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conversational-search

semantic-search

text-embeddings-inference

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vector-dev-god commited on Jul 31

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@@ -98,6 +98,8 @@ The model's evaluation metrics are available on the [MTEB Leaderboard](https://h
 - The model also is No 1. by a far margin on the [SemRel24STS](https://huggingface.co/datasets/SemRel/SemRel2024) task with an accuracy of 81.12% beating Google Gemini embedding model (second place) 73.14% (as at 30th March 2025). SemRel24STS evaluates the ability of systems to measure the semantic relatedness between two sentences over 14 different languages.
 - We noticed the model does exceptionally well with legal and news retrieval and similarity task from the MTEB leaderboard
 ### Strengths
 - Excellent at understanding conversational and natural language queries

 - The model also is No 1. by a far margin on the [SemRel24STS](https://huggingface.co/datasets/SemRel/SemRel2024) task with an accuracy of 81.12% beating Google Gemini embedding model (second place) 73.14% (as at 30th March 2025). SemRel24STS evaluates the ability of systems to measure the semantic relatedness between two sentences over 14 different languages.
 - We noticed the model does exceptionally well with legal and news retrieval and similarity task from the MTEB leaderboard
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/67c7a5db29514343cec86a60/ewqPXTiFoUACicUcj-Vlz.png)
 ### Strengths
 - Excellent at understanding conversational and natural language queries