Text Classification
LiteRT
LiteRT
GLiNER2
English
android
deberta-v3
zero-shot-classification
decision-model
Instructions to use litert-community/GLiNER2.5-Decide-LiteRT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use litert-community/GLiNER2.5-Decide-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-Decide-LiteRT with GLiNER2:
from gliner2 import AutoExtractor extractor = AutoExtractor.from_pretrained("litert-community/GLiNER2.5-Decide-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
Add the s256 and s512 graphs for float16 hardware: gliner25_decide_s256_npu_wfp16.tflite (Qualcomm NPU) and gliner25_decide_s512_fp16hw_wfp16.tflite (GPU FP16 with FP32 accumulation), with their card rows, contract entries, NOTICE sentence, conversion scripts and hfmodels.json
600fe62 verified