Token Classification
GLiNER2
Safetensors
English
extractor
Text classification
Intent classification
Sentiment Analysis
Topic classification
Named Entity Recognition
Instructions to use Deepdive404-3/GLiNER2.5-Decide with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use Deepdive404-3/GLiNER2.5-Decide with GLiNER2:
from gliner2 import AutoExtractor extractor = AutoExtractor.from_pretrained("Deepdive404-3/GLiNER2.5-Decide") # 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: 594 Bytes
03d8809 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | {
"_attn_implementation_autoset": true,
"_name_or_path": "/home/urchadezaratiana/checkpoints/checkpoint-45500",
"architecture": "span",
"architecture_version": 1,
"architectures": [
"SpanExtractor"
],
"attn_implementation": "sdpa",
"config_version": 3,
"counting_layer": "count_lstm",
"max_len": null,
"max_width": 8,
"model_name": "microsoft/deberta-v3-large",
"model_type": "extractor",
"span_head": {
"dropout": 0.1,
"max_width": 8,
"span_mode": "markerV0"
},
"token_pooling": "first",
"transformers_version": "4.48.1",
"use_moe": false
}
|