Token Classification
GLiNER2
Safetensors
multilingual
English
extractor
Text classification
Named Entity Recognition
Relation Extraction
Intent classification
Sentiment Analysis
Topic classification
Structured extraction
Json extraction
information-extraction
boundary-extraction
Instructions to use fastino/gliner2.5-multi-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use fastino/gliner2.5-multi-v1 with GLiNER2:
from gliner2 import GLiNER2 model = GLiNER2.from_pretrained("fastino/gliner2.5-multi-v1") # 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:
- 062b18d942f04ce937d1787c02f7a333e13191b7fd36b758c7fbe1f6ffaabcae
- Size of remote file:
- 1.15 GB
- SHA256:
- c1ff4ec0bc00031c15530b8f3c33d3677f27949e6a0cb52e1247a6224b6c5395
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