Text Classification
Transformers
Safetensors
GLiNER2
English
Text classification
Intent classification
Sentiment Analysis
Topic classification
Named Entity Recognition
decision-model
Instructions to use fastino/GLiNER2.5-Decide with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fastino/GLiNER2.5-Decide with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fastino/GLiNER2.5-Decide")# pip install -U transformers accelerate # Load model directly from transformers import Gliner2ForSchemaExtraction model = Gliner2ForSchemaExtraction.from_pretrained("fastino/GLiNER2.5-Decide", device_map="auto") - GLiNER2
How to use fastino/GLiNER2.5-Decide with GLiNER2:
from gliner2 import AutoExtractor extractor = AutoExtractor.from_pretrained("fastino/GLiNER2.5-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) - Inference
- Notebooks
- Google Colab
- Kaggle
Tag as text-classification (pipeline_tag only)
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by kzfastino - opened
README.md
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license: apache-2.0
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language:
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- gliner2
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- Text classification
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license: apache-2.0
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pipeline_tag: text-classification
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tags:
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- gliner2
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- Text classification
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