MOD: README.md

README.md

@@ -9,7 +9,7 @@ license: cc-by-nc-4.0
 
 **Linq-Embed-Mistral**
 
-Linq-Embed-Mistral has been developed by building upon the foundations of the [E5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct) and [Mistral-7B-v0.1](https://huggingface.co/mistralai/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 techniques. These methods are applied to both existing benchmark datasets and highly tailored synthetic datasets generated via LLMs. To enhance the quality of the synthetic data, we employ extensive prompt engineering and guidance from teacher models, ensuring these methods are specifically tailored to each task. Our efforts primarily aim to create high-quality triplet datasets (query, positive example, negative example), significantly improving text retrieval performance.
+Linq-Embed-Mistral has been developed by building upon the foundations of the [E5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct) and [Mistral-7B-v0.1](https://huggingface.co/mistralai/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. The model excels in retrieval tasks, ranking <ins>**`1st`**</ins> among all models listed on the MTEB leaderboard with a performance score of <ins>**`xxx`**</ins>. This outstanding performance underscores its superior capability in enhancing search precision and reliability. The model achieves an average score of <ins>**`yyy`**</ins> across 56 datasets in the MTEB benchmarks, making it the highest-ranking publicly accessible model and third overall.
 
@@ -20,7 +20,7 @@ This project is for research purposes only. Third-party datasets may be subject
 - [Mistral](https://arxiv.org/abs/2310.06825)
 - [E5-mistral-7b-instruct](https://arxiv.org/pdf/2401.00368.pdf)
 
-For more details, refer to [this blog post](https://getlinq.com/blog/linq-embed-mistral/).
+For more details, refer to [this blog post](https://getlinq.com/blog/linq-embed-mistral/) and [this report()].
 
 ## How to use