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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: distilbert-base-uncased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: creative-energy-analyzer |
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results: [] |
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--- |
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# 🎨 Creative Energy Sentiment Classifier |
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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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--- |
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## 🧠 Labels |
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The model classifies text into one of the following six labels: |
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| Label | Description | |
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|------------|-------------| |
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| **inspired** | Bursting with ideas, energized to create | |
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| **expressive** | In the flow of articulating or experimenting freely | |
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| **curious** | Exploring new ideas, researching, or discovering | |
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| **stuck** | Blocked from creating despite the desire | |
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| **doubtful** | Feeling unsure of one’s ideas, skill, or worth | |
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| **drained** | Creatively exhausted, lacking energy or motivation | |
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--- |
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## 🧭 States of Creative Energy |
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Each label is mapped to a **creative state** — a broader dimension of how energy shows up in the creative process: |
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| State | Description | Labels | |
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|--------------|-------------|--------| |
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| **momentum** | 🔥 Spark vs. paralysis | inspired ↔ stuck | |
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| **voice** | 🎤 Flow vs. self-criticism | expressive ↔ doubtful | |
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| **exploration** | 🌱 Wonder vs. exhaustion | curious ↔ drained | |
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These contrasts help reveal how creativity shifts between energized and blocked states. |
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--- |
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## 📊 Training |
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- **Base Model**: `distilbert-base-uncased` |
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- **Fine-tuned on**: 1,200 labeled examples (200 per class) |
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- **Format**: JSONL with `"text"`, `"label"`, and `"state"` |
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- **Split**: 80/10/10 (train/val/test) |
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--- |
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## 📦 Dataset |
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This model was trained on the custom King-8/creative-energy-sentiment |
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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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--- |
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## 💡 Inspiration |
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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 |
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```python |
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from transformers import pipeline |
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classifier = pipeline("text-classification", model="King-8/creative-energy-sentiment") |
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classifier("I’ve been experimenting with new textures all morning — it's so fun!") |
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# [{'label': 'expressive', 'score': 0.91}] |
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``` |
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--- |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 1.2884 | 1.0 | 120 | 1.2956 | 0.5 | 0.4050 | 0.5 | 0.4412 | |
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| 0.8774 | 2.0 | 240 | 0.9885 | 0.6 | 0.6306 | 0.6 | 0.5836 | |
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| 0.515 | 3.0 | 360 | 0.8751 | 0.6417 | 0.6590 | 0.6417 | 0.6466 | |
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| 0.3956 | 4.0 | 480 | 0.8428 | 0.6583 | 0.6679 | 0.6583 | 0.6564 | |
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| 0.2319 | 5.0 | 600 | 0.8588 | 0.65 | 0.6633 | 0.65 | 0.6443 | |
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--- |
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### Evaluation (Validation Set) |
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- Loss: 0.6559 |
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- Accuracy: 0.7667 |
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- Precision: 0.7630 |
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- Recall: 0.7667 |
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- F1: 0.7618 |
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--- |
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### Framework versions |
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- Transformers 4.56.1 |
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- Pytorch 2.8.0+cu126 |
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- Datasets 4.1.1 |
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- Tokenizers 0.22.0 |
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