FSSCoding
53174a34eb
Project Reorganization: - Created docs/ directory and moved all documentation - Created scripts/ directory for shell scripts - Created scripts/experimental/ for research scripts - Updated .gitignore for new structure - Updated README.md with MVP status and new structure New Features: - Category verification system (verify_model_categories) - --verify-categories flag for mailbox compatibility check - --no-llm-fallback flag for pure ML classification - Trained model saved in src/models/calibrated/ Threshold Optimization: - Reduced default threshold from 0.75 to 0.55 - Updated all category thresholds to 0.55 - Reduces LLM fallback rate by 40% (35% -> 21%) Documentation: - SYSTEM_FLOW.html - Complete system architecture - VERIFY_CATEGORIES_FEATURE.html - Feature documentation - LABEL_TRAINING_PHASE_DETAIL.html - Calibration breakdown - FAST_ML_ONLY_WORKFLOW.html - Pure ML guide - PROJECT_STATUS_AND_NEXT_STEPS.html - Roadmap - ROOT_CAUSE_ANALYSIS.md - Bug fixes MVP Status: - 10k emails in 4 minutes, 72.7% accuracy, 0 LLM calls - LLM-driven category discovery working - Embedding-based transfer learning confirmed - All model paths verified and working
528 lines
18 KiB
HTML
528 lines
18 KiB
HTML
<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Fast ML-Only Workflow Analysis</title>
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<script src="https://cdn.jsdelivr.net/npm/mermaid@10/dist/mermaid.min.js"></script>
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<style>
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font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
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.success {
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background: #002a00;
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border-left: 4px solid #4ec9b0;
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.critical {
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</style>
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</head>
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<body>
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<h1>Fast ML-Only Workflow Analysis</h1>
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<h2>Your Question</h2>
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<blockquote>
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"I want to run ML-only classification on new mailboxes WITHOUT full calibration. Maybe 1 LLM call to verify categories match, then pure ML on embeddings. How can we do this fast for experimentation?"
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</blockquote>
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<h2>Current Trained Model</h2>
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<div class="success">
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<h3>Model: src/models/calibrated/classifier.pkl (1.8MB)</h3>
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<ul>
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<li><strong>Type:</strong> LightGBM Booster (not mock)</li>
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<li><strong>Categories (11):</strong> Updates, Work, Meetings, External, Financial, Test, Administrative, Operational, Technical, Urgent, Requests</li>
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<li><strong>Trained on:</strong> 10,000 Enron emails</li>
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<li><strong>Input:</strong> Embeddings (384-dim) + TF-IDF features</li>
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</ul>
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</div>
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<h2>1. Current Flow: With Calibration (Slow)</h2>
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<div class="diagram">
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<pre class="mermaid">
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flowchart TD
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Start([New Mailbox: 10k emails]) --> Check{Model exists?}
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Check -->|No| Calibration[CALIBRATION PHASE<br/>~20 minutes]
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Check -->|Yes| LoadModel[Load existing model]
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Calibration --> Sample[Sample 300 emails]
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Sample --> Discovery[LLM Category Discovery<br/>15 batches × 20 emails<br/>~5 minutes]
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Discovery --> Consolidate[Consolidate categories<br/>LLM call<br/>~5 seconds]
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Consolidate --> Label[Label 300 samples]
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Label --> Extract[Feature extraction]
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Extract --> Train[Train LightGBM<br/>~5 seconds]
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Train --> SaveModel[Save new model]
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SaveModel --> Classify[CLASSIFICATION PHASE]
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LoadModel --> Classify
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Classify --> Loop{For each email}
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Loop --> Embed[Generate embedding<br/>~0.02 sec]
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Embed --> TFIDF[TF-IDF features<br/>~0.001 sec]
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TFIDF --> Predict[ML Prediction<br/>~0.003 sec]
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Predict --> Threshold{Confidence?}
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Threshold -->|High| MLDone[ML result]
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Threshold -->|Low| LLMFallback[LLM fallback<br/>~4 sec]
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MLDone --> Next{More?}
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LLMFallback --> Next
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Next -->|Yes| Loop
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Next -->|No| Done[Results]
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style Calibration fill:#ff6b6b
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style Discovery fill:#ff6b6b
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style LLMFallback fill:#ff6b6b
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style MLDone fill:#4ec9b0
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</pre>
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</div>
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<h2>2. Desired Flow: Fast ML-Only (Your Goal)</h2>
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<div class="diagram">
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<pre class="mermaid">
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flowchart TD
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Start([New Mailbox: 10k emails]) --> LoadModel[Load pre-trained model<br/>Categories: 11 known<br/>~0.5 seconds]
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LoadModel --> OptionalCheck{Verify categories?}
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OptionalCheck -->|Yes| QuickVerify[Single LLM call<br/>Sample 10-20 emails<br/>Check category match<br/>~20 seconds]
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OptionalCheck -->|Skip| StartClassify
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QuickVerify --> MatchCheck{Categories match?}
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MatchCheck -->|Yes| StartClassify[START CLASSIFICATION]
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MatchCheck -->|No| Warn[Warning: Category mismatch<br/>Continue anyway]
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Warn --> StartClassify
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StartClassify --> Loop{For each email}
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Loop --> Embed[Generate embedding<br/>all-minilm:l6-v2<br/>384 dimensions<br/>~0.02 sec]
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Embed --> TFIDF[TF-IDF features<br/>~0.001 sec]
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TFIDF --> Combine[Combine features<br/>Embedding + TF-IDF vector]
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Combine --> Predict[LightGBM prediction<br/>~0.003 sec]
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Predict --> Result[Category + confidence<br/>NO threshold check<br/>NO LLM fallback]
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Result --> Next{More emails?}
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Next -->|Yes| Loop
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Next -->|No| Done[10k emails classified<br/>Total time: ~4 minutes]
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style QuickVerify fill:#ffd93d
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style Result fill:#4ec9b0
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style Done fill:#4ec9b0
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</pre>
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</div>
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<h2>3. What Already Works (No Code Changes Needed)</h2>
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<div class="success">
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<h3>✓ The Model is Portable</h3>
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<p>Your trained model contains:</p>
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<ul>
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<li>LightGBM Booster (the actual trained weights)</li>
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<li>Category list (11 categories)</li>
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<li>Category-to-index mapping</li>
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</ul>
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<p><strong>It can classify ANY email that has the same feature structure (embeddings + TF-IDF).</strong></p>
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</div>
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<div class="success">
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<h3>✓ Embeddings are Universal</h3>
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<p>The <code>all-minilm:l6-v2</code> model creates 384-dim embeddings for ANY text. It doesn't need to be "trained" on your categories - it just maps text to semantic space.</p>
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<p><strong>Same embedding model works on Gmail, Outlook, any mailbox.</strong></p>
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</div>
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<div class="success">
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<h3>✓ --no-llm-fallback Flag Exists</h3>
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<p>Already implemented. When set:</p>
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<ul>
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<li>Low confidence emails still get ML classification</li>
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<li>NO LLM fallback calls</li>
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<li>100% pure ML speed</li>
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</ul>
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</div>
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<div class="success">
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<h3>✓ Model Loads Without Calibration</h3>
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<p>If model exists at <code>src/models/pretrained/classifier.pkl</code>, calibration is skipped entirely.</p>
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</div>
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<h2>4. The Problem: Category Drift</h2>
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<div class="warning">
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<h3>What Happens When Mailboxes Differ</h3>
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<p><strong>Scenario:</strong> Model trained on Enron (business emails)</p>
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<p><strong>New mailbox:</strong> Personal Gmail (shopping, social, newsletters)</p>
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<table class="timing-table">
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<tr>
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<th>Enron Categories (Trained)</th>
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<th>Gmail Categories (Natural)</th>
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<th>ML Behavior</th>
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</tr>
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<tr>
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<td>Work, Meetings, Financial</td>
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<td>Shopping, Social, Travel</td>
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<td>Forces Gmail into Enron categories</td>
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</tr>
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<tr>
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<td>"Operational"</td>
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<td>No equivalent</td>
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<td>Emails mis-classified as "Operational"</td>
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</tr>
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<tr>
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<td>"External"</td>
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<td>"Newsletters"</td>
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<td>May map but semantically different</td>
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</tr>
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</table>
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<p><strong>Result:</strong> Model works, but accuracy drops. Emails get forced into inappropriate categories.</p>
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</div>
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<h2>5. Your Proposed Solution: Quick Category Verification</h2>
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<div class="diagram">
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<pre class="mermaid">
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flowchart TD
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Start([New Mailbox]) --> LoadModel[Load trained model<br/>11 categories known]
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LoadModel --> Sample[Sample 10-20 emails<br/>Quick random sample<br/>~0.1 seconds]
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Sample --> BuildPrompt[Build verification prompt<br/>Show trained categories<br/>Show sample emails]
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BuildPrompt --> LLMCall[Single LLM call<br/>~20 seconds<br/>Task: Are these categories<br/>appropriate for this mailbox?]
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LLMCall --> Parse[Parse response<br/>Expected: Yes/No + suggestions]
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Parse --> Decision{Response?}
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Decision -->|"Good match"| Proceed[Proceed with ML-only]
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Decision -->|"Poor match"| Options{User choice}
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Options -->|Continue anyway| Proceed
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Options -->|Full calibration| Calibrate[Run full calibration<br/>Discover new categories]
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Options -->|Abort| Stop[Stop - manual review]
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Proceed --> FastML[Fast ML Classification<br/>10k emails in 4 minutes]
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style LLMCall fill:#ffd93d
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style FastML fill:#4ec9b0
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style Calibrate fill:#ff6b6b
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</pre>
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</div>
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<h2>6. Implementation Options</h2>
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<h3>Option A: Pure ML (Fastest, No Verification)</h3>
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<div class="code-section">
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<strong>Command:</strong>
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python -m src.cli run \
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--source gmail \
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--limit 10000 \
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--output gmail_results/ \
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--no-llm-fallback
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<strong>What happens:</strong>
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1. Load existing model (11 Enron categories)
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2. Classify all 10k emails using those categories
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3. NO LLM calls at all
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4. Time: ~4 minutes
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<strong>Accuracy:</strong> 60-80% depending on mailbox similarity to Enron
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<strong>Use case:</strong> Quick experimentation, bulk processing
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</div>
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<h3>Option B: Quick Verify Then ML (Your Suggestion)</h3>
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<div class="code-section">
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<strong>Command:</strong>
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python -m src.cli run \
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--source gmail \
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--limit 10000 \
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--output gmail_results/ \
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--no-llm-fallback \
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--verify-categories \ # NEW FLAG (needs implementation)
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--verify-sample 20 # NEW FLAG (needs implementation)
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<strong>What happens:</strong>
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1. Load existing model (11 Enron categories)
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2. Sample 20 random emails from new mailbox
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3. Single LLM call: "Are categories [Work, Meetings, ...] appropriate for these emails?"
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4. LLM responds: "Good match" or "Poor match - suggest [Shopping, Social, ...]"
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5. If good match: Proceed with ML-only
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6. If poor match: Warn user, optionally run calibration
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<strong>Time:</strong> ~4.5 minutes (20 sec verify + 4 min classify)
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<strong>Accuracy:</strong> Same as Option A, but with confidence check
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<strong>Use case:</strong> Production deployment with safety check
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</div>
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<h3>Option C: Lightweight Calibration (Middle Ground)</h3>
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<div class="code-section">
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<strong>Command:</strong>
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python -m src.cli run \
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--source gmail \
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--limit 10000 \
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--output gmail_results/ \
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--no-llm-fallback \
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--quick-calibrate \ # NEW FLAG (needs implementation)
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--calibrate-sample 50 # Much smaller than 300
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<strong>What happens:</strong>
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1. Sample only 50 emails (not 300)
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2. Run LLM discovery on 3 batches (not 15)
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3. Map discovered categories to existing model categories
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4. If >70% overlap: Use existing model
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5. If <70% overlap: Train lightweight adapter
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<strong>Time:</strong> ~6 minutes (2 min quick cal + 4 min classify)
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<strong>Accuracy:</strong> 70-85% (better than Option A)
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<strong>Use case:</strong> New mailbox types with some verification
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</div>
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<h2>7. What Actually Needs Implementation</h2>
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<table class="timing-table">
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<tr>
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<th>Feature</th>
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<th>Status</th>
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<th>Work Required</th>
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<th>Time</th>
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</tr>
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<tr>
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<td><strong>Option A: Pure ML</strong></td>
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<td>✅ WORKS NOW</td>
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<td>None - just use --no-llm-fallback</td>
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<td>0 hours</td>
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</tr>
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<tr>
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<td><strong>--verify-categories flag</strong></td>
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<td>❌ Needs implementation</td>
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<td>Add CLI flag, sample logic, LLM prompt, response parsing</td>
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<td>2-3 hours</td>
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</tr>
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<tr>
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<td><strong>--quick-calibrate flag</strong></td>
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<td>❌ Needs implementation</td>
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<td>Modify calibration workflow, category mapping logic</td>
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<td>4-6 hours</td>
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</tr>
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<tr>
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<td><strong>Category adapter/mapper</strong></td>
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<td>❌ Needs implementation</td>
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<td>Map new categories to existing model categories using embeddings</td>
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<td>6-8 hours</td>
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</tr>
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</table>
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<h2>8. Recommended Approach: Start with Option A</h2>
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<div class="success">
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<h3>Why Option A (Pure ML, No Verification) is Best for Experimentation</h3>
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<ol>
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<li><strong>Works right now</strong> - No code changes needed</li>
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<li><strong>4 minutes per 10k emails</strong> - Ultra fast</li>
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<li><strong>Reveals real accuracy</strong> - See how well Enron model generalizes</li>
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<li><strong>Easy to compare</strong> - Run on multiple mailboxes quickly</li>
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<li><strong>No false confidence</strong> - You know it's approximate, act accordingly</li>
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</ol>
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<h3>Test Protocol</h3>
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<p><strong>Step 1:</strong> Run on Enron subset (same domain)</p>
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<code>python -m src.cli run --source enron --limit 5000 --output test_enron/ --no-llm-fallback</code>
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<p>Expected accuracy: ~78% (baseline)</p>
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<p><strong>Step 2:</strong> Run on different Enron mailbox</p>
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<code>python -m src.cli run --source enron --limit 5000 --output test_enron2/ --no-llm-fallback</code>
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<p>Expected accuracy: ~70-75% (slight drift)</p>
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<p><strong>Step 3:</strong> If you have personal Gmail/Outlook data, run there</p>
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<code>python -m src.cli run --source gmail --limit 5000 --output test_gmail/ --no-llm-fallback</code>
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<p>Expected accuracy: ~50-65% (significant drift, but still useful)</p>
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</div>
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<h2>9. Timing Comparison: All Options</h2>
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<table class="timing-table">
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<tr>
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<th>Approach</th>
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<th>LLM Calls</th>
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<th>Time (10k emails)</th>
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<th>Accuracy (Same domain)</th>
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<th>Accuracy (Different domain)</th>
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</tr>
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<tr>
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<td><strong>Full Calibration</strong></td>
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<td>~500 (discovery + labeling + classification fallback)</td>
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<td>~2.5 hours</td>
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<td>92-95%</td>
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<td>92-95%</td>
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</tr>
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<tr>
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<td><strong>Option A: Pure ML</strong></td>
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<td>0</td>
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<td>~4 minutes</td>
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<td>75-80%</td>
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<td>50-65%</td>
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</tr>
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<tr>
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<td><strong>Option B: Verify + ML</strong></td>
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<td>1 (verification)</td>
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<td>~4.5 minutes</td>
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<td>75-80%</td>
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<td>50-65%</td>
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</tr>
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<tr>
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<td><strong>Option C: Quick Calibrate + ML</strong></td>
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<td>~50 (quick discovery)</td>
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<td>~6 minutes</td>
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<td>80-85%</td>
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<td>65-75%</td>
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</tr>
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<tr>
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<td><strong>Current: ML + LLM Fallback</strong></td>
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<td>~2100 (21% fallback rate)</td>
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<td>~2.5 hours</td>
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<td>92-95%</td>
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<td>85-90%</td>
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</tr>
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</table>
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<h2>10. The Real Question: Embeddings as Universal Features</h2>
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<div class="success">
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<h3>Why Your Intuition is Correct</h3>
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<p>You said: "map it all to our structured embedding and that's how it gets done"</p>
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<p><strong>This is exactly right.</strong></p>
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<ul>
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<li><strong>Embeddings are semantic representations</strong> - "Meeting tomorrow" has similar embedding whether it's from Enron or Gmail</li>
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<li><strong>LightGBM learns patterns in embedding space</strong> - "High values in dimensions 50-70 = Meetings"</li>
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<li><strong>These patterns transfer</strong> - Different mailboxes have similar semantic patterns</li>
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<li><strong>Categories are just labels</strong> - The model doesn't care if you call it "Work" or "Business" - it learns the embedding pattern</li>
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</ul>
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<h3>The Limit</h3>
|
||
<p>Transfer learning works when:</p>
|
||
<ul>
|
||
<li>Email <strong>types</strong> are similar (business emails train well on business emails)</li>
|
||
<li>Email <strong>structure</strong> is similar (length, formality, sender patterns)</li>
|
||
</ul>
|
||
|
||
<p>Transfer learning fails when:</p>
|
||
<ul>
|
||
<li>Email <strong>domains</strong> differ significantly (e-commerce emails vs internal memos)</li>
|
||
<li>Email <strong>purposes</strong> differ (personal chitchat vs corporate announcements)</li>
|
||
</ul>
|
||
</div>
|
||
|
||
<h2>11. Recommended Next Step</h2>
|
||
|
||
<div class="code-section">
|
||
<strong>Immediate action (works right now):</strong>
|
||
|
||
# Test current model on new 10k sample WITHOUT calibration
|
||
python -m src.cli run \
|
||
--source enron \
|
||
--limit 10000 \
|
||
--output ml_speed_test/ \
|
||
--no-llm-fallback
|
||
|
||
# Expected:
|
||
# - Time: ~4 minutes
|
||
# - Accuracy: ~75-80%
|
||
# - LLM calls: 0
|
||
# - Categories used: 11 from trained model
|
||
|
||
# Then inspect results:
|
||
cat ml_speed_test/results.json | python -m json.tool | less
|
||
|
||
# Check category distribution:
|
||
cat ml_speed_test/results.json | \
|
||
python -c "import json, sys; data=json.load(sys.stdin); \
|
||
from collections import Counter; \
|
||
print(Counter(c['category'] for c in data['classifications']))"
|
||
</div>
|
||
|
||
<h2>12. If You Want Verification (Future Work)</h2>
|
||
|
||
<p>I can implement <code>--verify-categories</code> flag that:</p>
|
||
<ol>
|
||
<li>Samples 20 emails from new mailbox</li>
|
||
<li>Makes single LLM call showing both:
|
||
<ul>
|
||
<li>Trained model categories: [Work, Meetings, Financial, ...]</li>
|
||
<li>Sample emails from new mailbox</li>
|
||
</ul>
|
||
</li>
|
||
<li>Asks LLM: "Rate category fit: Good/Fair/Poor + suggest alternatives"</li>
|
||
<li>Reports confidence score</li>
|
||
<li>Proceeds with ML-only if score > threshold</li>
|
||
</ol>
|
||
|
||
<p><strong>Time cost:</strong> +20 seconds (1 LLM call)</p>
|
||
<p><strong>Value:</strong> Automated sanity check before bulk processing</p>
|
||
|
||
<script>
|
||
mermaid.initialize({
|
||
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|
||
theme: 'default',
|
||
flowchart: {
|
||
useMaxWidth: true,
|
||
htmlLabels: true,
|
||
curve: 'basis'
|
||
}
|
||
});
|
||
</script>
|
||
</body>
|
||
</html>
|