Token Classification
GLiNER2
ONNX
GLiNER
pii
ner
privacy
redaction
information-extraction
span-extraction
Instructions to use yethdev/gliner2-pii-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use yethdev/gliner2-pii-onnx with GLiNER2:
from gliner2 import GLiNER2 model = GLiNER2.from_pretrained("yethdev/gliner2-pii-onnx") # 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 yethdev/gliner2-pii-onnx with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("yethdev/gliner2-pii-onnx") - Notebooks
- Google Colab
- Kaggle
GLiNER2-PII ONNX
This model is meant for use within the Redacta chrome extension, and this ONNX export was specifically meant for browser use, removing dependencies and preserving behavior.
GLINER2-PII ONNX is 2.75x smaller than the original GLINER2-PII with nearing performance.
Benchmark Results (SPY)
Evaluated on the SPY benchmark (Savkin et al., 2025) with exact-match span-level metrics:
| Model | Legal P | Legal R | Legal F1 | Medical P | Medical R | Medical F1 | Avg F1 |
|---|---|---|---|---|---|---|---|
| yethdev/gliner2-pii-onnx | .383 | .861 | .530 | .396 | .846 | .539 | .535 |
| fastino/gliner2-pii-v1 | .346 | .750 | .473 | .369 | .686 | .480 | .477 |
| nvidia/gliner-PII | .343 | .452 | .390 | .368 | .465 | .411 | .400 |
| urchade/gliner_multi_pii-v1 | .467 | .317 | .377 | .518 | .351 | .419 | .398 |
| openai/privacy-filter | .242 | .656 | .354 | .287 | .692 | .406 | .380 |
License & attribution
Apache-2.0 - All model weights are derived from fastino/gliner2-privacy-filter-PII-multi by Fastino Labs.
Model tree for yethdev/gliner2-pii-onnx
Base model
fastino/gliner2-privacy-filter-PII-multi