Public release

.gitattributes

@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,103 @@
+---
+license: other
+license_name: nutrient-commercial
+library_name: transformers
+pipeline_tag: text-classification
+language:
+  - multilingual
+  - en
+  - de
+  - fr
+  - es
+  - zh
+  - ja
+  - ar
+  - hi
+  - ru
+  - tr
+  - vi
+  - ko
+  - sw
+  - ur
+  - th
+tags:
+  - grounding
+  - hallucination-detection
+  - fact-verification
+  - nli
+  - zero-shot-classification
+  - multilingual
+  - document-ai
+  - cross-encoder
+datasets:
+  - nutrientdocs/grounding-benchmark
+metrics:
+  - roc_auc
+---
+
+# 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`](https://huggingface.co/nutrientdocs/grounding-en).
+
+- 🎯 **Try it:** [grounding-demo](https://huggingface.co/spaces/nutrientdocs/grounding-demo?model=multi)
+- 🏆 **Leaderboard:** [grounding-leaderboard](https://huggingface.co/spaces/nutrientdocs/grounding-leaderboard)
+- 📊 **Benchmark:** [grounding-benchmark](https://huggingface.co/datasets/nutrientdocs/grounding-benchmark)
+
+## Results
+
+On the held-out multilingual [grounding-benchmark](https://huggingface.co/datasets/nutrientdocs/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](https://huggingface.co/spaces/nutrientdocs/grounding-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](https://huggingface.co/datasets/nutrientdocs/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](https://www.nutrient.io/contact-sales/).**
+
+## About the author
+
+<a href="https://nutrient.io/">
+  <img src="https://avatars2.githubusercontent.com/u/1527679?v=3&s=200" height="80" />
+</a>
+
+This project is maintained and funded by [Nutrient](https://nutrient.io/) - The #1 PDF SDK library for viewing, editing, eSigning, and more.