Token Classification
GLiNER2
Safetensors
GLiNER
extractor
pii
ner
privacy
redaction
safety
moderation
guardrails
information-extraction
span-extraction
text-classification
multi-label-classification
jailbreak-detection
toxicity-classification
Instructions to use fastino/GLiNER2-Guardrails-PII-Multi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use fastino/GLiNER2-Guardrails-PII-Multi with GLiNER2:
from gliner2 import GLiNER2 model = GLiNER2.from_pretrained("fastino/GLiNER2-Guardrails-PII-Multi") # Extract entities text = "Apple CEO Tim Cook announced iPhone 15 in Cupertino yesterday." result = extractor.extract_entities(text, ["company", "person", "product", "location"]) print(result) - GLiNER
How to use fastino/GLiNER2-Guardrails-PII-Multi with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("fastino/GLiNER2-Guardrails-PII-Multi") - Notebooks
- Google Colab
- Kaggle
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**`fastino/GLiNER2-Guardrails-PII-Multi`** is a single [GLiNER2](https://github.com/fastino-ai/GLiNER2) model that combines two capabilities in one checkpoint:
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## When to use this model
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| Use case | Why
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| **Guardrails + PII in one pass** | Single deployment for moderation and redaction |
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| **PII redaction / GDPR-CCPA compliance** | 42 fine-grained, multilingual PII types |
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# GLiNER2-Guardrails-PII-Multi: Unified Multilingual Safety Moderation & PII Detection
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**`fastino/GLiNER2-Guardrails-PII-Multi`** is a single [GLiNER2](https://github.com/fastino-ai/GLiNER2) model that combines two capabilities in one checkpoint:
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## When to use this model
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| Use case | Why GLiNER2-Guardrails-PII-Multi |
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| **Guardrails + PII in one pass** | Single deployment for moderation and redaction |
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| **PII redaction / GDPR-CCPA compliance** | 42 fine-grained, multilingual PII types |
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