Instructions to use hamzab/roberta-fake-news-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hamzab/roberta-fake-news-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hamzab/roberta-fake-news-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hamzab/roberta-fake-news-classification") model = AutoModelForSequenceClassification.from_pretrained("hamzab/roberta-fake-news-classification", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 50f0da1fb817c37fadfd9a69572781c03b523343f28a2f76df8588c166042e63
- Size of remote file:
- 499 MB
- SHA256:
- ea9a259f46efa47b223850dcf1a206aa15bcaba3f168e692a49ed025b0170432
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