Instructions to use karths/binary_classification_train_process with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_process with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_process")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_process") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_process") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bf6f4154eff3236fd09060b4a65ce19c1dfff31d3eb538e7dfc3c0be5485beff
- Size of remote file:
- 328 MB
- SHA256:
- ada1597389e5da7ef87b916039d3442cc4cd9129f0d9d5fb51319f23e41053bb
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