Feature Extraction
Transformers
PyTorch
English
fill-mask
genomics
virology
dnabert
foundation-model
hvilm
viral-genomics
pathogenicity
transmissibility
host-tropism
hvue
custom_code
Instructions to use duttaprat/HViLM-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use duttaprat/HViLM-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="duttaprat/HViLM-base", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("duttaprat/HViLM-base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -302,6 +302,7 @@ For detailed instructions, see the [GitHub repository](https://github.com/duttap
|
|
| 302 |
|
| 303 |
## Citation
|
| 304 |
If you use HViLM in your research, please cite our paper:
|
|
|
|
| 305 |
```bibtex
|
| 306 |
@article{dutta2026hvilm,
|
| 307 |
title={HViLM: A foundation model for viral genomics enables multi-task prediction of pathogenicity, transmissibility, and host tropism},
|
|
|
|
| 302 |
|
| 303 |
## Citation
|
| 304 |
If you use HViLM in your research, please cite our paper:
|
| 305 |
+
|
| 306 |
```bibtex
|
| 307 |
@article{dutta2026hvilm,
|
| 308 |
title={HViLM: A foundation model for viral genomics enables multi-task prediction of pathogenicity, transmissibility, and host tropism},
|