Feature Extraction
Transformers
Safetensors
English
pivot
custom_code
decision-making
classification
routing
scoring
Instructions to use Q1z/Pivot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Q1z/Pivot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Q1z/Pivot", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Q1z/Pivot", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "state": "Customer dispute: invoice 120 vs PO 100, age=3d, region=US", | |
| "questions": [ | |
| { | |
| "id": "route", | |
| "primitive": "choice", | |
| "options": [ | |
| "billing", | |
| "tech", | |
| "sales" | |
| ] | |
| }, | |
| { | |
| "id": "approve", | |
| "primitive": "noul", | |
| "options": [ | |
| "true", | |
| "false" | |
| ] | |
| }, | |
| { | |
| "id": "severity", | |
| "primitive": "score", | |
| "options": [ | |
| "0", | |
| "1", | |
| "2", | |
| "3" | |
| ] | |
| } | |
| ] | |
| } |