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
File size: 90 Bytes
8358e80 | 1 2 3 4 5 6 | {
"_from_model_config": true,
"pad_token_id": 0,
"transformers_version": "4.28.0"
}
|