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-c-phage with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use neuralbioinfo/prokbert-mini-c-phage with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="neuralbioinfo/prokbert-mini-c-phage")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("neuralbioinfo/prokbert-mini-c-phage") model = AutoModelForSequenceClassification.from_pretrained("neuralbioinfo/prokbert-mini-c-phage", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from neuralbioinfo/prokbert-mini-c-phage: direct link, hf CLI and curl.
- Browser
- Download file 99.9 MB
-
https://huggingface.co/neuralbioinfo/prokbert-mini-c-phage/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://neuralbioinfo/prokbert-mini-c-phage/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/neuralbioinfo/prokbert-mini-c-phage/resolve/main/pytorch_model.bin
99.9 MB
- Xet hash:
- 2ad6fb17b1dcedcc72bcf2d26c777e8f6dcc3df44c93bd9aefa9a1cc7583b82a
- Size of remote file:
- 99.9 MB
- SHA256:
- bcc5f4955fe97e959c0acb318b7b4d03562f894ef4e29e4445f77c5eb2c2a372
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