Improve model card: Add project page and code link

This PR improves the model card by:

* Adding a link to https://medxpertqa.github.io.
* Adding a link to https://github.com/TsinghuaC3I/MedXpertQA to make it easier for people to find the code.

README.md

@@ -1,18 +1,11 @@
 ---
+base_model:
+- google/medgemma-4b-it
+library_name: transformers
 license: other
 license_name: health-ai-developer-foundations
 license_link: https://developers.google.com/health-ai-developer-foundations/terms
-library_name: transformers
 pipeline_tag: image-text-to-text
-extra_gated_heading: Access MedGemma on Hugging Face
-extra_gated_prompt: >-
-  To access MedGemma on Hugging Face, you're required to review and
-  agree to [Health AI Developer Foundation's terms of use](https://developers.google.com/health-ai-developer-foundations/terms).
-  To do this, please ensure you're logged in to Hugging Face and click below.
-  Requests are processed immediately.
-extra_gated_button_content: Acknowledge license
-base_model:
-- google/medgemma-4b-it
 tags:
 - medical
 - unsloth
@@ -22,7 +15,14 @@ tags:
 - pathology
 - ophthalmology
 - chest-x-ray
+extra_gated_heading: Access MedGemma on Hugging Face
+extra_gated_prompt: To access MedGemma on Hugging Face, you're required to review
+  and agree to [Health AI Developer Foundation's terms of use](https://developers.google.com/health-ai-developer-foundations/terms).
+  To do this, please ensure you're logged in to Hugging Face and click below. Requests
+  are processed immediately.
+extra_gated_button_content: Acknowledge license
 ---
+
 <div>
 <p style="margin-top: 0;margin-bottom: 0;">
     <em><a href="https://docs.unsloth.ai/basics/unsloth-dynamic-v2.0-gguf">Unsloth Dynamic 2.0</a> achieves superior accuracy & outperforms other leading quants.</em>
@@ -40,11 +40,13 @@ tags:
   </div>
 </div>
 
-
 # MedGemma model card
 
 **Model documentation:** [MedGemma](https://developers.google.com/health-ai-developer-foundations/medgemma)
 
+Project page: https://medxpertqa.github.io
+Code: https://github.com/TsinghuaC3I/MedXpertQA
+
 **Resources:**
 
 *   Model on Google Cloud Model Garden: [MedGemma](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/medgemma)
@@ -104,7 +106,6 @@ model locally on GPU. If you want to use the model at scale, we recommend that
 you create a production version using [Model
 Garden](https://cloud.google.com/model-garden).
 
-
 First, install the Transformers library. Gemma 3 is supported starting from
 transformers 4.50.0.
 
@@ -457,39 +458,10 @@ incorporated (described next).
     created by a research team led by Jason J. Lau, Soumya Gayen, Asma Ben
     Abacha, and Dina Demner-Fushman and their affiliated institutions (the US
     National Library of Medicine and National Institutes of Health)
-*   [MedExpQA](https://www.sciencedirect.com/science/article/pii/S0933365724001805):
-    This dataset was created by researchers at the HiTZ Center (Basque Center
-    for Language Technology and Artificial Intelligence).
-*   [MedXpertQA](https://huggingface.co/datasets/TsinghuaC3I/MedXpertQA): This
-    dataset was developed by researchers at Tsinghua University (Beijing, China)
-    and Shanghai Artificial Intelligence Laboratory (Shanghai, China).
-
-In addition to the public datasets listed above, MedGemma was also trained on
-de-identified datasets licensed for research or collected internally at Google
-from consented participants.
-
-*   Radiology dataset 1: De-identified dataset of different CT studies across
-    body parts from a US-based radiology outpatient diagnostic center network.
-*   Ophthalmology dataset 1: De-identified dataset of fundus images from
-    diabetic retinopathy screening.
-*   Dermatology dataset 1: De-identified dataset of teledermatology skin
-    condition images (both clinical and dermatoscopic) from Colombia.
-*   Dermatology dataset 2: De-identified dataset of skin cancer images (both
-    clinical and dermatoscopic) from Australia.
-*   Dermatology dataset 3: De-identified dataset of non-diseased skin images
-    from an internal data collection effort.
-*   Pathology dataset 1: De-identified dataset of histopathology H&E whole slide
-    images created in collaboration with an academic research hospital and
-    biobank in Europe. Comprises de-identified colon, prostate, and lymph nodes.
-*   Pathology dataset 2: De-identified dataset of lung histopathology H&E and
-    IHC whole slide images created by a commercial biobank in the United States.
-*   Pathology dataset 3: De-identified dataset of prostate and lymph node H&E
-    and IHC histopathology whole slide images created by a contract research
-    organization in the United States.
-*   Pathology dataset 4: De-identified dataset of histopathology, predominantly
-    H\&E whole slide images created in collaboration with a large, tertiary
-    teaching hospital in the United States. Comprises a diverse set of tissue
-    and stain types, predominantly H&E.
+*   [MedExpQA: Multilingual benchmarking of Large Language Models for
+    Medical Question
+    Answering](https://www.sciencedirect.com/science/article/pii/S0933365724001805)
+*   MedXpertQA: [arXiv:2501.18362v2](https://arxiv.org/abs/2501.18362)
 
 ### Data citation