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 mobile/eval.json from TyrusRC/haruspex: direct link, hf CLI and curl.
- Browser
- Download file 109 Bytes
-
https://huggingface.co/TyrusRC/haruspex/resolve/main/mobile/eval.json
- Command line
-
hf download hf://TyrusRC/haruspex/mobile/eval.json
-
curl -L -o eval.json https://huggingface.co/TyrusRC/haruspex/resolve/main/mobile/eval.json
109 Bytes
| { | |
| "accuracy": 0.9466, | |
| "ece": 0.0529, | |
| "brier": 0.0523, | |
| "test_n": 131, | |
| "dataset": "mobile held-out" | |
| } |