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
- Xet hash:
- d65f699431919c2bc8be0b66ebe5906d7ac96fc06a5abf13681c7637d9de0931
- Size of remote file:
- 1.15 GB
- SHA256:
- 9efe0f88c99f2aa794452e9559dc60e98d60d9fa2bf1b60cf2710411b6da5b4e
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