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
Add decision-model tag
Browse filesHi fastino team,
This PR adds the `decision-model` tag to the model card metadata. GLiNER2.5-multi-Decide scores labels passed at call time in a single forward pass, the kind of model the Hub groups under this tag, so it shows up next to the other decision models: https://huggingface.co/models?other=decision-model
<img src="https://cdn-uploads.huggingface.co/production/uploads/noauth/gKtmJfAU6e4uyiBQRFjU7.png" alt="decision-model tag" width="500">
It's a one-line metadata change, nothing else in the card is touched.
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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