Precision Retrieval: Tailored for legal professionals and researchers.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

from sentence_transformers import SentenceTransformer

# Load the fine-tuned model
model_tuned = SentenceTransformer("Quintu/bge-m3-legal_retrieval")

# Example usage: Encode queries and documents
queries = ["What are the key legal precedents for intellectual property disputes?"]
documents = [
    "This document discusses key precedents in intellectual property law.",
    "This document covers legal principles in criminal law."
]

# Encode the queries and documents
query_embeddings = model_tuned.encode(queries)
document_embeddings = model_tuned.encode(documents)

# Compute similarity (example with cosine similarity)
from sklearn.metrics.pairwise import cosine_similarity
similarity_scores = cosine_similarity(query_embeddings, document_embeddings)

# Output similarity scores
print(similarity_scores)

📚 Documentation

Model Details

📄 License

This project is licensed under the apache-2.0 license.