Text Classification
Transformers
grounding
hallucination-detection
fact-verification
nli
zero-shot-classification
document-ai
cross-encoder
Instructions to use nutrientdocs/grounding-multilingual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nutrientdocs/grounding-multilingual with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nutrientdocs/grounding-multilingual")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nutrientdocs/grounding-multilingual", dtype="auto") - Notebooks
- Google Colab
- Kaggle
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This project is maintained and funded by [Nutrient](https://nutrient.io/) - The
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This project is maintained and funded by [Nutrient](https://nutrient.io/) - The deterministic document infrastructure enterprises run their highest-stakes workflows on: replayable output, clear exceptions, and full audit trails on the messy, regulated documents where AI alone breaks.
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