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:
- 312be603d9bd97c7dcc5cde0625df80174077bc138ae9646e0cbd7e442e942f7
- Size of remote file:
- 793 MB
- SHA256:
- 6be940cd1592bab50330877d9f3919c8f8580e0d5f3e2ece3cfd37382a3ab5c0
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