Token Classification
GLiNER2
Safetensors
multilingual
English
extractor
Text classification
Intent classification
Sentiment Analysis
Topic classification
Named Entity Recognition
Instructions to use fastino/GLiNER2.5-multi-Decide with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use fastino/GLiNER2.5-multi-Decide with GLiNER2:
from gliner2 import GLiNER2 model = GLiNER2.from_pretrained("fastino/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
Install with the local extra; tag as text-classification
#3
by bkinge - opened
The Install section says pip install gliner2, but the base profile is torch-free and the next line imports AutoExtractor, which fails without the local extra (see the install section in the GLiNER2 README). Also switch pipeline_tag from token-classification to text-classification so the Hub lists this classification checkpoint under the task it serves.