Purpose
It’s a training set for resonance-based language modeling.
Where ordinary sentiment data rates “positive/negative,” this one rates coherence vs noise—how much a short text expresses clarity, compassion, or awareness rather than confusion or manipulation.
It lets a model learn to amplify clear signals and down-weight incoherent ones—the practical job of a Signal Curator.
Structure
Column —> Meaning
id —> Row number
text —> The short message or post (369 total)
resonance_score —> 0–1 float showing vibrational clarity: 0 = chaotic / distorted, 1 = clear / coherent
coherence_label —> coherent or incoherent—a categorical version of that score
tone_tag —> Dominant virtue or quality (clarity, compassion, truth, etc.)
stance —> Narrative posture: constructive, neutral, or harmful
notes —> Quick instruction for your pipeline – either amplify or filter_downweight
How it was built
• About 300 entries are “coherent”: short lines that promote awareness or balanced perception.
• About 69 entries are “incoherent”: reactive, misleading, or noisy messages.
• Each entry has a random tone_tag to show which quality it transmits.
• The text templates are written to sound like real micro-posts (so you can train or benchmark models that evaluate social content).
Use cases
1. Classifier fine-tuning
– Use text → coherence_label as supervised data.
– Helps an LLM or smaller model learn to detect truth/resonance.
2. Scoring function
– Use resonance_score as a target for regression or weighting in retrieval.
3. Filtering layer
– In a content pipeline, drop any row where notes = filter_downweight.
– Let “coherent” items propagate as high-trust signals.
4. Teaching example
– Demonstrates how to embed ethics + signal quality in data rather than after-the-fact moderation.
Conceptual takeaway
A Signal Curator doesn’t produce new data; they tune the field by deciding which words carry awareness cleanly and which distort it.
This CSV is now our first miniature “truth field.”
JSONL version – Each line is a complete JSON object containing the same fields:
id, text, resonance_score, coherence_label, tone_tag, stance, and notes.
Inner I Datasets on HuggingFace – https://huggingface.co/datasets/InnerI/Truthfield_set1
Inner I Net Company/ - Innovative Solutions to Shape Reality 