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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ENTUM-AI/FactGuard") model = AutoModelForSequenceClassification.from_pretrained("ENTUM-AI/FactGuard", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from ENTUM-AI/FactGuard: direct link, hf CLI and curl.
- Browser
- Download file 3.58 MB
-
https://huggingface.co/ENTUM-AI/FactGuard/resolve/main/tokenizer.json
- Command line
-
hf download hf://ENTUM-AI/FactGuard/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/ENTUM-AI/FactGuard/resolve/main/tokenizer.json
3.58 MB
File too large to display, you can check the raw version instead.