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vector-dev-god commited on Mar 30

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@@ -95,7 +95,7 @@ The model excels at understanding natural language queries like:
 ### Evaluation
 The model's evaluation metrics are available on the [MTEB Leaderboard](https://huggingface.co/spaces/mteb/leaderboard)
 - The model is currently by far the best embedding model under 1B parameters size and very easy to run locally on a small GPU due to it's memory size
-- 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
 - We noticed the model does exceptionally well with legal and news retrieval and similarity task from the MTEB leaderboard

 ### Evaluation
 The model's evaluation metrics are available on the [MTEB Leaderboard](https://huggingface.co/spaces/mteb/leaderboard)
 - The model is currently by far the best embedding model under 1B parameters size and very easy to run locally on a small GPU due to it's memory size
+- 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. 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