Text Classification
Transformers
PyTorch
megatron-bert
prokbert
bioinformatics
genomics
sequence embedding
genomic language models
nucleotide
dna-sequence
promoter-prediction
phage
Instructions to use neuralbioinfo/prokbert-mini-phage with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use neuralbioinfo/prokbert-mini-phage with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="neuralbioinfo/prokbert-mini-phage")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("neuralbioinfo/prokbert-mini-phage") model = AutoModelForSequenceClassification.from_pretrained("neuralbioinfo/prokbert-mini-phage", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
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by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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oid sha256:2756d85dca27d24fd748ce02bca10a54d3ed56152803d173af73a9f51dd91df8
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