Update README.md

README.md

@@ -33,7 +33,7 @@ If you use this model in your research, please cite the following paper:
 
 ## Key Features:
 - Fine-tuned to compute semantic similarity between disease names.
-- **Achieves an F1 score of 0.88** in distinguishing **protein-level experimentally (functional MTI, validated via western blot or reporter assay) validated MTIs** and **sequence-based predicted MTIs**.
+- **Achieves an F1 score of 0.88** in distinguishing **protein-level interaction MTIs (functional MTIs, validated via western blot or reporter assay)** and **sequence-based predicted MTIs**.
 - Built for applications in understanding miRNA-gene regulatory networks, disease diagnosis, treatment, and drug discovery.
 
 ## Full Model Architecture
@@ -57,7 +57,7 @@ Then you can use the model like this:
 # Load the pre-trained SBERT model
 from sentence_transformers import SentenceTransformer, util
 
-# Directly use following code to download model from hugging face or Replace 'your/path/to/Calcu_Disease_Similarity' with the actual path to run local model
+# Directly use the following code to download model from hugging face or Replace 'Baiming123/Calcu_Disease_Similarity' with the local path to run model
 model = SentenceTransformer("Baiming123/Calcu_Disease_Similarity")
 
 # Example usage