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
Download config.json from Deepdive404-3/GLiNER2.5-Decide: direct link, hf CLI and curl.
- Browser
- Download file 594 Bytes
-
https://huggingface.co/Deepdive404-3/GLiNER2.5-Decide/resolve/main/config.json
- Command line
-
hf download hf://Deepdive404-3/GLiNER2.5-Decide/config.json
-
curl -L -o config.json https://huggingface.co/Deepdive404-3/GLiNER2.5-Decide/resolve/main/config.json
594 Bytes
| { | |
| "_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 | |
| } | |