Improve dataset card: Add metadata, links, description, and sample usage

This PR significantly improves the dataset card by:
- Adding `task_categories: ['image-text-to-text']` and `license: cc-by-nc-4.0` to the metadata.
- Including links to the paper, project page.
- Adding the paper abstract for better context.
- Providing a "Sample Usage" snippet derived directly from the GitHub README, demonstrating how to generate CLIP embeddings for the data.
- Structuring the card with clear headings for readability and moving the existing BibTeX citations to a dedicated "Citation" section.

README.md

@@ -1,3 +1,51 @@
+---
+license: cc-by-nc-4.0
+task_categories:
+- image-text-to-text
+tags:
+- hateful-memes
+- multimodal
+- retrieval-augmented-generation
+- lmm
+---
+
+# Robust Adaptation of Large Multimodal Models for Retrieval Augmented Hateful Meme Detection Datasets
+
+This repository contains the datasets used in the paper [Robust Adaptation of Large Multimodal Models for Retrieval Augmented Hateful Meme Detection](https://huggingface.co/papers/2502.13061).
+
+[Project Page](https://rgclmm.github.io/) | [Code](https://github.com/JingbiaoMei/RGCL)
+
+## Abstract
+Recent advances in Large Multimodal Models (LMMs) have shown promise in hateful meme detection, but face challenges like sub-optimal performance and limited out-of-domain generalization. This work proposes a robust adaptation framework for hateful meme detection that enhances in-domain accuracy and cross-domain generalization while preserving the general vision-language capabilities of LMMs. Our approach achieves improved robustness under adversarial attacks compared to supervised fine-tuning (SFT) models and state-of-the-art performance on six meme classification datasets, outperforming larger agentic systems. Additionally, our method generates higher-quality rationales for explaining hateful content, enhancing model interpretability.
+
+## Dataset Preparation
+The datasets consist of image data and corresponding annotation data.
+
+### Image data
+Copy images into `./data/image/dataset_name/All` folder.
+For example: `./data/image/FB/All/12345.png`, `./data/image/HarMeme/All`, `./data/image/Propaganda/All`, etc..
+
+### Annotation data
+Copy `jsonl` annotation file into `./data/gt/dataset_name` folder.
+
+## Sample Usage
+To generate CLIP embeddings for the datasets prior to training, you can use the provided script as follows:
+
+```shell
+python3 src/utils/generate_CLIP_embedding_HF.py --dataset "FB"
+python3 src/utils/generate_CLIP_embedding_HF.py --dataset "HarMeme"
+```
+
+Similarly, to generate ALIGN embeddings:
+
+```shell
+python3 src/utils/generate_ALIGN_embedding_HF.py --dataset "FB"
+python3 src/utils/generate_ALIGN_embedding_HF.py --dataset "HarMeme"
+```
+
+## Citation
+If our work helped your research, please kindly cite our papers:
+
 ```
 @inproceedings{RGCL2024Mei,
     title = "Improving Hateful Meme Detection through Retrieval-Guided Contrastive Learning",