Instructions to use karths/binary_classification_train_code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_code with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_code")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_code") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_code") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 390699063e84a3fedf762e38f89f88e0937008798ffab896a73f5771c28f4db1
- Size of remote file:
- 328 MB
- SHA256:
- ddf8cae4165bf8c4ce39f9a029b7910466c5a2651b3a41a86f29e7176e98ca7e
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