Token Classification
GLiNER
ONNX
German
English
extractor
ner
zero-shot
pii-detection
privacy
multilingual
quantized
edge
Instructions to use patronus-studio/gliner2-multi-edge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use patronus-studio/gliner2-multi-edge with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("patronus-studio/gliner2-multi-edge") 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
Download tokenizer.json from patronus-studio/gliner2-multi-edge: direct link, hf CLI and curl.
- Browser
- Download file 16.3 MB
-
https://huggingface.co/patronus-studio/gliner2-multi-edge/resolve/main/tokenizer.json
- Command line
-
hf download hf://patronus-studio/gliner2-multi-edge/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/patronus-studio/gliner2-multi-edge/resolve/main/tokenizer.json
16.3 MB
- Xet hash:
- ba75cafa615417b5fc120088385494f01665c2c4de9b297e7ef6857700dbc0ba
- Size of remote file:
- 16.3 MB
- SHA256:
- a1c7ccb287623cccb7c03150953b6d2a09dd95122933393c9151c3a60095c97e
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