Update dataset card with paper link, metadata, and sample usage (#2)

- Update dataset card with paper link, metadata, and sample usage (17a8790dfc62cd46ff528e69330e64e5c1af71ee)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

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}
+}
+```