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
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## Model Description
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**HViLM (Human Virome Language Model)** is a genomic foundation model adapted to virus sequences through continued pre-training of [DNABERT-2](https://huggingface.co/zhihan1996/DNABERT-2-117M). HViLM-base was trained on approximately **5 million non-redundant virus-derived sequence fragments** from the [VIRION](https://
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The architecture and tokenizer remain those of DNABERT-2; continued pre-training updates the model weights using a masked-language-modeling objective on the virus-focused corpus.
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## Acknowledgments
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HViLM builds on DNABERT-2 by Zhou et al. Continued-pretraining data were derived from the VIRION
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## Model Description
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**HViLM (Human Virome Language Model)** is a genomic foundation model adapted to virus sequences through continued pre-training of [DNABERT-2](https://huggingface.co/zhihan1996/DNABERT-2-117M). HViLM-base was trained on approximately **5 million non-redundant virus-derived sequence fragments** from the from [VIRION](https://www.viralemergence.org/virion) ([Zenodo deposit](https://doi.org/10.5281/zenodo.15643003)), representing approximately 9,000 virus species across 45+ families.
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The architecture and tokenizer remain those of DNABERT-2; continued pre-training updates the model weights using a masked-language-modeling objective on the virus-focused corpus.
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## Acknowledgments
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HViLM builds on DNABERT-2 by Zhou et al. Continued-pretraining data were derived from the from [VIRION](https://www.viralemergence.org/virion) ([Zenodo deposit](https://doi.org/10.5281/zenodo.15643003)) maintained by the Viral Emergence Research Initiative (Verena).
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