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
Add decision-model tag
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by mishig HF Staff - opened
README.md
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- Sentiment Analysis
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- Topic classification
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- Named Entity Recognition
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base_model: fastino/gliner2.5-multi-v1
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---
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<div align="center">
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- Sentiment Analysis
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- Topic classification
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- Named Entity Recognition
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- decision-model
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base_model: fastino/gliner2.5-multi-v1
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<div align="center">
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