Text Classification
GLiNER2
Safetensors
English
code
extractor
decision-model
typed-decisions
system-one
code-review
code-quality
javascript
typescript
deberta-v3
Eval Results (legacy)
Eval Results
Instructions to use yannelli/gliner2.5-decide-code-review-decisions with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use yannelli/gliner2.5-decide-code-review-decisions with GLiNER2:
from gliner2 import AutoExtractor extractor = AutoExtractor.from_pretrained("yannelli/gliner2.5-decide-code-review-decisions") # 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
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