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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d8ba62770d634e6ae6651a8c22cf11162e7d4392ab7cd4feaa5000d6e6f9bab9
- Size of remote file:
- 331 MB
- SHA256:
- e50e862da246267a22319c87cffb3bca1da14146ea8ee0d4c612fbe1830770d0
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