King-8/creative-energy-analyzer

🎨 Creative Energy Sentiment Classifier

This model is fine-tuned to detect creative emotional states in text. It predicts one of six nuanced sentiment labels that represent different dimensions of creative energy: from inspiration and flow to burnout and doubt.

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🧠 Labels

The model classifies text into one of the following six labels:

Label	Description
inspired	Bursting with ideas, energized to create
expressive	In the flow of articulating or experimenting freely
curious	Exploring new ideas, researching, or discovering
stuck	Blocked from creating despite the desire
doubtful	Feeling unsure of one’s ideas, skill, or worth
drained	Creatively exhausted, lacking energy or motivation

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🧭 States of Creative Energy

Each label is mapped to a creative state — a broader dimension of how energy shows up in the creative process:

State	Description	Labels
momentum	🔥 Spark vs. paralysis	inspired ↔ stuck
voice	🎤 Flow vs. self-criticism	expressive ↔ doubtful
exploration	🌱 Wonder vs. exhaustion	curious ↔ drained

These contrasts help reveal how creativity shifts between energized and blocked states.

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📊 Training

• Base Model: distilbert-base-uncased
• Fine-tuned on: 1,200 labeled examples (200 per class)
• Format: JSONL with "text", "label", and "state"
• Split: 80/10/10 (train/val/test)

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📦 Dataset

This model was trained on the custom King-8/creative-energy-sentiment dataset (https://huggingface.co/datasets/King-8/creative-energy-sentiment), containing 1,200 examples crafted and categorized into 6 emotion-based labels.

💡 Inspiration

This project aims to go beyond typical positive/negative sentiment and capture the emotional complexity of the creative process — to better support artists, writers, students, and thinkers navigating their creative energy.

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🧪 Example Usage

from transformers import pipeline

classifier = pipeline("text-classification", model="King-8/creative-energy-sentiment")

classifier("I’ve been experimenting with new textures all morning — it's so fun!")
# [{'label': 'expressive', 'score': 0.91}]

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Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
1.2884	1.0	120	1.2956	0.5	0.4050	0.5	0.4412
0.8774	2.0	240	0.9885	0.6	0.6306	0.6	0.5836
0.515	3.0	360	0.8751	0.6417	0.6590	0.6417	0.6466
0.3956	4.0	480	0.8428	0.6583	0.6679	0.6583	0.6564
0.2319	5.0	600	0.8588	0.65	0.6633	0.65	0.6443

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Evaluation (Validation Set)

• Loss: 0.6559
• Accuracy: 0.7667
• Precision: 0.7630
• Recall: 0.7667
• F1: 0.7618

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Framework versions

• Transformers 4.56.1
• Pytorch 2.8.0+cu126
• Datasets 4.1.1
• Tokenizers 0.22.0