Feature Extraction
Transformers
PyTorch
English
fill-mask
genomics
virology
dnabert
foundation-model
hvilm
viral-genomics
pathogenicity
transmissibility
host-tropism
hvue-v2
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)# 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
Could You Upload the Fine-Tuned Model Versions?
#2
by qtyang - opened
Hi, thank you for your excellent work! I was wondering if you could upload the fine-tuned model checkpoints.
Hi, thank you for your interest in HViLM.
We have now uploaded the fine-tuned model checkpoints for all three downstream tasks:
HViLM-Patho: https://huggingface.co/duttaprat/HViLM-Patho
HViLM-R0: https://huggingface.co/duttaprat/HViLM-R0
HViLM-Tropism: https://huggingface.co/duttaprat/HViLM-Tropism
We have also grouped the base model, fine-tuned models, and HVUE v2 benchmark in the HViLM Hugging Face collection.
Thanks again for your interest, and please let us know if you run into any issues using the models.