Token Classification
GLiNER2
Safetensors
English
extractor
information-extraction
named-entity-recognition
relation-extraction
event-extraction
text-classification
Instructions to use whr778/gliner2-large-v1-wikievents with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use whr778/gliner2-large-v1-wikievents with GLiNER2:
from gliner2 import GLiNER2 model = GLiNER2.from_pretrained("whr778/gliner2-large-v1-wikievents") # 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
| { | |
| "_attn_implementation_autoset": true, | |
| "asl_clip": 0.05, | |
| "asl_gamma_neg": 4.0, | |
| "asl_gamma_pos": 1.0, | |
| "counting_layer": "count_lstm", | |
| "dice_smooth": 1.0, | |
| "event_struct_loss": null, | |
| "event_struct_pos_weight": null, | |
| "focal_alpha": 0.25, | |
| "focal_gamma": 2.0, | |
| "max_len": null, | |
| "max_width": 8, | |
| "model_name": "microsoft/deberta-v3-large", | |
| "model_type": "extractor", | |
| "struct_loss": "bce", | |
| "struct_pos_weight": 1.0, | |
| "token_pooling": "first", | |
| "transformers_version": "5.6.2", | |
| "use_moe": false | |
| } | |