Text Classification
GLiNER2
Safetensors
English
decision-model
calibration
selective-prediction
conformal-prediction
abstention
risk-aware
security
cybersecurity
security-operations
soc
pentest
red-team
blue-team
Eval Results (legacy)
Instructions to use TyrusRC/haruspex with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use TyrusRC/haruspex with GLiNER2:
from gliner2 import AutoExtractor extractor = AutoExtractor.from_pretrained("TyrusRC/haruspex") # Extract entities text = "Apple CEO Tim Cook announced iPhone 15 in Cupertino yesterday." result = extractor.extract_entities(text, ["company", "person", "product", "location"]) print(result) - Notebooks
- Google Colab
- Kaggle
File size: 396 Bytes
a04efa5 697293c a04efa5 697293c a04efa5 697293c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | {
"model_type": "gliner2",
"library_name": "gliner2",
"base_model": "fastino/GLiNER2.5-Decide",
"pipeline_tag": "text-classification",
"haruspex": {
"contract": [
"/v1/evaluate",
"/v1/systemone"
],
"decision_types": [
"boolean",
"choice",
"score"
],
"adapter": "final",
"calibrator": "calibrator.json",
"cve_routing": true
}
} |