Instructions to use DraiP/Fake_News_Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DraiP/Fake_News_Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DraiP/Fake_News_Classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DraiP/Fake_News_Classifier") model = AutoModelForSequenceClassification.from_pretrained("DraiP/Fake_News_Classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6e48e4527a8d3b5cba44ce2f25bec526794ea4faaffe189031847d96f12bb86e
- Size of remote file:
- 3.9 kB
- SHA256:
- eaddd9ad0c4dbda686ce6cef98d9af6569f496b801150e581acf6a9370b68474
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.