Instructions to use litert-community/GLiClass-Edge-v3.0-LiteRT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use litert-community/GLiClass-Edge-v3.0-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
- Notebooks
- Google Colab
- Kaggle
File size: 941 Bytes
88c9095 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | """The model card's Python example: one invented support ticket, single-label and multi-label.
Run from the repository root: python examples/run_example.py
"""
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
from gliclass_litert import GliclassLiteRT # noqa: E402
text = (
"The Zorvik X2 router from Tallowmere Networks keeps dropping the connection every evening "
"around 9 pm. I have restarted it and reset it to factory settings, but the status light "
"still blinks orange."
)
labels = ["connectivity problem", "hardware defect", "billing issue", "feature request",
"installation help"]
with GliclassLiteRT(Path(__file__).resolve().parents[1]) as classifier:
for mode in ("single-label", "multi-label"):
result = classifier.classify(text, labels, classification_type=mode)
print(mode, [(r["label"], round(r["score"], 3)) for r in result])
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