Linq-Embed-Mistral
| Property | Value |
|---|---|
| Model Base | E5-mistral-7b-instruct, Mistral-7B-v0.1 |
| Primary Purpose | Text Embedding & Retrieval |
| MTEB Retrieval Score | 60.2 (Rank #1) |
| Model URL | https://huggingface.co/Linq-AI-Research/Linq-Embed-Mistral |
What is Linq-Embed-Mistral?
Linq-Embed-Mistral is a state-of-the-art text embedding model developed by Linq AI Research, building upon the E5-mistral-7b-instruct and Mistral-7B-v0.1 foundations. The model excels in text retrieval tasks, achieving the top position on the MTEB leaderboard with a remarkable performance score of 60.2 across retrieval tasks and an impressive 68.2 average across 56 datasets.
Implementation Details
The model employs sophisticated data refinement methods including advanced data crafting, filtering, and negative mining guided by task-specific teacher models. It focuses on creating high-quality triplet datasets (query, positive example, negative example) to enhance retrieval performance.
- Advanced data refinement and filtering techniques
- Task-specific teacher model guidance
- Sophisticated triplet dataset creation
- Integration with both Sentence Transformers and Transformers frameworks
Core Capabilities
- Superior text retrieval performance (60.2 MTEB score)
- Efficient semantic search functionality
- Multi-language support (demonstrated in Korean and English examples)
- Flexible implementation options through different frameworks
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its superior retrieval performance, achieved through sophisticated data refinement methods and task-specific optimization. It currently holds the top position for retrieval tasks on the MTEB leaderboard among publicly available models.
Q: What are the recommended use cases?
The model is particularly well-suited for text retrieval applications, semantic search systems, and document similarity tasks. It can be effectively used in both academic and commercial contexts where high-precision text matching is required.
