[Task] tabular-classification model support

[DingmaomaoBJTU]

Overview

Tabular classification models predict a categorical label from structured (tabular) data — rows of numeric and categorical features. These models are heavily used in enterprise and data analytics workflows. Unlike vision/NLP models, they typically use gradient-boosted tree or MLP architectures (e.g. XGBoost-backed ONNX exports, TabNet, FT-Transformer).

Target: top 2–8 models by HuggingFace downloads (>2k) covering representative architectures and business domains.

Agent Scenarios

• Risk / fraud detection agent: classify financial transactions as fraudulent or legitimate using structured account and transaction features
• Customer churn prediction agent: predict likelihood of customer churn from CRM feature tables to trigger retention workflows
• Medical diagnosis agent: classify patient records against structured clinical features (lab values, vitals, demographics)
• Lead scoring agent: rank and classify sales leads from CRM attributes to prioritize outreach

ModelKit Integration

Models must pass the full wmk pipeline on all EPs:

wmk config → wmk build (ONNX export) → wmk perf → wmk eval

Acceptance Criteria

• Identify top 2–8 tabular-classification models (>2k downloads) and add to model list
• All selected models pass wmk perf on CPU EP
• All selected models pass wmk eval with tabular dataset