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
File size: 432 Bytes
14bf8c2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | {
"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"
}
]
} |