Instructions to use karths/binary_classification_train_documentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_documentation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_documentation")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_documentation") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_documentation") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 62c8b7bdcef494517b43d02f0becac8a96d789fac0223f2260fb5dea1822ca3b
- Size of remote file:
- 14.2 kB
- SHA256:
- a9adb5bd3a062ebd1d21c9334d6d8b85df023540f771a086f57cc8450b18fd3f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.