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:
- 54da53319d0d7ca69cc3cd0b4035d3b41035ea4c62cdd3e462ac7400c856dbdb
- Size of remote file:
- 438 MB
- SHA256:
- 4722609b56ed46485be337cead5c03f71943097a8eef2635174d0eeb9a77a978
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