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
| { | |
| "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": 0.709779679775238, | |
| "tech": 0.013995149172842503, | |
| "sales": 0.27622511982917786 | |
| }, | |
| "prob_vector": [ | |
| 0.709779679775238, | |
| 0.013995149172842503, | |
| 0.27622511982917786 | |
| ], | |
| "confidence": 0.709779679775238 | |
| }, | |
| { | |
| "id": "approve", | |
| "primitive": "noul", | |
| "description": null, | |
| "options": [ | |
| "true", | |
| "false" | |
| ], | |
| "index": 1, | |
| "value": "false", | |
| "probs": { | |
| "true": 0.44199028611183167, | |
| "false": 0.5580097436904907 | |
| }, | |
| "prob_vector": [ | |
| 0.44199028611183167, | |
| 0.5580097436904907 | |
| ], | |
| "confidence": 0.5580097436904907, | |
| "p_true": 0.44199028611183167 | |
| }, | |
| { | |
| "id": "severity", | |
| "primitive": "score", | |
| "description": null, | |
| "options": [ | |
| "0", | |
| "1", | |
| "2", | |
| "3" | |
| ], | |
| "index": 3, | |
| "value": "3", | |
| "probs": { | |
| "0": 0.007189917378127575, | |
| "1": 0.22580669820308685, | |
| "2": 0.04488111659884453, | |
| "3": 0.7221222519874573 | |
| }, | |
| "prob_vector": [ | |
| 0.007189917378127575, | |
| 0.22580669820308685, | |
| 0.04488111659884453, | |
| 0.7221222519874573 | |
| ], | |
| "confidence": 0.7221222519874573, | |
| "expected": 2.4819356873631477 | |
| } | |
| ], | |
| "schema_version": "pivot-alpha-v1" | |
| } |