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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7f77f9a8267e2e25835e96d7a782cbd924454c40ba9a234d3feab0d4135e8c68
- Size of remote file:
- 924 MB
- SHA256:
- c1d95073446f5c2e98de40a187b81866742e34cdf07329d404efb637a48b0540
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