Instructions to use Palettetech/Palette_nlu_service with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Palettetech/Palette_nlu_service with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Palettetech/Palette_nlu_service")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Palettetech/Palette_nlu_service", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Palette_nlu_service — Persian Sales NLU
مدل تخصصی تشخیص Intent / Slot / Search-Query برای فروش فارسی.
- معماری: encoder مشترک + سرهای Intent / Slot / Query
- Output schema: structured JSON matching the Palette
INTENT_PROMPT
Intents (23)
PRODUCT_SEARCH, PRODUCT_PRICE, PRODUCT_DETAIL, PRODUCT_AVAILABILITY,
PRODUCT_RECOMMENDATION, PRODUCT_COMPARE, PRODUCT_IMAGE, NEGOTIATION,
ORDER_TRACKING, FAQ_*, GREETING, WELLNESS, THANKS, FAREWELL, HELP,
OUT_OF_SCOPE
Files
| Path | Description |
|---|---|
checkpoints/best.pt |
Trained weights |
configs/model_config.yaml |
Head sizes / encoder settings |
label_maps/ |
intent2id / slot2id |
tokenizer/ |
tokenizer assets |
Serve API
See the companion repo Palette_nlu_service — python scripts/serve.py.