Instructions to use karths/binary_classification_train_design with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_design with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_design")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_design") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_design") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4ec2ca30e1061d614f5fbc93ddaf764acc9258ca93c4139480634bbdb9591ccb
- Size of remote file:
- 929 kB
- SHA256:
- 107554ebf73c426012064aeedf9d4f6ab2e37bc4a9c211ae4bea43f1f31aaff6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.