Instructions to use karths/binary_classification_train_port with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_port with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_port")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_port") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_port", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6841fcfffd4e7762ad9a945fd9a70c4486e635ecefc7c40f73a00120d203907d
- Size of remote file:
- 657 MB
- SHA256:
- 46572d7bd8d7ef33b73df79399062f5dc3b17ff40faef0087e294565b88b31d8
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