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:
- 24db0ad7bfcaca9c5226fb8d822bf37b4ec4e22565a1cb72eb29076bad4186e6
- Size of remote file:
- 16 MB
- SHA256:
- c62446df87ae18ec98b133f8f84fc449a07cc89bbf8ef192a4cb5f9c53777a7a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.