Instructions to use nicolasembleton/gliner2.5-multi-v1-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use nicolasembleton/gliner2.5-multi-v1-onnx with GLiNER2:
from gliner2 import GLiNER2 model = GLiNER2.from_pretrained("nicolasembleton/gliner2.5-multi-v1-onnx") # 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
| { | |
| "architecture": "boundary", | |
| "base_model": "fastino/gliner2.5-multi-v1", | |
| "hidden_size": 768, | |
| "opset": 17, | |
| "inputs": [ | |
| "input_ids", | |
| "attention_mask", | |
| "text_word_indices", | |
| "text_word_mask", | |
| "query_marker_indices", | |
| "query_marker_mask" | |
| ], | |
| "outputs": [ | |
| "start_logits", | |
| "end_logits" | |
| ], | |
| "notes": "Host must pack schema markers into input_ids and pass word/query gather indices. Decode spans from start/end logits in JS." | |
| } |