Token Classification
GLiNER2
Safetensors
GLiNER
extractor
pii
ner
privacy
redaction
information-extraction
span-extraction
Instructions to use mateoopa/gliner2-privacy-filter-PII-multi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use mateoopa/gliner2-privacy-filter-PII-multi with GLiNER2:
from gliner2 import AutoExtractor extractor = AutoExtractor.from_pretrained("mateoopa/gliner2-privacy-filter-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 mateoopa/gliner2-privacy-filter-PII-multi with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("mateoopa/gliner2-privacy-filter-PII-multi") text = "Cristiano Ronaldo dos Santos Aveiro was born on 5 February 1985 in Funchal, Madeira, Portugal." labels = ["person", "date", "location"] entities = model.predict_entities(text, labels) for entity in entities: print(entity["text"], "=>", entity["label"]) - Notebooks
- Google Colab
- Kaggle
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