Does the document actually support this claim? grounding-en 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 open, English member of Nutrient's grounding model family.

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

Results

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

Facet	`grounding-en`	`grounding-multilingual`	DeBERTa-v3-large zero-shot	DeBERTa-v3-large MNLI/FEVER/ANLI	BART-large MNLI
Overall	.882	.925	.786	.769	.636
Number	.923	.969	.658	.642	.478
Date	.998	.999	.995	.995	.924
String	.955	.949	.941	.913	.757
Table premises	.863	.915	.766	.747	.611
Prose premises	.964	.970	.929	.945	.892

grounding-en leads the field on the hard axis — number grounding .92 vs .48–.66 for general-purpose NLI models — while matching or beating them everywhere else. Full ranking on the leaderboard. The commercial sibling grounding-multilingual scores a touch higher again and covers 15+ languages.

Usage

python

import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

m = "nutrientdocs/grounding-en"
tok = AutoTokenizer.from_pretrained(m)
model = AutoModelForSequenceClassification.from_pretrained(m).eval()

premise = "Revenue | 2023 | $4,213M\nRevenue | 2022 | $3,905M"
hypothesis = "2023 revenue was $4.2 billion."

enc = tok(premise, hypothesis, truncation=True, max_length=1024, return_tensors="pt")
with torch.no_grad():
    probs = torch.softmax(model(**enc).logits, dim=-1)[0]
p_support = probs[0].item()   # entailment is class index 0 (id2label = {0: entailment, 1: not_entailment})
print(f"grounded support = {p_support:.3f}")

An ONNX export is provided under onnx/ for on-device / ONNX Runtime deployment.

Calibrating the score

Fine-tuning maximizes ranking (AUC), which tends to make 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 it leaves AUC/ranking untouched and only repairs the confidence values. On the serving distribution we fit T = 1.29 (ECE 0.028 → 0.009). 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, routing low-confidence extractions for review.
Limits: English only. The remaining ceiling is reasoning table-claim negatives and multi-step arithmetic. As a dedicated grounding model it trades a little general-NLI accuracy for grounding.

License & training data

Weights are Apache-2.0. Trained on a multi-corpus grounding set. The public, redistributable slice of the evaluation data is grounding-benchmark (CC-BY-SA-4.0); the full training set is not redistributed.

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.