Update README.md

README.md

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 # FremyCompany/BioLORD-STAMB2-v1
-This model was trained using BioLORD, a new pre-training strategy for producing **meaningful representations for clinical sentences and biomedical concepts**. State-of-the-art methodologies operate by maximizing the similarity in representation of names referring to the same concept, and preventing collapse through contrastive learning. However, because biomedical names are not always self-explanatory, it sometimes results in non-semantic representations. 
+This model was trained using BioLORD, a new pre-training strategy for producing meaningful representations for clinical sentences and biomedical concepts. 
 
-BioLORD overcomes this issue by **grounding its concept representations using definitions**, as well as short descriptions derived from a multi-relational knowledge graph consisting of biomedical ontologies. Thanks to this grounding, our model produces more semantic concept representations that match more closely the hierarchical structure of ontologies. BioLORD establishes a new state of the art for text similarity on both clinical sentences (MedSTS) and biomedical concepts (MayoSRS).
+State-of-the-art methodologies operate by maximizing the similarity in representation of names referring to the same concept, and preventing collapse through contrastive learning. However, because biomedical names are not always self-explanatory, it sometimes results in non-semantic representations. 
+
+BioLORD overcomes this issue by grounding its concept representations using definitions, as well as short descriptions derived from a multi-relational knowledge graph consisting of biomedical ontologies. Thanks to this grounding, our model produces more semantic concept representations that match more closely the hierarchical structure of ontologies. BioLORD establishes a new state of the art for text similarity on both clinical sentences (MedSTS) and biomedical concepts (MayoSRS).
 
 This model is based on [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) and was further finetuned on the [BioLORD-Dataset](https://huggingface.co/datasets/FremyCompany/BioLORD-Dataset).
 <img width="640" src="https://s3.amazonaws.com/moonup/production/uploads/1665568401241-5f04e8865d08220171a0ad3f.png" />