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
Download config.json from TyrusRC/haruspex: direct link, hf CLI and curl.
- Browser
- Download file 396 Bytes
-
https://huggingface.co/TyrusRC/haruspex/resolve/main/config.json
- Command line
-
hf download hf://TyrusRC/haruspex/config.json
-
curl -L -o config.json https://huggingface.co/TyrusRC/haruspex/resolve/main/config.json
396 Bytes
| { | |
| "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 | |
| } | |
| } |