Instructions to use manueldeprada/FactCC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use manueldeprada/FactCC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="manueldeprada/FactCC")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("manueldeprada/FactCC") model = AutoModelForSequenceClassification.from_pretrained("manueldeprada/FactCC") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a2be0ff755e791650d667693d5eacc01e68f98b0a4395f327d7b119c3403f80d
- Size of remote file:
- 438 MB
- SHA256:
- 4b040c69b68537c82a71601e24c75187494699b41fc3d8ae6565cb2678dba9ed
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