Create README.md

README.md

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+---
+license: mit
+datasets:
+- vector-institute/open-pmc
+metrics:
+- accuracy
+- f1
+- recall
+---
+<div align="center">
+    <img src="https://github.com/VectorInstitute/pmc-data-extraction/blob/0a969136344a07267bb558d01f3fe76b36b93e1a/media/open-pmc-pipeline.png?raw=true" 
+     alt="Open-PMC Pipeline" 
+     width="1000" />
+</div>
+
+<p align="center">
+  <strong>Arxiv:</strong> <a href="http://arxiv.org/abs/2503.14377" target="_blank">Arxiv</a> 
+  &nbsp;&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;&nbsp;
+ <strong>Code:</strong> <a href="https://github.com/VectorInstitute/pmc-data-extraction" target="_blank">Open-PMC Github</a>
+  &nbsp;&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;&nbsp;
+ <strong>Dataset:</strong> <a href="https://huggingface.co/datasets/vector-institute/open-pmc" target="_blank">Hugging Face</a>
+ </p>
+
+
+## Model Overview
+
+This model is a checkpoint trained on the **Open-PMC** dataset. It utilizes a **Vision Transformer (ViT-base16)** as the backbone for visual feature extraction and **PubMedBERT** for processing text data. The model is trained using **Contrastive Learning** with the **vanilla Info-NCE loss** to learn meaningful representations across different modalities.
+
+## Model Architecture
+
+- **Vision Backbone**: ViT-B/16 (Pretrained on ImageNet)
+- **Text Backbone**: PubMedBERT (Pretrained on PubMedCentral Abstracts)
+- **Training Objective**: Contrastive Learning with **Info-NCE Loss**
+
+## Training Framework
+
+The model was trained using the **mmlearn** framework, which is designed for multimodal learning. You can find more information and access the framework [here](https://github.com/vectorInstitute/mmlearn).
+
+## How to Use
+
+Please visit out GitHub for information on how to run benchmarking using this checkpoint