Add metadata, paper link, GitHub link, and sample usage

README.md

@@ -1,10 +1,17 @@
-## Original Data for ICLR 2026 PepBenchmark: A Standardized Benchmark for Peptide Machine Learning
+---
+task_categories:
+- text-classification
+tags:
+- biology
+- chemistry
+- peptide-ml
+---
 
-**Note:**  
-This repository provides the **raw dataset** collected for PepBenchmark. It is **not** the final dataset used in our paper.  
-For the processed, machine-learning-ready dataset, please refer to:
+# PepBenchData_raw
 
-- **PepBenchData (final dataset):** https://huggingface.co/datasets/jiahuizhang/PepBenchData
+This repository provides the **raw dataset** collected for **PepBenchmark: A Standardized Benchmark for Peptide Machine Learning**.
+
+[**Paper**](https://huggingface.co/papers/2604.10531) | [**GitHub**](https://github.com/ZGCI-AI4S-Pep/PepBenchmark/) | [**Processed Dataset (PepBenchData)**](https://huggingface.co/datasets/jiahuizhang/PepBenchData)
 
 ---
 
@@ -12,7 +19,7 @@ For the processed, machine-learning-ready dataset, please refer to:
 
 We provide two related but distinct datasets:
 
-### 1. PepBenchData_raw
+### 1. PepBenchData_raw (This Repository)
 
 - Contains **raw peptide sequences** collected from various databases and published literature.
 - Most samples are **experimentally validated**, ensuring high reliability.
@@ -41,14 +48,41 @@ As a result, **not all negative samples are experimentally validated**.
 
 ---
 
-## Data Sources
+## Sample Usage
 
-The raw dataset is aggregated from multiple sources, including:
+You can use the `pepbenchmark` library to manage and load these datasets. Below is an example of how to load a dataset and its official splits:
 
-- Public biological databases
-- Published scientific literature
+```python
+from pepbenchmark.dataset_manager.single_dataset import SinglePeptideDatasetManager
 
-![Data Sources](data_source.png)
+# Initialize the manager for a specific task (e.g., ACE inhibitory)
+manager = SinglePeptideDatasetManager(
+    "ace_inhibitory",
+    official_feature_names=["fasta", "label"],
+    dataset_dir="path/to/PepBenchData",
+)
+
+# Access sequences and labels
+sequences = manager.get_feature("fasta")
+labels = manager.get_feature("label")
+
+# Load official splits
+splits = manager.set_official_split_indices(
+    split_type="hybrid_split",
+    fold_seed=0
+)
+
+print(f"Train samples: {len(splits['train'])}")
+print(f"Test samples: {len(splits['test'])}")
+```
+
+---
+
+## Data Sources
+
+The raw dataset is aggregated from multiple sources, including public biological databases and published scientific literature.
+
+![Data Sources](https://huggingface.co/datasets/jiahuizhang/PepBenchData_raw/resolve/main/data_source.png)
 
 ---
 
@@ -57,22 +91,16 @@ The raw dataset is aggregated from multiple sources, including:
 ### Classification Tasks
 
 - `pos.csv`: Experimentally validated **positive samples**
-- `neg.csv`: Experimentally validated **negative samples**  
-  *(Note: For many datasets, `neg.csv` is not available.)*
-- `pos_filter_id_0.9_cov_f0.9.csv`:  
-  Positive samples after redundancy removal using **MMseqs2**  
-  (identity ≥ 0.9, coverage ≥ 0.9).  
-  This file is used to construct the final **PepBenchData**.
-
-![Classification Statistics](cls.png)
+- `neg.csv`: Experimentally validated **negative samples** (Note: For many datasets, `neg.csv` is not available.)
+- `pos_filter_id_0.9_cov_f0.9.csv`: Positive samples after redundancy removal using **MMseqs2** (identity ≥ 0.9, coverage ≥ 0.9).
 
----
+![Classification Statistics](https://huggingface.co/datasets/jiahuizhang/PepBenchData_raw/resolve/main/cls.png)
 
 ### Regression Tasks
 
-- Contains datasets with continuous labels for peptide-related properties.
+Contains datasets with continuous labels for peptide-related properties.
 
-![Regression Statistics](reg.png)
+![Regression Statistics](https://huggingface.co/datasets/jiahuizhang/PepBenchData_raw/resolve/main/reg.png)
 
 ---
 
@@ -82,4 +110,19 @@ The raw dataset is aggregated from multiple sources, including:
 dataset_name/
 ├── pos.csv
 ├── neg.csv (optional)
-├── pos_filter_id_0.9_cov_f0.9.csv
+└── pos_filter_id_0.9_cov_f0.9.csv
+```
+
+## Citation
+
+If you use PepBenchmark, please cite:
+
+```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}
+}
+```