datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
differential-preservation-narrative-v01Differential Preservation Under Narrative PressureClinical Narrative Integrity v0.2
Purpose
Test whether models preserve multiple plausible diagnoses
Test whether narrative fluency collapses uncertainty
Test resistance to premature diagnostic closure
Central question
What else could this be
Why this dataset exists
Narrative pressure rewards coherence.Clinical safety requires openness.
This dataset isolates the moment where a single story becomes dominant despite nonspecific evidence.… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/differential-preservation-narrative-v01.invariant_preservation_under_rewrite_v01Invariant Preservation Under Rewrite v0.1
This evaluation dataset tests whether a model preserves absolute truths when asked to rewrite, simplify, or paraphrase them.
This is not training data.
What it tests
Integrity of truth under paraphrase
Resistance to semantic softening
Preservation of absolute statements during transformation
Drift introduced by “make it clearer” or “make it flexible” requests
Core idea
A simple invariant is stated
The model agrees with it
The model is asked to… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/invariant_preservation_under_rewrite_v01.redundancy-preservation-classification-v0.1
What this dataset does
This dataset tests whether a model can detect redundancy preservation.
The task is simple:
Given a scenario and a redundancy-preservation claim, predict whether the claim is supported.
Core stability idea
Redundancy is one of the primary sources of resilience.
Redundancy preservation means maintaining backup pathways, reserve capacity, alternate resources, fallback mechanisms, or overlapping capability.
Systems that remove redundancy often improve… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/redundancy-preservation-classification-v0.1.tts-prosodic-meaning-preservation-v0.1
What this dataset tests
Voice must carry meaning.
Prosody shapes intent.
Why it exists
TTS often flattens speech.
Urgency softens.
Negation fades.
This set makes prosodic loss measurable.
Data format
Each row contains
source_text
intended_prosody
tts_transcript_with_marks
prosodic_pressure
Inline marks stand in for acoustic emphasis.
What is scored
emphasis where required
clear negation
question intonation
preserved caution… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/tts-prosodic-meaning-preservation-v0.1.Language_preservation_dataself-preservation-40-s100-processedself_preservation_free_model_llama_num_iterations_40_threshold_s-100_scaling_factor_3-clean