Token Classification
GLiNER2
Safetensors
multilingual
English
extractor
Text classification
Intent classification
Sentiment Analysis
Topic classification
Named Entity Recognition
Instructions to use helmo/GLiNER2.5-multi-Decide with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use helmo/GLiNER2.5-multi-Decide with GLiNER2:
from gliner2 import AutoExtractor extractor = AutoExtractor.from_pretrained("helmo/GLiNER2.5-multi-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: 133 Bytes
db79b2f | 1 2 3 4 | version https://git-lfs.github.com/spec/v1
oid sha256:c62446df87ae18ec98b133f8f84fc449a07cc89bbf8ef192a4cb5f9c53777a7a
size 16035853
|