Text Classification
Transformers
Safetensors
English
modernbert
hallucination-detection
grounding
factual-consistency
nli
rag
text-embeddings-inference
Instructions to use ENTUM-AI/FactGuard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ENTUM-AI/FactGuard with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ENTUM-AI/FactGuard")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ENTUM-AI/FactGuard") model = AutoModelForSequenceClassification.from_pretrained("ENTUM-AI/FactGuard") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e569ede0bc2d463fce8e048b377608fc5464c067a4a621be9300269db8f13994
- Size of remote file:
- 598 MB
- SHA256:
- ca692bc1e264ae0d6febb14e04ea93452616592a2c1eb461c0e67fbdc75d4ee3
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