Update README.md

README.md

@@ -12,93 +12,104 @@ tags:
 - Protein
 - Foundation Model
 - LucaVirus
+- LucaVirus-Prot
 - AI4Bio
 - AI4Science
 - Nucleotide-Protein
 task_categories:
-  - feature-extraction
+- feature-extraction
 size_categories:
-- 10M<n<100M
+- 1M<n<10M
 ---
 
-# Dataset Card for LucaVirus-OpenVirus-Gene-Prot
+# Dataset Card for LucaVirus-OpenVirus-Prot
 
 ## 1. Dataset Summary
 
-**LucaVirus-OpenVirus-Gene-Prot** is the complete, multi-modal **OpenVirus** corpus, curated for the pre-training of the **LucaVirus** biological foundation model. This dataset provides a massive-scale collection of viral sequences, bridging the gap between genomic (nucleotide) and proteomic (protein) data.
+**LucaVirus-OpenVirus-Prot** is a large-scale proteomics dataset consisting exclusively of viral protein sequences. It is a specialized subset of the **OpenVirus** corpus, specifically curated for the pre-training of the **LucaVirus-Prot** (or LucaVirus-Protein) foundation model.
 
-The corpus comprises **15.7 million** non-redundant viral sequences, providing a robust foundation for learning the complex language of viral evolution and the "central dogma" of viral biology.
+This dataset provides a comprehensive representation of the viral proteosphere, comprising **5.2 million** protein sequences. It is designed to enable biological models to learn the "language of proteins," capturing structural motifs, functional domains, and evolutionary signatures across a vast array of viral families.
 
 ## 2. Dataset Statistics
 
-| Data Type | Count | `obj_type` Identifier |
-| :--- | :--- | :--- |
-| **Nucleotide (Genomes)** | 10.4 Million | `gene` |
-| **Protein (Amino Acids)** | 5.2 Million | `prot` |
-| **Total Sequences** | **15.7 Million** | - |
+The dataset focuses strictly on amino acid sequences:
 
-## 3. Data Structure
+| Feature | Count / Description |
+| :--- | :--- |
+| **Total Sequences** | 5.2 Million |
+| **Sequence Type** | Protein (Amino Acids) |
+| **`obj_type` Identifier** | `prot` (Exclusive) |
+| **Primary Use** | Pre-training for LucaVirus-Prot |
 
-The dataset is provided as a compressed **`.tar`** archive. Once extracted, the directory structure follows a standard machine-learning split:
+## 3. Data Structure & Format
+
+### 3.1 File Organization
+The dataset is distributed as a compressed **`.tar`** archive. Upon extraction, the data is partitioned into three standard machine-learning subsets:
 
 ```text
-LucaVirus-OpenVirus-Gene-Prot/
-├── train/          # Training set (primary corpus for pre-training)
-├── dev/            # Validation set (for hyperparameter tuning)
-└── test/           # Test set (for final evaluation)
+LucaVirus-OpenVirus-Prot/
+├── train/          # Training set (primary corpus for protein pre-training)
+├── dev/            # Validation set (for model selection and tuning)
+└── test/           # Test set (for final evaluation and benchmarking)
 ```
 
-Each directory contains one or more **CSV files with headers**.
+Each directory (`train`, `dev`, `test`) contains one or more **CSV files** with headers.
 
-### Data Schema
-Each CSV file includes the following columns:
+### 3.2 CSV Schema
+All CSV files follow a consistent four-column schema:
 
 | Column Name | Description | Details                                                                                                |
 | :--- | :--- |:-------------------------------------------------------------------------------------------------------|
-| **`obj_id`** | Sample ID | Unique identifier for the sample.                                                                                  |
-| **`obj_type`** | Sequence Type | Sequence modality: `gene` (nucleotide) or `prot` (protein).                                                        |
-| **`obj_seq`** | Sequence Content | The raw biological sequence (ATGC for gene; Amino Acids for prot).                                                 |
-| **`obj_label`** | Label | Metadata, taxonomic info, or functional labels associated with the genome and proteins (Annotation, Bio Knowledge) |
+| **`obj_id`** | Sample ID | Unique identifier for each protein sequence.                                                           |
+| **`obj_type`** | Sequence Type | Set to `prot` for all entries in this dataset.                                                         |
+| **`obj_seq`** | Sequence Content | Raw amino acid string (standard IUPAC codes).                                                          |
+| **`obj_label`** | Annotations | Metadata, taxonomic info, or functional labels associated with the protein (Annotation, Bio Knowledge) |
 
-## 4. Dataset Intent
+## 4. Intended Use
 
-This dataset is specifically designed for:
-- **Foundation Model Pre-training**: Building models that can process both DNA/RNA and Protein sequences.
-- **Cross-modal Learning**: Understanding the translation and structural relationships within viral biology.
-- **Viral Research**: A large-scale benchmark for viral sequence classification, functional annotation, and mutation analysis.
+- **Protein Foundation Modeling**: Building models like **LucaVirus-Prot** that specialize in understanding protein sequences and their biophysical properties.
+- **Functional Annotation**: Developing tools to predict viral protein functions, domains, and active sites.
+- **Virus-Host Interaction**: Studying how viral proteins interact with host cellular machinery based on sequence patterns.
 
-## 5. Usage
+## 5. Usage Example
 
-### Loading with Python
-You can use standard Python libraries to process the data:
+You can extract the archive and load the protein data using the following Python snippet:
 
 ```python
-import pandas as pd
 import tarfile
+import pandas as pd
 import os
 
-# Example: Extracting and reading a file
-with tarfile.open("LucaVirus-OpenVirus-Gene-Prot.tar.gz", "r:gz") as tar:
-    tar.extractall(path="./LucaVirus-OpenVirus-Gene-Prot/")
+# 1. Extract the protein dataset
+with tarfile.open("LucaVirus-OpenVirus-Prot.tar.gz", "r:gz") as tar:
+    tar.extractall(path="./LucaVirus-OpenVirus-Prot")
+
+with tarfile.open("LucaVirus-OpenVirus-Prot/dataset.tar.gz", "r:gz") as tar:
+    tar.extractall(path="./LucaVirus-OpenVirus-Prot/dataset")
 
-with tarfile.open("./LucaVirus-OpenVirus-Gene-Prot/dataset.tar.gz", "r:gz") as tar:
-    tar.extractall(path="./LucaVirus-OpenVirus-Gene-Prot/dataset/")
+# 2. Load a sample from the training set
+train_path = "./LucaVirus-OpenVirus-Prot/dataset/v1.0/train"
+csv_files = [f for f in os.listdir(train_path) if f.endswith('.csv')]
 
-# Read a specific CSV from the train set
-df = pd.read_csv("../LucaVirus-OpenVirus-Gene-Prot/dataset/v1.0/train/3072_train_1.csv")
-print(df.head())
+if csv_files:
+    # Load the first CSV file
+    df = pd.read_csv(os.path.join(train_path, csv_files[0]))
+    
+    # Verify the sequence type
+    print(f"Loaded {len(df)} protein sequences.")
+    print(df[['obj_id', 'obj_seq', 'obj_label']].head())
 ```
 
-## 6. Pre-training with LucaVirus
-This dataset is the primary source for the **LucaVirus** model family.   
-- **Full Corpus (Gene + Prot)**: [LucaVirus-OpenVirus-Gene](https://huggingface.co/datasets/LucaGroup/LucaVirus-OpenVirus-Gene)
-- **Protein Subset**: [LucaVirus-OpenVirus-Prot](https://huggingface.co/datasets/LucaGroup/LucaVirus-OpenVirus-Prot)
-- **Models**: Visit the [LucaVirus Collection](https://huggingface.co/collections/LucaGroup/lucavirus).
+## 6. Related Resources
 
+This dataset is a core component of the **LucaGroup** biological modeling ecosystem.
+- **Full Corpus (Gene + Prot)**: [LucaVirus-OpenVirus-Gene-Prot](https://huggingface.co/datasets/LucaGroup/LucaVirus-OpenVirus-Gene-Prot)
+- **Genomic Subset**: [LucaVirus-OpenVirus-Gene](https://huggingface.co/datasets/LucaGroup/LucaVirus-OpenVirus-Gene)
+- **Models**: Visit the [LucaVirus Collection](https://huggingface.co/collections/LucaGroup/lucavirus).
 
 ## 7. Citation
 
-If you use this dataset in your research, please cite the following:
+If you use this dataset in your research, please cite:
 
 ```bibtex
 @article{lucavirus2025,
@@ -111,10 +122,10 @@ If you use this dataset in your research, please cite the following:
 ```
 
 ## 8. License
-This dataset is released under the **Apache License 2.0**.
+
+This dataset is released under the **MIT License**.
 
 ## 9. Contact
 
 *For further information, please visit the [LucaGroup GitHub](https://github.com/LucaOne), email to: [sanyuan.hy@alibaba-inc.com/heyongcsat@gmail.com], or contact the team via the Hugging Face organization profile.*
 
-