Token Classification
LiteRT
LiteRT
GLiNER2
English
android
named-entity-recognition
information-extraction
deberta-v3
Instructions to use litert-community/GLiNER2.5-Small-LiteRT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use litert-community/GLiNER2.5-Small-LiteRT with LiteRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- GLiNER2
How to use litert-community/GLiNER2.5-Small-LiteRT with GLiNER2:
from gliner2 import AutoExtractor extractor = AutoExtractor.from_pretrained("litert-community/GLiNER2.5-Small-LiteRT") # 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
Commit History
-
Add android/ (Kotlin host + Compose sample), switch the worked example to an invented-name sentence
Verified signaturef7b39d3 -
Add GLiNER2.5 Small LiteRT graphs (windows 128/256/512), host assets and card
Verified signature8c4759d