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")# 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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f7144d66a2a2d7856c1855aca27884247f0a8e32fd926977aed04978f31f6c09
- Size of remote file:
- 2.24 GB
- SHA256:
- aca552f63f80841496212a6446959d3683f4be851fdfea7edd8679671e531960
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.