Transformers
Safetensors
GLiNER2
multilingual
English
Text classification
Intent classification
Sentiment Analysis
Topic classification
Named Entity Recognition
decision-model
schema-extraction
Instructions to use fastino/GLiNER2.5-multi-Decide with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fastino/GLiNER2.5-multi-Decide with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import Gliner2ForSchemaExtraction model = Gliner2ForSchemaExtraction.from_pretrained("fastino/GLiNER2.5-multi-Decide", device_map="auto") - GLiNER2
How to use fastino/GLiNER2.5-multi-Decide with GLiNER2:
from gliner2 import AutoExtractor extractor = AutoExtractor.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
Supported languages
👀 1
#5 opened 11 days ago
by
jglowa
Demo for this model on Spaces
🔥 1
#4 opened 14 days ago
by
multimodalart
Install with the local extra; tag as text-classification
1
#3 opened 14 days ago
by
bkinge