Instructions to use rafmacalaba/gliner2-datause-large-v15 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use rafmacalaba/gliner2-datause-large-v15 with GLiNER2:
from gliner2 import GLiNER2 model = GLiNER2.from_pretrained("rafmacalaba/gliner2-datause-large-v15") # 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
| { | |
| "adapter_type": "lora", | |
| "adapter_version": "1.0", | |
| "lora_r": 16, | |
| "lora_alpha": 32, | |
| "lora_dropout": 0.1, | |
| "target_modules": [ | |
| "classifier", | |
| "count_embed", | |
| "count_pred", | |
| "encoder", | |
| "span_rep" | |
| ], | |
| "created_at": "2026-03-30T13:38:25.613632Z" | |
| } |