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: 1,762 Bytes
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"model": "Pivot",
"contract": "unstructured_state_in__typed_probabilistic_decisions_out",
"state": "Customer dispute: invoice 120 vs PO 100, age=3d, region=US",
"decisions": [
{
"id": "route",
"primitive": "choice",
"description": null,
"options": [
"billing",
"tech",
"sales"
],
"index": 0,
"value": "billing",
"probs": {
"billing": 1.0,
"tech": 3.63460943200028e-13,
"sales": 2.232490754761063e-11
},
"prob_vector": [
1.0,
3.63460943200028e-13,
2.232490754761063e-11
],
"confidence": 1.0
},
{
"id": "approve",
"primitive": "noul",
"description": null,
"options": [
"true",
"false"
],
"index": 1,
"value": "false",
"probs": {
"true": 9.747503781909472e-07,
"false": 0.9999990463256836
},
"prob_vector": [
9.747503781909472e-07,
0.9999990463256836
],
"confidence": 0.9999990463256836,
"p_true": 9.747503781909472e-07
},
{
"id": "severity",
"primitive": "score",
"description": null,
"options": [
"0",
"1",
"2",
"3"
],
"index": 1,
"value": "1",
"probs": {
"0": 0.0008368478738702834,
"1": 0.9990244507789612,
"2": 0.0001381254114676267,
"3": 5.941537892795168e-07
},
"prob_vector": [
0.0008368478738702834,
0.9990244507789612,
0.0001381254114676267,
5.941537892795168e-07
],
"confidence": 0.9990244507789612,
"expected": 0.9993024840632643
}
],
"schema_version": "pivot-alpha-v1"
} |