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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 51441eecf71cacd2fe5de48a37a8b5d4fab4105aaf81aaf01f8576e8ef7b8038
- Size of remote file:
- 657 MB
- SHA256:
- 0f363796208fc3a6915a060e5d3299ab6411727ab75b30699dc80164980e85c7
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