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Updated Readme

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@@ -45,6 +45,17 @@ MedOCR-Vision is a vision-language model fine-tuned specifically for optical cha
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  - **Target Modules**: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj, out_proj, fc1, fc2, linear_1, linear_2
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  - **Precision**: Mixed (BF16/FP16)
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  ## Intended Uses
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  ### Primary Use Cases
@@ -282,16 +293,12 @@ _ = model.generate(
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  - Implement appropriate error handling and validation
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  - Consider the implications of automated medical document processing
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- ## Model Card Authors
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-
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- - **Naazim** (naazimsnh02)
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-
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  ## Citation
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  ```bibtex
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  @misc{medocr-vision-2025,
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  title={MedOCR-Vision: Medical Document OCR with PaddleOCR-VL},
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- author={Naazim},
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  year={2025},
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  publisher={HuggingFace},
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  howpublished={\url{https://huggingface.co/naazimsnh02/medocr-vision}}
@@ -300,7 +307,7 @@ _ = model.generate(
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  ## Additional Resources
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- - **Code Repository**: https://github.com/Naazimsnh02/medocr-vision
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  - **Training Dataset**: https://huggingface.co/datasets/naazimsnh02/medocr-vision-dataset
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  - **Training Notebook**: Available in the repository
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  - **ERNIE Challenge**: Submitted for ERNIE AI Developer Challenge
@@ -317,10 +324,6 @@ This model is released under the MIT License. Please refer to individual dataset
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  - **LLM Providers**: Nebius and Novita for data processing
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  - **PaddleOCR Team**: For the excellent OCR framework
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- ## Model Card Contact
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-
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- For questions or issues, please open an issue in the [GitHub repository](https://github.com/Naazimsnh02/medocr-vision).
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-
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  ---
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  **Model Version**: 1.0
 
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  - **Target Modules**: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj, out_proj, fc1, fc2, linear_1, linear_2
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  - **Precision**: Mixed (BF16/FP16)
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+ ## Performance Highlights
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+
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+ ### Model Improvements Over Base Model
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+
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+ Our fine-tuned model demonstrates significant improvements across multiple metrics:
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+
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+ - ✅ **Enhanced Information Extraction**: Captures more complete medical information including headers, test values, and reference ranges
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+ - ✅ **Better Document Understanding**: Improved coverage of document structure and context
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+ - ✅ **Medical Domain Specialization**: Superior performance on medical terminology and clinical data
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+ - ✅ **Comprehensive Coverage**: Extracts significantly more relevant content from medical documents
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+
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  ## Intended Uses
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  ### Primary Use Cases
 
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  - Implement appropriate error handling and validation
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  - Consider the implications of automated medical document processing
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  ## Citation
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  ```bibtex
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  @misc{medocr-vision-2025,
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  title={MedOCR-Vision: Medical Document OCR with PaddleOCR-VL},
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+ author={Syed Naazim Hussain},
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  year={2025},
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  publisher={HuggingFace},
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  howpublished={\url{https://huggingface.co/naazimsnh02/medocr-vision}}
 
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  ## Additional Resources
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+ - **Code Repository**: https://github.com/naazimsnh02/medocr-vision
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  - **Training Dataset**: https://huggingface.co/datasets/naazimsnh02/medocr-vision-dataset
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  - **Training Notebook**: Available in the repository
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  - **ERNIE Challenge**: Submitted for ERNIE AI Developer Challenge
 
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  - **LLM Providers**: Nebius and Novita for data processing
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  - **PaddleOCR Team**: For the excellent OCR framework
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  ---
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  **Model Version**: 1.0