Update README.md

README.md

@@ -201,6 +201,20 @@ and ultimately lead to embeddings that are more useful for your use-case.
 - In cases where the two texts to compare are different in nature (e.g. query and document) – also called “asymmetric” – we suggest to first add an instruction to query texts only. Again, try and ideally evaluate the model in this setting. Then, if your aim is to further boost performance, we suggest that you add instructions to document texts as well where [X] and [Y] are flipped accordingly.
 
 
+## Evaluation
+
+Pharia-1-Embedding-4608-control has not been optimized for [MTEB](https://github.com/embeddings-benchmark/mteb) (a generic benchmark), 
+and naturally would be expected to underperform on it as we optimize instead for real-world usage and multilinguality. 
+Nonetheless, for comparability we share results on a subset of tasks of the 
+English MTEB benchmark. The subset contains tasks from all task types (classification, summarization, etc.) of 
+the full benchmark and is therefore roughly representative of it.
+
+#### MTEB – English
+For this evaluation we use task-specific instructions from [MEDI2](https://huggingface.co/datasets/GritLM/MEDI2).
+
+|Model Name|ArguAna|AskUbuntuDupQuestions|BIOSSES|Banking77Classification|EmotionClassification|MedrxivClusteringS2S|NFCorpus|STS17|STSBenchmark|SciFact|SummEval|TwitterSemEval2015|Average|
+|--|--|--|--|--|--|--|--|--|--|--|--|--|--|
+|Pharia-1-Embedding-4608-control|51.09|61.71|84.56|86.37|51.77|34.29|37.82|89.56|87.08|69.7|30.95|70.97|**62.99**|