Text Classification
Transformers
ONNX
Safetensors
English
modernbert
grounding
hallucination-detection
fact-verification
nli
zero-shot-classification
document-ai
cross-encoder
text-embeddings-inference
Instructions to use nutrientdocs/grounding-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nutrientdocs/grounding-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nutrientdocs/grounding-en")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nutrientdocs/grounding-en") model = AutoModelForSequenceClassification.from_pretrained("nutrientdocs/grounding-en") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 66d363d73998cee49a43d3fe751fbecc705365c71c173e25eec43d40f5d141c2
- Size of remote file:
- 792 MB
- SHA256:
- 37fce822256e4e8ba423172d57093ac30a6434e7856114c5267e1d7285297334
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