Feature Extraction
Transformers
PyTorch
English
fill-mask
genomics
virology
dnabert
foundation-model
hvilm
pathogenicity
transmissibility
host-tropism
viral-genomics
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, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 66bbde1cc48ddc37958dcbe31d4b991e50dc834ff2840356639d203b8f65b24d
- Size of remote file:
- 468 MB
- SHA256:
- 7ea8bf7db06bfc7948bc7152881c0a9c1d04c9ae499ff1d15cedb399cefde336
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