grounding-multilingual · commercial

Does the document actually support this claim — in 15+ languages? grounding-multilingual is a cross-encoder that scores whether a hypothesis (a number, date, or fact) is entailed by a premise drawn from a real document — a financial table, a filing, prose evidence.

It is the strongest, multilingual member of Nutrient's grounding model family. Weights are commercial (not downloadable here); this page is a spec + scorecard. For the open, English model see grounding-en.

• 🎯 Try it: grounding-demo
• 🏆 Leaderboard: grounding-leaderboard
• 📊 Benchmark: grounding-benchmark

Results

On the held-out multilingual grounding-benchmark (ROC-AUC), against the top open multilingual NLI models:

Facet	grounding-multilingual	mDeBERTa-v3-base XNLI-2mil7	mDeBERTa-v3-base MNLI-XNLI	XLM-R-large XNLI
Overall	.965	.894	.831	.811
Number	.999	.775	.806	.765
Table premises	.999	.727	.780	.715
Prose premises	.926	.961	.864	.869

It leads on number (.999 vs .77–.81) and table grounding (.999 vs .72–.78) across 15+ languages; on general prose NLI the best multilingual zero-shot model edges it. Full ranking on the leaderboard. (Date/string grounding is too rare in the multilingual corpus to score reliably, so it's omitted here.)

Calibrating the score

Fine-tuning maximizes ranking (AUC), which can leave the raw probability overconfident. For a score you can gate on ("0.9 means ~90% right"), apply temperature scaling — divide the logits by a fitted T before softmax. It's monotonic, so AUC/ranking is untouched and only the confidence values are repaired. On the serving distribution we fit T = 0.94 (ECE 0.012 → 0.007). Re-fit T on your distribution whenever your input pipeline changes.

Intended use & limits

• Use it for: verifying extracted values against source documents, hallucination/citation checking, and routing low-confidence extractions for review — across 15+ languages, on-prem.
• Limits: the remaining ceiling is reasoning table-claim negatives and multi-step arithmetic. As a dedicated grounding checkpoint it trades a little general-NLI accuracy for grounding.

License & data

The model weights are offered under a commercial Nutrient license — on-prem, so documents never leave your infrastructure. The training set is not redistributed. The evaluation data is public and reproducible: grounding-benchmark (CC-BY-SA-4.0); the full training set is not redistributed.

📩 Get access

grounding-multilingual is commercial and its weights are not downloadable here. To run it on-prem — multilingual, calibrated, private — contact Nutrient: nutrient.io/contact-sales.

About the author

This project is maintained and funded by Nutrient - 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.