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: 1,046 Bytes
14bf8c2 | 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 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | {
"schema_version": "pivot-native-v1",
"contract": "context_in__closed_semantic_candidates_out",
"closed_set": true,
"probability_scope": "supplied_candidates_only",
"selected": {
"id": "billing",
"text": "route to billing support",
"kind": "candidate",
"prob": 1.0
},
"index": 0,
"relative_confidence": 1.0,
"abstained": false,
"candidates": [
{
"id": "billing",
"text": "route to billing support",
"kind": "candidate",
"prob": 1.0
},
{
"id": "technical",
"text": "route to technical support",
"kind": "candidate",
"prob": 1.4294558425831383e-08
},
{
"id": "sales",
"text": "route to sales",
"kind": "candidate",
"prob": 1.0551385321022622e-09
},
{
"id": "none",
"text": "none of these routes is appropriate",
"kind": "abstain",
"prob": 6.683787908984967e-15
}
],
"prob_vector": [
1.0,
1.4294558425831383e-08,
1.0551385321022622e-09,
6.683787908984967e-15
]
} |