Improve model card: Add metadata, paper, project, and code links

This PR significantly enhances the model card for `jaeunglee/resnet18-cifar10-unlearning` by:

- Adding `pipeline_tag: image-classification` and `library_name: pytorch` to the metadata, which improves the model's discoverability and categorization on the Hugging Face Hub.
- Adding prominent links to the paper ([Unlearning Comparator: A Visual Analytics System for Comparative Evaluation of Machine Unlearning Methods](https://huggingface.co/papers/2508.12730)), the project page ([https://gnueaj.github.io/Machine-Unlearning-Comparator/](https://gnueaj.github.io/Machine-Unlearning-Comparator/)), and the GitHub repository ([https://github.com/gnueaj/Machine-Unlearning-Comparator](https://github.com/gnueaj/Machine-Unlearning-Comparator)) at the top of the model card for easier access to related resources.

Please review and merge if these improvements align with the repository's goals.

README.md

@@ -4,17 +4,23 @@ tags:
 - machine-unlearning
 - unlearning
 - resnet18
+pipeline_tag: image-classification
+library_name: pytorch
 ---
 
 # Model Card for jaeunglee/resnet18-cifar10-unlearning
 
 This repository contains ResNet18 models retrained on the CIFAR-10 dataset with specific classes excluded during training. Each model is trained to study the impact of class exclusion on model performance and generalization.
 
+**Paper:** [Unlearning Comparator: A Visual Analytics System for Comparative Evaluation of Machine Unlearning Methods](https://huggingface.co/papers/2508.12730)
+**Project Page:** [https://gnueaj.github.io/Machine-Unlearning-Comparator/](https://gnueaj.github.io/Machine-Unlearning-Comparator/)
+**GitHub Repository:** [https://github.com/gnueaj/Machine-Unlearning-Comparator](https://github.com/gnueaj/Machine-Unlearning-Comparator)
+
 ---
 ## Evaluation
 
-- **Testing Data:** CIFAR-10 test set
-- **Metrics:** Top-1 accuracy
+-   **Testing Data:** CIFAR-10 test set
+-   **Metrics:** Top-1 accuracy
 
 ### Results
 
@@ -36,19 +42,19 @@ This repository contains ResNet18 models retrained on the CIFAR-10 dataset with
 
 ### Training Procedure
 
-- **Base Model:** ResNet18
-- **Dataset:** CIFAR-10
-- **Excluded Class:** Varies by model
-- **Loss Function:** CrossEntropyLoss
-- **Optimizer:** SGD with:
-  - Learning rate: `0.1`
-  - Momentum: `0.9`
-  - Weight decay: `5e-4`
-  - Nesterov: `True`
-- **Scheduler:** CosineAnnealingLR (T_max: `200`)
-- **Training Epochs:** `200`
-- **Batch Size:** `128`
-- **Hardware:** Single GPU
+-   **Base Model:** ResNet18
+-   **Dataset:** CIFAR-10
+-   **Excluded Class:** Varies by model
+-   **Loss Function:** CrossEntropyLoss
+-   **Optimizer:** SGD with:
+    -   Learning rate: `0.1`
+    -   Momentum: `0.9`
+    -   Weight decay: `5e-4`
+    -   Nesterov: `True`
+-   **Scheduler:** CosineAnnealingLR (T_max: `200`)
+-   **Training Epochs:** `200`
+-   **Batch Size:** `128`
+-   **Hardware:** Single GPU
 
 ### Notes on Training
 
@@ -59,21 +65,21 @@ The training recipe is adapted from the paper **"Benchopt: Reproducible, efficie
 
 The following transformations were applied to the CIFAR-10 dataset:
 
-- **Base Transformations (applied to both training and test sets):**
-  - Conversion to PyTorch tensors using `ToTensor()`.
-  - Normalization using mean `(0.4914, 0.4822, 0.4465)` and standard deviation `(0.2023, 0.1994, 0.2010)`.
+-   **Base Transformations (applied to both training and test sets):**
+    -   Conversion to PyTorch tensors using `ToTensor()`.
+    -   Normalization using mean `(0.4914, 0.4822, 0.4465)` and standard deviation `(0.2023, 0.1994, 0.2010)`.
 
-- **Training Set Augmentation (only for training data):**
-  - **RandomCrop(32, padding=4):** Randomly crops images with padding for spatial variation.
-  - **RandomHorizontalFlip():** Randomly flips images horizontally with a 50% probability.
+-   **Training Set Augmentation (only for training data):**
+    -   **RandomCrop(32, padding=4):** Randomly crops images with padding for spatial variation.
+    -   **RandomHorizontalFlip():** Randomly flips images horizontally with a 50% probability.
 
 These augmentations help improve the model's ability to generalize by introducing variability in the training data.
 
 ### Model Description
 
-- **Developed by:** Jaeung Lee
-- **Model type:** Image Classification
-- **License:** MIT
+-   **Developed by:** Jaeung Lee
+-   **Model type:** Image Classification
+-   **License:** MIT
 
 ### Related Work