Instructions to use prubach/KnotProtSequencesModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prubach/KnotProtSequencesModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="prubach/KnotProtSequencesModel")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("prubach/KnotProtSequencesModel") model = AutoModelForSequenceClassification.from_pretrained("prubach/KnotProtSequencesModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0bcd225f039d0261db815e450f0800dd95f3e7ca58551cd4d01fc13234e21070
- Size of remote file:
- 924 MB
- SHA256:
- 50e6759b2af7ad1040f3ff1f4f136207ff734b8e6216353dc7f0349a7971bd5e
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