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:

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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:

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.

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💡 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

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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