Token Classification
LiteRT
LiteRT
GLiNER2
multilingual
English
Japanese
android
named-entity-recognition
information-extraction
mdeberta-v3
japanese
Instructions to use litert-community/GLiNER2.5-Multi-LiteRT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use litert-community/GLiNER2.5-Multi-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-Multi-LiteRT with GLiNER2:
from gliner2 import AutoExtractor extractor = AutoExtractor.from_pretrained("litert-community/GLiNER2.5-Multi-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
Ctrl+K
GLiNER2.5 Multi for LiteRT: s128/s256/s512 wfp16 + fp32 graphs, fp16/fp32 host embedding tables, sparse decoder, Python host runtime, host contract, card
3bcde0b verified