lucavirus 1.1.1

LucaVirus: LucaVirus Model compatible with HuggingFace Transformers

LucaVirus

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LucaVirus: a Unified Nucleotide-Protein Language Model for Virus

LucaVirus is a Unified Nucleotide-Protein Language Model for predicting the Evolutionary and Functional Landscapes of Viruses. This repository provides the refactored implementation that is fully compatible with the Hugging Face transformers ecosystem, supporting seamless integration for various viral downstream bioinformatics tasks.

Key Features

• Hugging Face Native: Full support for AutoModel, AutoModelForMaskedLM, AutoModelForSequenceClassification, AutoModelForTokenClassification, AutoConfig, and AutoTokenizer.
• Unified Architecture: Single model architecture handling multiple biological modalities.
• Task-Specific Heads:
  • LucaVirusModel: For sequences embedding.
  • LucaVirusForMaskedLM: For pre-training and sequence recovery.
  • LucaVirusForSequenceClassification: For sequence-level tasks (e.g., protein family, solubility, RdRP identity, or promoter prediction).
  • LucaVirusForTokenClassification: For residue-level tasks (e.g., secondary structure, binding sites, or post-translational modifications).
• Extensible: Easily adaptable to custom downstream tasks using the standard transformers API.

Installation

pip install lucavirus==1.1.0
pip install tokenizers==0.19.1
pip install transformers==4.41.2

You can install LucaVirus directly from source:

git clone -b huggingface https://github.com/LucaOne/LucaVirus.git
cd LucaVirus
pip install .

For development mode:

pip install -e .

🚀Quick Start

1. Feature Extraction/Embedding

Extract high-dimensional embeddings for downstream analysis or training downstream tasks using LucaVirus-Embedding.

Please refer to the code in test/test_lucavirus_embedding.py.

2. MLM Pre-training and Sequence Recovery

Continue to perform MLM pre-training or sequence recovery.

Please refer to the code in test/test_lucavirus_mlm.py.

3. Sequence Classification

Predict properties for the entire sequence (e.g., RdRP vs. Non-RdRP):

Supports multi-class classification, binary classification, multi-label classification, and regression tasks.

Please refer to the code in test/test_lucavirus_seq_classification.py.

4. Token Classification

Predict properties for each residue/nucleotide (e.g., Secondary Structure, Binding Sites, Post-Translational Modifications):

Supports multi-class classification, binary classification, multi-label classification, and regression tasks.

Please refer to the code in test/test_lucavirus_token_classification.py.

Model Configuration

Parameter	Description	Default Value
vocab_size	Size of the dictionary	39
hidden_size	Dimension of the hidden layers	2560
num_hidden_layers	Number of Transformer layers	12
num_attention_heads	Number of attention heads	20
position_embeddings	-	ROPE
alphabet	Type of sequences (e.g., gene, prot, or gene_prot)	DNA, RNA, and Protein

Weights Conversion

If you have legacy weights in .pth format, use the provided conversion script to migrate them to the Hugging Face format. This script maps the original state dictionary to the new lucavirus. prefixed structure.

python scripts/convert_weights.py     

Citation

If you use LucaVirus in your research, please cite:

@article{lucavirus2025,
  title={Predicting the Evolutionary and Functional Landscapes of Viruses with a Unified Nucleotide-Protein Language Model: LucaVirus.},
  author={Pan, Yuan-Fei* and He, Yong*. et al.},
  journal={bioRxiv},
  year={2025},
  url={https://www.biorxiv.org/content/early/2025/06/20/2025.06.14.659722}
}

License

This project is licensed under the MIT License - see the LICENSE file for details.

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For more information or issues, please open a GitHub issue or contact the maintainers at [[e-mail]/[e-mail]].