Text Classification
Transformers
PyTorch
JAX
Safetensors
English
bert
biology
microbiology
protein-language-model
pLM
deep-learning
Instructions to use virtual-human-chc/prot_bert_bfd_membrane with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use virtual-human-chc/prot_bert_bfd_membrane with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="virtual-human-chc/prot_bert_bfd_membrane")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("virtual-human-chc/prot_bert_bfd_membrane") model = AutoModelForSequenceClassification.from_pretrained("virtual-human-chc/prot_bert_bfd_membrane") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 36f66619731c2197f4ca168657d5f7c65e55f8ca7bb1720e746325368c65b6ee
- Size of remote file:
- 1.68 GB
- SHA256:
- 0670587c72cfd295cbab3d3398c8ba4ae9667e7217232194d3b9a4ed5e4db36b
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