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
File size: 523 Bytes
2808b3e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | {
"_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
}
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