Add metadata and links to paper and code

This PR adds metadata to the dataset card, including the task category and license. It also adds links to the paper and the Github repository. The datasets themselves are hosted elsewhere (links are in the README).

README.md

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+---
+task_categories:
+- image-segmentation
+license: mit
+tags:
+- parameter-efficient-fine-tuning
+- peft
+- segment-anything
+- sam
+- medical-image-segmentation
+---
+
+This repository contains the code for SALT: Parameter-Efficient Fine-Tuning via Singular Value Adaptation with Low-Rank Transformation. SALT is a method for adapting large-scale foundation models, particularly the Segment Anything Model (SAM), to domain-specific tasks, such as medical image segmentation, with high parameter efficiency.
+
+
+Paper: [SALT: Parameter-Efficient Fine-Tuning via Singular Value Adaptation with Low-Rank Transformation](https://huggingface.co/papers/2503.16055)
+
+Code: https://github.com/YourUsername/SALT.git
+
+
+The following datasets, used in the experiments, are available on Hugging Face:
+
+- **ROSE:** (https://huggingface.co/datasets/pythn/ROSE)
+- **ARCADE:** (https://huggingface.co/datasets/pythn/ARCADE)
+- **DRIVE:** (https://huggingface.co/datasets/pythn/drive)
+- **DIAS:** (https://huggingface.co/datasets/pythn/DIAS)
+- **Xray-Angio:** (https://huggingface.co/datasets/pythn/DB)