| # Humanizer-5M |
|
|
| Version: 2.0.0 |
|
|
| Humanizer-5M is a synthetic conversational adaptation dataset. |
|
|
| The central objective is not simply to rewrite text to sound casual. |
|
|
| Each example models: |
|
|
| 1. what the user explicitly asks, |
| 2. what the user may implicitly need, |
| 3. the user's conversational signals, |
| 4. the appropriate response calibration, |
| 5. the final response, |
| 6. quality and stability metrics. |
|
|
| The dataset specifically teaches proportional adaptation. |
|
|
| High user energy does not automatically mean high hype. |
| Frustration does not automatically mean excessive empathy. |
| Casual language does not automatically require slang. |
| Emojis are used only when contextually appropriate. |
|
|
| The dataset also contains hard negatives representing responses |
| that are grammatically correct but poorly calibrated. |
|
|
| ## Dataset size |
|
|
| Total: 5,000,000 |
|
|
| Train: 4,750,000 |
| Validation: 125,000 |
| Test: 125,000 |
|
|
| Shard size: 100,000 |
|
|
| ## Core dimensions |
|
|
| The dataset includes explicit calibration dimensions for: |
|
|
| - energy |
| - hype |
| - empathy |
| - warmth |
| - formality |
| - directness |
| - verbosity |
| - humor |
| - emoji usage |
| - technicality |
| - confidence |
| - urgency |
|
|
| ## Quality metrics |
|
|
| Examples also contain: |
|
|
| - naturalness |
| - human_likeness_proxy |
| - context_fit |
| - emotional_fit |
| - hype_fit |
| - empathy_fit |
| - emoji_fit |
| - verbosity_fit |
| - directness_fit |
| - formality_fit |
| - humor_fit |
| - technicality_fit |
| - consistency |
| - emotional_stability |
| - overreaction_penalty |
| - forced_human_penalty |
| - generic_ai_penalty |
| - repetition_penalty |
| - emoji_overuse_penalty |
| - hype_without_reason_penalty |
| - tone_jump_penalty |
| - overall_quality |
| |
| ## Hard negatives |
| |
| The dataset includes deliberately miscalibrated candidate responses. |
| |
| Examples include: |
| |
| - unnecessary hype |
| - forced empathy |
| - emoji overuse |
| - excessive verbosity |
| - generic assistant phrasing |
| - abrupt tone changes |
| |
| ## Important |
| |
| Metrics are synthetic training labels and calibration signals. |
| They are not empirical measurements of whether a response was |
| written by a human. |
| |
| The dataset does not contain private conversations. |
| |
