Update README.md

README.md

@@ -17,100 +17,90 @@ tags:
 - AI4Science
 - Nucleotide-Protein
 task_categories:
+- sequence-modeling
 - feature-extraction
 size_categories:
 - 10M<n<100M
 ---
 
-# Dataset Card for LucaVirus-OpenVirus-Gene
+# Dataset Card for LucaVirus-OpenVirus-Gene-Prot
 
 ## 1. Dataset Summary
 
-**LucaVirus-OpenVirus-Gene** is a large-scale genomic dataset consisting exclusively of viral nucleotide sequences. It is a specialized subset of the **OpenVirus** corpus, curated specifically for the pre-training of the **LucaVirus-Gene** foundation model.
+**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.
 
-By focusing purely on viral genomes, this dataset provides a high-density corpus of 10.4 million sequences, enabling models to capture the intricate evolutionary patterns, regulatory motifs, and genomic architectures of DNA and RNA viruses.
+The corpus comprises **15.7 million(10.4M nucleotide sequences and 5.2M protein sequences)** non-redundant viral sequences, providing a robust foundation for learning the complex language of viral evolution and the "central dogma" of viral biology.
 
 ## 2. Dataset Statistics
 
-The dataset focuses solely on nucleotide sequences (genomes, genes, and fragments):
+| Data Type | Count | `obj_type` Identifier |
+| :--- | :--- | :--- |
+| **Nucleotide (Genomes)** | 10.4 Million | `gene` |
+| **Protein (Amino Acids)** | 5.2 Million | `prot` |
+| **Total Sequences** | **15.7 Million** | - |
 
-| Feature | Count / Description |
-| :--- | :--- |
-| **Total Sequences** | 10.4 Million |
-| **Sequence Type** | Nucleotide (DNA/RNA) |
-| **`obj_type` Identifier** | `gene` (Exclusive) |
-| **Primary Use** | Pre-training for LucaVirus-Gene |
+## 3. Data Structure
 
-## 3. Data Structure & Format
-
-### 3.1 File Organization
-The dataset is provided as a compressed **`.tar`** archive. Upon extraction, the data is partitioned into three standard machine-learning subsets:
+The dataset is provided as a compressed **`.tar`** archive. Once extracted, the directory structure follows a standard machine-learning split:
 
 ```text
-LucaVirus-OpenVirus-Gene/
-├── train/          # Training set (primary corpus for genomic pre-training)
-├── dev/            # Validation set (for model selection and tuning)
-└── test/           # Test set (for final evaluation and benchmarking)
+LucaVirus-OpenVirus-Gene-Prot/
+├── train/          # Training set (primary corpus for pre-training)
+├── dev/            # Validation set (for hyperparameter tuning)
+└── test/           # Test set (for final evaluation)
 ```
 
-Each directory (`train`, `dev`, `test`) contains one or more **CSV files** with headers.
-
-### 3.2 CSV Schema
-All CSV files follow a consistent four-column schema:
+Each directory contains one or more **CSV files with headers**.
 
-| Column Name | Description | Details                                                                                                |
-| :--- | :--- |:-------------------------------------------------------------------------------------------------------|
-| **`obj_id`** | Sample ID | Unique identifier for each viral sequence.                                                             |
-| **`obj_type`** | Sequence Type | Set to `gene` for all entries in this dataset (Nucleotide).                                            |
-| **`obj_seq`** | Sequence Content | Raw nucleotide string (A, T(U), C, G, N).                                                              |
-| **`obj_label`** | Label | Metadata, taxonomic info, or functional labels associated with the genome (Annotation, Bio Knowledge). |
+### Data Schema
+Each CSV file includes the following columns:
 
+| 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 (AT(U)GCN for gene; Amino Acids for prot).                                             |
+| **`obj_label`** | Label | Metadata, taxonomic info, or functional labels associated with the genome and proteins (Annotation, Bio Knowledge) |
 
-## 4. Intended Use
+## 4. Dataset Intent
 
-- **Genomic Foundation Modeling**: Building models like **LucaVirus-Gene** that specialize in the "language of genomes."
-- **Viral Evolution Studies**: Analyzing conserved nucleotide patterns across divergent viral lineages.
-- **Regulatory Element Discovery**: Identifying viral gene boundaries, promoters, and other non-coding functional motifs.
+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.
 
-## 5. Usage Example
+## 5. Usage
 
-You can extract the archive and load the genomic data using the following Python snippet:
+### Loading with Python
+You can use standard Python libraries to process the data:
 
 ```python
-import tarfile
 import pandas as pd
+import tarfile
 import os
 
-# 1. Extract the genomic dataset
-with tarfile.open("LucaVirus-OpenVirus-Gene.tar.gz", "r:gz") as tar:
-    tar.extractall(path="./LucaVirus-OpenVirus-Gene")
-
-with tarfile.open("LucaVirus-OpenVirus-Gene/dataset.tar.gz", "r:gz") as tar:
-    tar.extractall(path="./LucaVirus-OpenVirus-Gene/dataset")
-    
-# 2. Load a sample from the training set
-train_path = "./LucaVirus-OpenVirus-Gene/dataset/v1.0/train"
-csv_files = [f for f in os.listdir(train_path) if f.endswith('.csv')]
-
-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)} genomic sequences.")
-    print(df[['obj_id', 'obj_seq']].head())
-```
+# 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/")
+
+with tarfile.open("./LucaVirus-OpenVirus-Gene-Prot/dataset.tar.gz", "r:gz") as tar:
+    tar.extractall(path="./LucaVirus-OpenVirus-Gene-Prot/dataset/")
 
-## 6. Related Resources
+# 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())
+```
 
-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)
+## 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).
-- 
+
+
 ## 7. Citation
 
-If you use this dataset in your research, please cite:
+If you use this dataset in your research, please cite the following:
 
 ```bibtex
 @article{lucavirus2025,
@@ -123,9 +113,10 @@ If you use this dataset in your research, please cite:
 ```
 
 ## 8. License
-
-This dataset is released under the **MIT License**.
+This dataset is released under the **Apache License 2.0**.
 
 ## 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.*
+
+