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
File size: 135 Bytes
e70b183 | 1 2 3 4 5 6 | {
"clean_up_tokenization_spaces": true,
"model_max_length": 1000000000000000019884624838656,
"tokenizer_class": "EsmTokenizer"
}
|