Update README.md

README.md

@@ -189,7 +189,7 @@ embeddings = model.encode_text([text])
 ## Evaluation
 
 The model has been evaluated on chest X-ray report analysis tasks, particularly for:
-- Pleural effusion status determination
+- Text retrieval/encoder
 - Medical text similarity comparison
 - Clinical finding extraction
 
@@ -225,19 +225,20 @@ If you use this model in your research, please cite:
 
 ```bibtex
 @misc{llm2vec4cxr,
-  title={LLM2Vec4CXR: Fine-tuned Language Model for Chest X-ray Report Analysis},
-  author={[Your Name]},
-  year={2024},
+  title={LLM2Vec4CXR: Fine-tuned LLM for Chest X-ray Report Analysis},
+  author={Hanbin Ko},
+  year={2025},
   howpublished={\\url{https://huggingface.co/lukeingawesome/llm2vec4cxr}},
 }
 ```
 
+A preprint of this model will be released soon.
+
 ## Acknowledgments
 
 This model is built upon:
 - [LLM2Vec](https://github.com/McGill-NLP/llm2vec) - Framework for converting decoder-only LLMs into text encoders
 - [LLM2CLIP](https://github.com/microsoft/LLM2CLIP) - Microsoft's implementation for connecting LLMs with CLIP models
-- [microsoft/LLM2CLIP-Llama-3.2-1B-Instruct-CC-Finetuned](https://huggingface.co/microsoft/LLM2CLIP-Llama-3.2-1B-Instruct-CC-Finetuned) - Base model
 
 ## License