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:
- a1875a52bfe3eef2a7b0beded95eefb7ed2457fda45cd8ea217e89e310883793
- Size of remote file:
- 858 kB
- SHA256:
- 428f13aa08f1aa12370c4a644f235ddc46db85e429a1dee6a8d22ead095f2183
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