Publish Sano Shield 1.1 model card

README.md

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+---
+language:
+  - de
+  - en
+library_name: transformers
+pipeline_tag: token-classification
+base_model: bardsai/eu-pii-anonimization-multilang
+tags:
+  - pii-detection
+  - named-entity-recognition
+  - privacy
+  - xlm-roberta
+---
+
+# Sano Shield 1.1
+
+Sano Shield 1.1 is Sano AI's multilingual privacy model for identifying sensitive
+information before text reaches an AI provider. Its technical model ID is
+`sano-shield-1-multilingual`.
+
+> **Availability:** This repository publishes the model card only. Model weights,
+> tokenizer files, ONNX artifacts, and training data are not publicly distributed or
+> downloadable from Hugging Face.
+
+## Model details
+
+| Field | Value |
+|---|---|
+| Model family | Sano Shield |
+| Release | Sano Shield 1.1 |
+| Technical ID | `sano-shield-1-multilingual` |
+| Task | Token classification / sensitive-entity recognition |
+| Evaluated languages | German and English |
+| Entity classes | 35 entity types, 69 BIO token labels |
+| Architecture | XLM-RoBERTa token classifier |
+| Base-model lineage | [`bardsai/eu-pii-anonimization-multilang`](https://huggingface.co/bardsai/eu-pii-anonimization-multilang) |
+| Release date | 2026-08-01 |
+
+Sano Shield 1.1 continues a private Sano Shield 1.0 checkpoint. Sano Shield 1.0 was a
+full-parameter continued fine-tune of the pinned base-model revision
+`0e72e19f030ed4e661b1673e549af8e0dd176386`. Training targets come from Sano's
+versioned synthetic corpus rather than predictions copied from a separate evaluator.
+
+## Intended use
+
+Sano Shield 1.1 is designed as one component in a privacy pipeline:
+
+1. identify sensitive spans in German and English text;
+2. replace those spans with controlled placeholders before external model inference; and
+3. support re-identification only inside a trusted boundary.
+
+Production deployments should combine the model with deterministic recognizers, policy
+checks, monitoring, and fail-safe handling. Organization names remain user-selectable but
+are not treated as mandatory natural-person PII by the release policy.
+
+The model should not be used as the sole basis for legal-compliance decisions, medical
+decisions, identity verification, employee monitoring, or irreversible actions affecting
+a person.
+
+## Evaluation
+
+The promoted checkpoint passed both synthetic checkpoint gates and a separately maintained
+product-pipeline evaluation. The product evaluation data was held out from training.
+
+| Product gate | Sano Shield 1.1 result |
+|---|---:|
+| German protected-span recall | 470 / 470 (100%) |
+| English protected-span recall | 4,187 / 4,188 (99.976%) |
+| Zero-touch documents | 29 / 30 |
+| Raw false or unknown detections | 83, reduced from 141 |
+| ONNX/PyTorch label agreement | 100% |
+
+On the synthetic continuation evaluation, exact entity F1 improved by 0.0454 for German
+and 0.0209 for English relative to the Sano Shield 1.0 parent, while privacy-span recall
+was unchanged.
+
+These results do not guarantee equivalent performance on other domains. The held-out
+product evaluation measures Sano's release policy and complete product pipeline, including
+deterministic rules; it should not be interpreted as a neural-model-only benchmark.
+
+## Training
+
+- Parent: private Sano Shield 1.0 selected checkpoint
+- Corpus: Sano Shield v0.4 synthetic continuation corpus
+- Schedule: one epoch at a `1e-6` learning rate
+- Composition: 62.5% replay, 25% positive contextual data, 4.2% contrastive data, and
+  8.3% new negatives
+- Loss: square-root inverse class weighting capped at `3.0`, with the outside label at
+  weight `1.0`
+
+The continuation run restarted from Sano Shield 1.0 rather than from either rejected
+candidate run.
+
+## Limitations
+
+- German and English are the only release-policy target languages.
+- Unseen document types, OCR errors, adversarial formatting, and ambiguous context can
+  produce missed or extra spans.
+- No statistical detector can guarantee that all sensitive information is found.
+- The model recognizes spans; it does not determine whether processing them is lawful or
+  satisfy a particular compliance regime.
+- High-risk deployments require domain-specific, legally usable, human-reviewed evaluation.
+
+## Distribution
+
+This public page is provided for transparency about the model's purpose, lineage,
+evaluation, and limitations. It does not grant access to the checkpoint or corpus, and no
+public redistribution license for those artifacts is granted here. Contact Sano AI for
+deployment and access information.