Instructions to use Sami92/XLM-R-Large-Disinfo-Narrative-Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sami92/XLM-R-Large-Disinfo-Narrative-Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sami92/XLM-R-Large-Disinfo-Narrative-Classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sami92/XLM-R-Large-Disinfo-Narrative-Classifier") model = AutoModelForSequenceClassification.from_pretrained("Sami92/XLM-R-Large-Disinfo-Narrative-Classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Sami92/XLM-R-Large-Disinfo-Narrative-Classifier: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/Sami92/XLM-R-Large-Disinfo-Narrative-Classifier/resolve/main/tokenizer.json
- Command line
-
hf download hf://Sami92/XLM-R-Large-Disinfo-Narrative-Classifier/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Sami92/XLM-R-Large-Disinfo-Narrative-Classifier/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 2d08df1c3d32a4f6170a99f30db3e272bc3e90b0dde5af751a35fb9bf9cb2612
- Size of remote file:
- 17.1 MB
- SHA256:
- 8373f9cd3d27591e1924426bcc1c8799bc5a9affc4fc857982c5d66668dd1f41
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