Instructions to use sgonzalezsilot/FakeNewsDetection_sydney_NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sgonzalezsilot/FakeNewsDetection_sydney_NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sgonzalezsilot/FakeNewsDetection_sydney_NER")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sgonzalezsilot/FakeNewsDetection_sydney_NER") model = AutoModelForSequenceClassification.from_pretrained("sgonzalezsilot/FakeNewsDetection_sydney_NER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from sgonzalezsilot/FakeNewsDetection_sydney_NER: direct link, hf CLI and curl.
- Browser
- Download file 4.73 kB
-
https://huggingface.co/sgonzalezsilot/FakeNewsDetection_sydney_NER/resolve/main/training_args.bin
- Command line
-
hf download hf://sgonzalezsilot/FakeNewsDetection_sydney_NER/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/sgonzalezsilot/FakeNewsDetection_sydney_NER/resolve/main/training_args.bin
4.73 kB
- Xet hash:
- 37ea5ad0b2ddbb155e9c5aff56005133347cdab1162d693fc30293de52e6c750
- Size of remote file:
- 4.73 kB
- SHA256:
- dff59c236f053ddedcc9ab1458611efc1e6e3955fc75fa435e9014db113b5076
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