Update README.md

README.md

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 # Ara-EuroBERT: Arabic Semantic Text Embeddings
 
-<img src="https://i.ibb.co/d4svDscP/Clear-Familiar-situations-that-you-already-have-best-practices-for-4.png" width="120" align="left"/>
+<img src="https://i.ibb.co/d4svDscP/Clear-Familiar-situations-that-you-already-have-best-practices-for-4.png" width="150" align="left"/>
 
 Ara-EuroBERT is a [sentence-transformers](https://www.SBERT.net) model fine-tuned from [EuroBERT/EuroBERT-610m](https://huggingface.co/EuroBERT/EuroBERT-610m) specifically optimized for **Semantic Arabic text embeddings**.
 
@@ -115,11 +115,28 @@ It can be used for semantic textual similarity, semantic search, paraphrase mini
 
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-## Model Details
+## Model Details & Benchmark Performance
 
 <img src="https://cdn-uploads.huggingface.co/production/uploads/628f7a71dd993507cfcbe587/Kv78q7NmI3hhOXkRv30s9.png" width="1000" align="center"/>
 
+The benchmark results above demonstrate the significant performance improvements of AraEuroBERT models compared to standard EuroBERT models:
 
+- **STS17 Benchmark**: AraEuroBERT-610M achieves a score of 83, significantly outperforming the standard EuroBERT-610M (14) and even the much larger EuroBERT-2.1B (12).
+- **STS22.v2 Benchmark**: AraEuroBERT-210M scores 61, outperforming both the larger AraEuroBERT-610M (53) and all standard EuroBERT variants.
+
+These results highlight the effectiveness of our specialized fine-tuning for Arabic text embeddings, with even our smaller 210M parameter model demonstrating superior performance on Arabic semantic tasks.
+
+### Metrics
+
+#### Semantic Similarity
+
+* Datasets: `sts-dev-1152`, `sts-dev-960`, `sts-dev-768` and `sts-dev-512`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | sts-dev-1152 | sts-dev-960 | sts-dev-768 | sts-dev-512 |
+|:--------------------|:-------------|:------------|:------------|:------------|
+| pearson_cosine      | 0.8264       | 0.8259      | 0.8244      | 0.8238      |
+| **spearman_cosine** | **0.8307**   | **0.8302**  | **0.8293**  | **0.8293**  |
 
 
 ### Model Description