Instructions to use isikz/phosphorylation_binaryclassification_esm1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use isikz/phosphorylation_binaryclassification_esm1b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="isikz/phosphorylation_binaryclassification_esm1b")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("isikz/phosphorylation_binaryclassification_esm1b") model = AutoModelForSequenceClassification.from_pretrained("isikz/phosphorylation_binaryclassification_esm1b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4c7ce04ecb494fdde02ad2e04a7a4c786fc5cc90934cecb109255923d08dfb5f
- Size of remote file:
- 2.61 GB
- SHA256:
- 09b512c812777279ab11f8819fa193ede932d7a867db8d7a9bdd340725a8dee9
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