Text Classification
Transformers
Safetensors
GLiNER2
English
Text classification
Intent classification
Sentiment Analysis
Topic classification
Named Entity Recognition
decision-model
Eval Results
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
Add decision-model tag (#10)
Browse files- Add decision-model tag (24793867a04d1f7d347b0ae525deffc1d0d75e72)
Co-authored-by: Victor Mustar <victor@users.noreply.huggingface.co>
README.md
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base_model: fastino/gliner2-large-v1
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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-large-v1
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