Token Classification
GLiNER2
Safetensors
GLiNER
English
extractor
named-entity-recognition
ner
pii
anonymisation
privacy
Eval Results (legacy)
Instructions to use OvermindLab/nerpa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use OvermindLab/nerpa with GLiNER2:
from gliner2 import GLiNER2 model = GLiNER2.from_pretrained("OvermindLab/nerpa") # 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 OvermindLab/nerpa with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("OvermindLab/nerpa") - Notebooks
- Google Colab
- Kaggle
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# NERPA - Fine-Tuned GLiNER2 for PII Anonymisation
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A fine-tuned [GLiNER2 Large](https://huggingface.co/fastino/gliner2-large-v1) (340M params) model trained to detect Personally Identifiable Information (PII) in text. Built as a flexible, self-hosted replacement for AWS Comprehend at [Overmind](https://overmindlab.ai).
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# NERPA - Fine-Tuned GLiNER2 for PII Anonymisation
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A fine-tuned [GLiNER2 Large](https://huggingface.co/fastino/gliner2-large-v1) (340M params) model trained to detect Personally Identifiable Information (PII) in text. Built as a flexible, self-hosted replacement for AWS Comprehend at [Overmind](https://overmindlab.ai).
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