Update dataset card with paper link, metadata, and sample usage

Hi! I'm Niels, part of the community science team at Hugging Face.

I've noticed this dataset is part of the "PepBenchmark" project. This PR aims to improve the dataset card by:
1. Linking it to the corresponding paper on the Hugging Face Hub (https://huggingface.co/papers/2604.10531).
2. Adding the `text-classification` task category and relevant tags to the metadata.
3. Including a link to the official GitHub repository.
4. Providing a sample usage snippet found in the GitHub documentation to help users load and interact with the data using the `pepbenchmark` library.

README.md

@@ -1,2 +1,59 @@
-This dataset is released for the ICLR 2026 paper “PepBenchmark: A Standardized Benchmark for Peptide Machine Learning.”
-Additional details are available at: https://github.com/ZGCI-AI4S-Pep/PepBenchmark
+---
+task_categories:
+- text-classification
+tags:
+- biology
+- peptide
+- drug-discovery
+---
+
+This dataset is part of **PepBenchmark**, a standardized benchmark for peptide machine learning introduced in the paper [PepBenchmark: A Standardized Benchmark for Peptide Machine Learning](https://huggingface.co/papers/2604.10531).
+
+PepBenchmark unifies datasets, preprocessing, and evaluation protocols for peptide drug discovery. It comprises three components:
+- **PepBenchData**: A well-curated collection of 29 canonical-peptide and 6 non-canonical-peptide datasets across 7 groups.
+- **PepBenchPipeline**: A standardized preprocessing pipeline ensuring consistent data cleaning, construction, and splitting.
+- **PepBenchLeaderboard**: A unified evaluation protocol and leaderboard with strong baselines across major methodological families.
+
+- **GitHub Repository**: [https://github.com/ZGCI-AI4S-Pep/PepBenchmark](https://github.com/ZGCI-AI4S-Pep/PepBenchmark)
+- **Paper**: [https://huggingface.co/papers/2604.10531](https://huggingface.co/papers/2604.10531)
+
+## Sample Usage
+
+You can use the `pepbenchmark` library to load and manage the datasets:
+
+```python
+from pepbenchmark.dataset_manager.single_dataset import SinglePeptideDatasetManager
+
+# Initialize the manager for a specific dataset
+manager = SinglePeptideDatasetManager(
+    "ace_inhibitory",
+    official_feature_names=["fasta", "label"],
+    dataset_dir="../PepBenchData/PepBenchData-50",
+)
+
+# Access features
+sequences = manager.get_feature("fasta")
+labels = manager.get_feature("label")
+
+# Set data splits
+splits = manager.set_official_split_indices(
+    split_type="hybrid_split",
+    fold_seed=0
+)
+
+print(f"Train samples: {len(splits['train'])}")
+print(f"Validation samples: {len(splits['valid'])}")
+print(f"Test samples: {len(splits['test'])}")
+```
+
+## Citation
+
+```bibtex
+@inproceedings{zhang2026pepbenchmark,
+  title={PepBenchmark: A Standardized Benchmark for Peptide Machine Learning},
+  author={Zhang, Jiahui and Wang, Rouyi and Zhou, Kuangqi and Xiao, Tianshu and Zhu, Lingyan and Min, Yaosen and Wang, Yang},
+  booktitle={International Conference on Learning Representations (ICLR)},
+  year={2026},
+  url={https://openreview.net/forum?id=NskQgtSdll}
+}
+```