Instructions to use karths/binary_classification_train_service with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_service with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_service")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_service") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_service") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ed8f1169f15168e7afdd15d25c907c064362c8881164ff5e9f00ec14f7e86e0c
- Size of remote file:
- 515 MB
- SHA256:
- 2094f68d7c804ecb000d36ef4705aee88e041aef341e4a8ed145e6bf77e26db2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.