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Upload Humanizer-5M v2.0.0

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  1. README.md +101 -0
  2. dataset_card.md +57 -0
  3. manifest.json +125 -0
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README.md ADDED
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+ # Humanizer-5M
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+
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+ Version: 2.0.0
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+
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+ Humanizer-5M is a synthetic conversational adaptation dataset.
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+
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+ The central objective is not simply to rewrite text to sound casual.
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+
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+ Each example models:
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+
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+ 1. what the user explicitly asks,
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+ 2. what the user may implicitly need,
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+ 3. the user's conversational signals,
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+ 4. the appropriate response calibration,
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+ 5. the final response,
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+ 6. quality and stability metrics.
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+
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+ The dataset specifically teaches proportional adaptation.
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+
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+ High user energy does not automatically mean high hype.
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+ Frustration does not automatically mean excessive empathy.
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+ Casual language does not automatically require slang.
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+ Emojis are used only when contextually appropriate.
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+
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+ The dataset also contains hard negatives representing responses
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+ that are grammatically correct but poorly calibrated.
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+
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+ ## Dataset size
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+
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+ Total: 5,000,000
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+
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+ Train: 4,750,000
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+ Validation: 125,000
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+ Test: 125,000
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+
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+ Shard size: 100,000
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+
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+ ## Core dimensions
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+
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+ The dataset includes explicit calibration dimensions for:
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+
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+ - energy
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+ - hype
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+ - empathy
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+ - warmth
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+ - formality
47
+ - directness
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+ - verbosity
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+ - humor
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+ - emoji usage
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+ - technicality
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+ - confidence
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+ - urgency
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+
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+ ## Quality metrics
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+
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+ Examples also contain:
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+
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+ - naturalness
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+ - human_likeness_proxy
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+ - context_fit
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+ - emotional_fit
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+ - hype_fit
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+ - empathy_fit
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+ - emoji_fit
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+ - verbosity_fit
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+ - directness_fit
68
+ - formality_fit
69
+ - humor_fit
70
+ - technicality_fit
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+ - consistency
72
+ - emotional_stability
73
+ - overreaction_penalty
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+ - forced_human_penalty
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+ - generic_ai_penalty
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+ - repetition_penalty
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+ - emoji_overuse_penalty
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+ - hype_without_reason_penalty
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+ - tone_jump_penalty
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+ - overall_quality
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+
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+ ## Hard negatives
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+
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+ The dataset includes deliberately miscalibrated candidate responses.
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+
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+ Examples include:
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+
88
+ - unnecessary hype
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+ - forced empathy
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+ - emoji overuse
91
+ - excessive verbosity
92
+ - generic assistant phrasing
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+ - abrupt tone changes
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+
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+ ## Important
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+
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+ Metrics are synthetic training labels and calibration signals.
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+ They are not empirical measurements of whether a response was
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+ written by a human.
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+
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+ The dataset does not contain private conversations.
dataset_card.md ADDED
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+ ---
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+ language:
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+ - fr
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+ tags:
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+ - conversational
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+ - dialogue
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+ - instruction-tuning
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+ - tone-adaptation
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+ - empathy
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+ - style
11
+ - synthetic
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+ size_categories:
13
+ - 1M<n<10M
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+ ---
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+
16
+ # Humanizer-5M
17
+
18
+ Humanizer-5M is a synthetic dataset focused on proportional
19
+ conversational adaptation.
20
+
21
+ The dataset separates:
22
+
23
+ 1. understanding the request,
24
+ 2. understanding user signals,
25
+ 3. choosing a response calibration,
26
+ 4. generating a response,
27
+ 5. evaluating calibration quality.
28
+
29
+ It includes hard negatives designed to represent common
30
+ failure modes such as excessive hype, forced empathy,
31
+ emoji overuse, generic assistant language and abrupt tone shifts.
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+
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+ ## Size
34
+
35
+ 5,000,000 examples.
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+
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+ ## Splits
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+
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+ - train: 4,750,000
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+ - validation: 125,000
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+ - test: 125,000
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+
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+ ## Synthetic data notice
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+
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+ The examples and metrics are generated using deterministic
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+ controlled procedures and should be treated as synthetic
47
+ training data and calibration labels.
48
+
49
+ The quality metrics are not human-annotated measurements.
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+
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+ ## Design principle
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+
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+ The objective is not to maximize enthusiasm.
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+
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+ The objective is proportional adaptation:
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+
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+ appropriate tone + context awareness + stability + usefulness.
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+ {
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+ "implicit_need_detection": true,
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+ "emoji_calibration": true,
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+ "verbosity_calibration": true,
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+ "formality_calibration": true,
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+ "directness_calibration": true,
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+ "quality_metrics": true,
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+ "stability_metrics": true
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+ "calibration_principles": [
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+ "proportionality",
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+ "context_awareness",
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+ "emotional_stability",
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+ "usefulness_first",
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+ "no_forced_hype",
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+ "no_forced_empathy",
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+ "no_emoji_spam",
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+ "no_artificial_personality_switches"
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+ ],
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+ "synthetic": true
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