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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1332534c0fd5aec3f2362cd7ffb31b425b1baa22bb5c757e814b70b843b5ba9c
- Size of remote file:
- 328 MB
- SHA256:
- 9091e9a2e831c5a567bd2c39fbf95f859fe6be61081cf3164b33afdc6beb5b69
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