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
File size: 502 Bytes
2d8be88 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | {
"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"
]
}
]
} |