Update README.md

README.md

@@ -126,7 +126,7 @@ similarity = F.cosine_similarity(embeddings[0:1], embeddings[1:2])
 print(f"Similarity: {similarity.item():.4f}")
 ```
 
-## Evaluation
+<!-- ## Evaluation
 
 The model has been evaluated on comprehensive biomedical benchmarks including:
 
@@ -136,17 +136,17 @@ The model has been evaluated on comprehensive biomedical benchmarks including:
 - **Semantic Search**: Retrieval quality on biomedical text corpora
 
 For detailed evaluation results, see the [SODA-VEC benchmark notebooks](https://github.com/EMBO/soda-vec).
-
+ -->
 ## Intended Use
 
 This model is designed for:
 
 - **Biomedical Semantic Search**: Finding relevant papers, abstracts, or text passages
 - **Scientific Text Similarity**: Computing similarity between biomedical texts
-- **Information Retrieval**: Building search systems for scientific literature
+<!-- - **Information Retrieval**: Building search systems for scientific literature
 - **Downstream Tasks**: As a base for fine-tuning on specific biomedical tasks
 - **Research Applications**: Academic and research use in life sciences
-
+ -->
 ## Limitations
 
 - **Domain Specificity**: Optimized for biomedical and life sciences text; may not perform as well on general domain text
@@ -163,7 +163,7 @@ If you use this model, please cite:
   title = {SODA-VEC: Scientific Open Domain Adaptation for Vector Embeddings},
   author = {EMBO},
   year = {2024},
-  url = {https://github.com/EMBO/soda-vec}
+  url = {https://github.com/source-data/soda-vec}
 }
 ```