Update README.md

README.md

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 # WikiMedical_sent_biobert
 
-This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
 
-<!--- Describe your model here -->
+WikiMedical_sent_bert is based on the [https://huggingface.co/dmis-lab/biobert-base-cased-v1.2](dmis-lab/biobert-base-cased-v1.2) backbone and has been trained on the [WikiMedical_sentence_simialrity](https://huggingface.co/datasets/nuvocare/WikiMedical_sentence_similarity) dataset.
+
+The model is able to predict whether two texts are related to the same wikipedia page, with only medical topic. 
 
 ## Usage (Sentence-Transformers)
 
@@ -75,7 +77,10 @@ print(sentence_embeddings)
 
 ## Evaluation Results
 
-<!--- Describe how your model was evaluated -->
+The model is evaluated on the test set of [WikiMedical_sentence_simialrity](https://huggingface.co/datasets/nuvocare/WikiMedical_sentence_similarity). 
+It achieves a :
+- cosine spearman score of 0.87
+- cosine pearson score of 0.95
 
 For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=WikiMedical_sent_biobert)