| --- |
| library_name: transformers |
| base_model: duttaprat/HViLM-base |
| datasets: |
| - duttaprat/HVUE-v2 |
| pipeline_tag: text-classification |
| tags: |
| - genomics |
| - virology |
| - dna |
| - virus |
| - pathogenicity |
| - hvue-v2 |
| license: apache-2.0 |
| --- |
| |
| # HViLM-Patho |
|
|
| **HViLM-Patho** is the official HViLM model for binary virus pathogenicity classification. |
|
|
| - **Fine-tuned from:** [duttaprat/HViLM-base](https://huggingface.co/duttaprat/HViLM-base) |
| - **Benchmark:** [duttaprat/HVUE-v2](https://huggingface.co/datasets/duttaprat/HVUE-v2) |
| - **HVUE v2 configuration:** `Pathogenicity/standard_capped_1000bp` |
| - **Checkpoint selection:** best validation F1 (`checkpoint-3000`) |
| - **Input:** virus nucleotide sequence |
| - **Output:** non-pathogenic vs. pathogenic |
|
|
| This repository contains a **standalone full fine-tuned checkpoint**, so users can load `duttaprat/HViLM-Patho` directly without separately loading `HViLM-base`. |
|
|
| ## Label Mapping |
|
|
| | ID | Label | |
| |---:|---| |
| | 0 | `NON_PATHOGENIC` | |
| | 1 | `PATHOGENIC` | |
|
|
| ## Performance |
|
|
| Held-out HVUE v2 test set, standard 1000-nt configuration: |
|
|
| | Metric | Score | |
| |---|---:| |
| | Accuracy | 92.39 | |
| | F1 | 91.32 | |
| | MCC | 83.10 | |
| | Precision | 93.03 | |
| | Recall | 90.12 | |
|
|
| ## Training Details |
|
|
| - **Fine-tuning method:** LoRA |
| - **LoRA rank:** 8 |
| - **LoRA alpha:** 16 |
| - **Target modules:** query and value projections across all 12 transformer layers |
| - **Approximate trainable LoRA parameters:** ~0.3M |
| - **Learning rate:** 3e-5 |
| - **Maximum input length:** 250 BPE tokens (approximately 1000 nt) |
| - **Early stopping:** patience 3, monitored using validation F1 |
| - **Hardware:** NVIDIA A40 GPU |
|
|
| The released repository contains the full task-specific model weights rather than only the LoRA adapter. |
|
|
| ## Usage |
|
|
| ```python |
| import torch |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| |
| model_id = "duttaprat/HViLM-Patho" |
| |
| tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) |
| model = AutoModelForSequenceClassification.from_pretrained( |
| model_id, |
| trust_remote_code=True, |
| ) |
| |
| sequence = "ATGCGTACGTTAGCCGATCGATTACGCGTACGTAGCTAGC" |
| inputs = tokenizer( |
| sequence, |
| return_tensors="pt", |
| truncation=True, |
| max_length=250, |
| ) |
| |
| with torch.no_grad(): |
| logits = model(**inputs).logits |
| |
| prediction_id = logits.argmax(dim=-1).item() |
| print(model.config.id2label[prediction_id]) |
| ``` |
|
|
| Possible outputs are `NON_PATHOGENIC` and `PATHOGENIC`. |
|
|
| ## Intended Use |
|
|
| HViLM-Patho is intended for research on virus sequence representation and computational pathogenicity classification. Predictions should be interpreted as model outputs rather than experimental or clinical evidence. |
|
|
| ## Related Resources |
|
|
| - [HViLM-base](https://huggingface.co/duttaprat/HViLM-base) |
| - [HViLM-R0](https://huggingface.co/duttaprat/HViLM-R0) |
| - [HViLM-Tropism](https://huggingface.co/duttaprat/HViLM-Tropism) |
| - [HVUE-v2](https://huggingface.co/datasets/duttaprat/HVUE-v2) |
| - [HViLM GitHub repository](https://github.com/duttaprat/HViLM) |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{dutta2026hvilm, |
| title={HViLM: A foundation model for viral genomics enables multi-task prediction of pathogenicity, transmissibility, and host tropism}, |
| author={Dutta, Pratik and Vaska, Jack and Surana, Pallavi and Sathian, Rekha and Chao, Max and Zhou, Zhihan and Liu, Han and Davuluri, Ramana V}, |
| journal={bioRxiv}, |
| pages={2026--03}, |
| year={2026}, |
| publisher={Cold Spring Harbor Laboratory} |
| } |
| ``` |
|
|
