Feature Extraction
Transformers
Safetensors
pivot
decision-making
classification
scoring
custom_code
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
| { | |
| "context": "Customer dispute: invoice 120 vs PO 100, age=3d, region=US", | |
| "candidates": [ | |
| { | |
| "id": "billing", | |
| "text": "route to billing support" | |
| }, | |
| { | |
| "id": "technical", | |
| "text": "route to technical support" | |
| }, | |
| { | |
| "id": "sales", | |
| "text": "route to sales" | |
| }, | |
| { | |
| "id": "none", | |
| "text": "none of these routes is appropriate", | |
| "kind": "abstain" | |
| } | |
| ] | |
| } |