---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- lore
- logic-oriented-retrieval
license: mit
---

For more details please refer to our github repo: https://github.com/FlagOpen/FlagEmbedding

# Lore-Bge3: Logic-ORiented Retriever Enhancement for BGE-M3

This model is a fine-tuned version of [BAAI/bge-m3](https://arxiv.org/pdf/2402.03216.pdf) using the LORE (Logic-ORiented Retriever Enhancement) method. It significantly improves retrieval performance for complex logical expressions and queries.

## LORE Method Overview

LORE is a novel embedding enhancement method that improves retrieval performance through fine-grained contrastive learning:

- **Three-tier Contrastive Learning**: Fine-grained sample classification with P (Positive), N1 (Distractor), and N2 (Negative) samples
- **Dual Encoder Architecture**: Frozen document encoder M_d and trainable query encoder M_q
- **InfoNCE-based Loss**: Differentiated weights for hierarchical separation P ≻ N1 ≻ N2
- **Query Rewriting**: LLM-assisted dataset construction with discourse relations from Rhetorical Structure Theory (RST)
- **No External Dependencies**: Requires no external supervision, resources, or pre-retrieval analysis

## Key Improvements

- **Enhanced Logical Reasoning**: Improved ability to handle complex logical expressions in queries
- **Fine-grained Discrimination**: Better distinction between relevant content and distractors
- **Maintained Efficiency**: Preserves the computational efficiency of the original model