Legal-Embed-bge-base-en-v1.5

This repository hosts a fine-tuned version of BAAI/bge-base-en-v1.5 optimized for legal document (text) retrieval and Retrieval-Augmented Generation (RAG) tasks.

Model Details

• Base model: BAAI/bge-base-en-v1.5
• Dataset: axondendriteplus/legal-rag-embedding-dataset
• Task: Dense embedding learning for legal Q&A retrieval
• Framework: SentenceTransformers + HuggingFace Trainer
• Loss: MatryoshkaLossFunction (multi-resolution contrastive)

Evaluation (NDCG@10)

Metrics include cosine accuracy, MRR, MAP and NDCG.

Training Configuration

• Epochs: 4
• Batch size: 32
• Learning rate: 2e-5
• Data: 1,456 train / 162 test samples
• Hardware: CUDA GPU with FlashAttention

Findings

• Maximum improvement: 16.07%

• Fine-tuned 64D vs Baseline 768D: -15.03%

• Fine-tuned 128D vs Baseline 768D: -7.41%

• Storage reduction with 128D: 6× smaller

• Storage reduction with 64D: 12× smaller

• Baseline best score: 0.6105

• Fine-tuned best score: 0.6412

Usage

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("axondendriteplus/Legal-Embed-bge-base-en-v1.5")
embeddings = model.encode(["your legal text"])

Credits

Fine-tuning guide: https://www.philschmid.de/fine-tune-embedding-model-for-rag