This model is a fine-tuned version of Qwen/Qwen3-Embedding-0.6B 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

XiaSheng/Lore-Qwen3-embedding-0.6B

Lore-Qwen3-embedding-0.6B: Logic-ORiented Retriever Enhancement

LORE Method Overview

Key Improvements

XiaSheng/
Lore-Qwen3-embedding-0.6B