Create README.md

README.md

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+---
+library_name: gliner2
+language:
+  - en
+  - fr
+  - es
+  - de
+  - it
+  - pt
+  - nl
+tags:
+  - pii
+  - ner
+  - privacy
+  - redaction
+  - gliner
+  - gliner2
+  - information-extraction
+  - span-extraction
+license: apache-2.0
+datasets:
+  - synthetic
+pipeline_tag: token-classification
+---
+
+# GLiNER2-PII: Multilingual PII Detection & Masking
+
+**GLiNER2-PII** is a fine-tune of the [GLiNER2](https://github.com/fastino-ai/GLiNER2) model (205M parameters) for detecting and masking personally identifiable information across **42 entity types** and **7 languages**.
+
+Trained entirely on a constraint-driven synthetic corpus of 4,910 annotated texts, it achieves the **highest span-level F1 (0.477)** on the [SPY benchmark](https://aclanthology.org/2025.naacl-srw.23/) among four compared systems — including OpenAI Privacy Filter, NVIDIA GLiNER-PII, and urchade/gliner\_multi\_pii-v1.
+
+📄 **[Technical Report](https://github.com/fastino-ai/GLiNER2)**  
+🔗 **[GitHub](https://github.com/fastino-ai/GLiNER2)**
+
+---
+
+## Quick Start
+
+```bash
+pip install gliner2
+```
+
+```python
+from gliner2 import GLiNER2
+
+model = GLiNER2.from_pretrained("fastino/gliner2-pii-v1")
+
+text = "Email john.smith@acme.com or call +1 415 555 0199."
+labels = ["email", "phone_number", "person"]
+
+result = model.extract_entities(
+    text,
+    labels,
+    threshold=0.5,
+    include_confidence=True,
+    include_spans=True,
+)
+
+print(result)
+```
+
+You can pass **any subset** of the 42 supported labels — the model conditions on the labels you provide at inference time.
+
+---
+
+## Supported PII Labels (42 types)
+
+| Group | Labels |
+|---|---|
+| **Person / names** | `person`, `full_name`, `first_name`, `middle_name`, `last_name`, `date_of_birth` |
+| **Contact / address** | `email`, `phone_number`, `address`, `street_address`, `city`, `state_or_region`, `postal_code`, `country` |
+| **Government / tax IDs** | `government_id`, `national_id_number`, `passport_number`, `drivers_license_number`, `license_number`, `tax_id`, `tax_number` |
+| **Banking / payment** | `bank_account`, `account_number`, `routing_number`, `iban`, `payment_card`, `card_number`, `card_expiry`, `card_cvv` |
+| **Digital identity** | `username`, `ip_address`, `account_id`, `sensitive_account_id` |
+| **Secrets / credentials** | `password`, `secret`, `api_key`, `access_token`, `recovery_code` |
+| **Sensitive dates** | `sensitive_date`, `document_date`, `expiration_date`, `transaction_date` |
+
+---
+
+## Benchmark Results (SPY)
+
+Evaluated on the [SPY benchmark](https://aclanthology.org/2025.naacl-srw.23/) (Savkin et al., 2025) with exact-match span-level metrics:
+
+| Model | Legal P | Legal R | Legal F1 | Medical P | Medical R | Medical F1 | **Avg F1** |
+|---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
+| **fastino/gliner2-pii-v1** | .346 | **.750** | **.473** | .369 | **.686** | **.480** | **.477** |
+| nvidia/gliner-PII | .343 | .452 | .390 | .368 | .465 | .411 | .400 |
+| urchade/gliner\_multi\_pii-v1 | **.467** | .317 | .377 | **.518** | .351 | .419 | .398 |
+| openai/privacy-filter | .242 | .656 | .354 | .287 | .692 | .406 | .380 |
+
+### Key takeaways
+
+- **Highest F1** on both legal and medical domains.
+- **Best recall** among GLiNER-based detectors (0.718 avg) — critical for redaction workflows where missed spans are data leaks.
+- Consistent performance across domains (< 2-point F1 difference).
+
+---
+
+## When to Use This Model
+
+| Use case | Why GLiNER2-PII |
+|---|---|
+| **PII redaction / masking** | High recall minimises missed sensitive spans |
+| **Data governance & GDPR/CCPA compliance** | 42 fine-grained types enable policy-specific routing |
+| **Training-data hygiene** | Exact character spans for precise masking before model training |
+| **Multi-language pipelines** | Trained on EN, FR, ES, DE, IT, PT, NL formats |
+
+---
+
+## Redaction Example
+
+```python
+def redact(text, labels, threshold=0.5):
+    model = GLiNER2.from_pretrained("fastino/gliner2-pii-v1")
+    result = model.extract_entities(
+        text, labels, threshold=threshold,
+        include_spans=True,
+    )
+    entities = result.get("entities", {})
+    spans = []
+    for label, values in entities.items():
+        for value in values:
+            start = text.find(value)
+            if start != -1:
+                spans.append((start, start + len(value), label))
+
+    spans.sort(key=lambda s: s[0], reverse=True)
+    redacted = text
+    for start, end, label in spans:
+        redacted = redacted[:start] + f"[{label.upper()}]" + redacted[end:]
+    return redacted
+
+
+text = "Please contact Maria Jensen at maria.jensen@example.dk or +45 20 12 34 56."
+labels = ["person", "email", "phone_number"]
+print(redact(text, labels))
+# "Please contact [PERSON] at [EMAIL] or [PHONE_NUMBER]."
+```
+
+---
+
+## Training Details
+
+| Detail | Value |
+|---|---|
+| Base model | GLiNER2 (205M parameters) |
+| Training data | 4,910 synthetic annotated texts |
+| PII mentions | 129,951 total (mean 26.5 per example) |
+| Generator | GPT-5.4 (temperature 0.01) |
+| Data framework | Constraint-driven generation (same framework as [Pioneer Agent](https://arxiv.org/abs/2604.09791)) |
+| Languages | English, French, Spanish, German, Italian, Portuguese, Dutch |
+| Label types | 42 PII entity types across 7 semantic groups |
+
+---
+
+## Limitations
+
+- **Precision** (0.35–0.37 on SPY) leaves room for improvement; the model tends to over-predict `name` entities, sometimes confusing common nouns, organisation names, and product names with personal names.
+- Evaluated on a **single benchmark** (SPY) covering two domains. Broader multilingual and fine-grained evaluation is ongoing.
+- Training data is **fully synthetic** and has not been validated by human annotators.
+- Performance on **non-European** locales and scripts has not been measured.
+
+### Improving precision
+
+For production use, consider:
+- Per-label confidence thresholds (raise threshold for `person` / `full_name`)
+- Dictionary-based filtering for common false positives
+- Calibration on a small domain-specific development set
+
+---
+
+## Citation
+
+```bibtex
+@misc{fastino2026gliner2pii,
+  title   = {GLiNER2-PII: Multilingual PII Extraction via Synthetic Fine-Tuning},
+  author  = {{Fastino AI Team}},
+  year    = {2026},
+  url     = {https://huggingface.co/fastino/gliner2-pii-v1}
+}
+```
+
+### Related work
+
+```bibtex
+@inproceedings{zaratiana-etal-2025-gliner2,
+  title     = {GLiNER2: Schema-Driven Multi-Task Learning for Structured Information Extraction},
+  author    = {Zaratiana, Urchade and Pasternak, Gil and Boyd, Oliver and Hurn-Maloney, George and Lewis, Ash},
+  booktitle = {Proceedings of EMNLP 2025: System Demonstrations},
+  year      = {2025}
+}
+
+@inproceedings{zaratiana-etal-2024-gliner,
+  title     = {GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer},
+  author    = {Zaratiana, Urchade and Tomeh, Nadi and Holat, Pierre and Charnois, Thierry},
+  booktitle = {Proceedings of NAACL 2024},
+  year      = {2024}
+}
+
+@misc{atreja2026pioneeragent,
+  title  = {Pioneer Agent: Continual Improvement of Small Language Models in Production},
+  author = {Atreja, Dhruv and White, Julia and Nayak, Nikhil and Zhang, Kelton and Princis, Henrijs and Hurn-Maloney, George and Lewis, Ash and Zaratiana, Urchade},
+  year   = {2026},
+  url    = {https://arxiv.org/abs/2604.09791}
+}
+```
+
+---
+
+## License
+
+Apache 2.0