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
- Xet hash:
- 655c51d4b54757cbe4dc28bba24e6b35115d1f8641e04137334759d2078bd0fb
- Size of remote file:
- 5.84 kB
- SHA256:
- 973c1da75c199f7418a7aa7be5dac462e2e0e788996a48ee730dbd1acd3e0401
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