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
- Xet hash:
- 8daef94ce7f7f7959b927ac7b2db5b263551aed71754a8ba50aec1765155ebf3
- Size of remote file:
- 1.23 GB
- SHA256:
- 82ee0ed2483aa7eae3483e95b8622139f5bc7697de3294aec4d0d7088bdb7658
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