priteshloke

feat(model): publish Kepler Ops Anomaly Classifier architecture & model card

3f1b101 4 days ago

1.92 kB

language:
  - en
license: mit
tags:
  - text-classification
  - sequence-classification
  - enterprise-operations
  - logistics
  - finops
  - anomaly-detection
datasets:
  - priteshloke/enterprise-operations-benchmark
pipeline_tag: text-classification
widget:
  - text: >-
      Carrier BlueDart billed 3.5 kg on a 0.5 kg t-shirt box due to dimensional
      laser scanner bulge.
    example_title: Volumetric Freight Overcharge
  - text: >-
      Client logged 68 hours on a contracted 40-hour monthly retainer with zero
      change orders.
    example_title: Agency Retainer Scope Creep
  - text: >-
      Departed marketing contractor retains paid Google Workspace and Slack
      licenses for 90 days.
    example_title: SaaS Zombie Seat Waste

🤖 Kepler Ops — Enterprise Anomaly & Exception Classifier

Author: Kepler Operations Intelligence (getkeplerops.com)

This model classifies transaction narratives, invoice discrepancy notes, and operational logs into standard enterprise failure modes.

📊 Target Classes (6 Exception Categories)

VOLUMETRIC_WEIGHT_OVERCHARGE_CRITICAL
COURIER_FAKE_NDR_ATTEMPT_CRITICAL
AGENCY_RETAINER_SCOPE_CREEP_CRITICAL
SAAS_DORMANT_SEAT_LICENSE_WASTE_HIGH
FBA_AGED_INVENTORY_SURCHARGE_CRITICAL
NONE_CLEAN_TRANSACTION

💻 Quick Python Usage

from transformers import pipeline

classifier = pipeline("text-classification", model="priteshloke/kepler-ops-anomaly-classifier")
result = classifier("Carrier billed 3.5 kg on a 0.5 kg shipment.")
print(result)

🚀 Live Production Engine & Benchmarks

Web Platform: getkeplerops.com
Interactive Simulator: Hugging Face Space
Evaluation Dataset: priteshloke/enterprise-operations-benchmark