Instructions to use christinacdl/clickbait_binary_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use christinacdl/clickbait_binary_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="christinacdl/clickbait_binary_detection")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("christinacdl/clickbait_binary_detection") model = AutoModelForSequenceClassification.from_pretrained("christinacdl/clickbait_binary_detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e2c3fe4db4ef75e54dc560a8ae29e0235e72b2ca8d697d731882f9e9400f9c5c
- Size of remote file:
- 1.42 GB
- SHA256:
- 386bd3cfa38462efe23b617d13255ee5d05ab8b02c761fb26903d14b6a704368
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