Linq AI Research/Linq Embed Mistral Linq Embed Mistral Linq Embed Mistral has been developed by building upon the foundations of the E5 mistral 7b instruct and Mistral 7B v0.1 models. We focus on improving text retrieval using advanced data refinement methods, including sophisticated data crafting, data filtering, and negative mining guided by teacher models, which are highly tailored to each task, to improve the quality of the synthetic data generated by LLM. These methods are applied to both existing benchmark dataset and highly tailored synthetic dataset generated via LLMs. Our efforts primarily aim to create high quality triplet datasets (query, positive example, negative example), significantly improving text retrieval performance. Linq Embed Mistral performs well in the MTEB benchmarks (as of May 29, 2024). The model excels in retrieval tasks, ranking 1st among all models listed on the MTEB leaderboard with a performance score of 60.2 . This outstanding performance underscores its superior capability in enhancing search precision and reliability. The model achieves an average score of 68.2 across 56 datasets in the MTEB benchmarks, making it the highest ranking publicly acces…
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