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--- |
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language: |
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- en |
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pipeline_tag: text-classification |
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license: mit |
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tags: |
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- storyboard |
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- beats |
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- style-suggestion |
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- nlp |
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- sklearn |
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- educational |
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model-index: |
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- name: StoryboardBeats-Mini-0.1 |
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results: [] |
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--- |
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# StoryboardBeats-Mini v0.1 |
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**What it does** |
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Given a short story prompt, this model predicts: |
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- **Narrative beats** (multi-label): `opening`, `rising_action`, `key_moment`, `twist`, `resolution` |
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- **Suggested visual style**: `Realistic`, `Anime`, `Comic`, `Watercolor`, or `Sketch` |
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**Why it's unique** |
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A tiny, proprietary prototype that links **text prompts** to **story structure** and **visual style** — designed for pre-visualization workflows. Lightweight and CPU-friendly (scikit‑learn). |
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## Files |
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- `model.joblib` — scikit-learn pipelines (TF‑IDF + Logistic Regression) for beats (multi-label) and style. |
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- `inference.py` — minimal interface with `load_model()` and `predict()`. |
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- `requirements.txt` — dependencies to run `inference.py`. |
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## Quick use (local) |
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```bash |
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pip install -r requirements.txt |
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python -c "import inference; print(inference.predict(['A robot in a neon city discovers a secret, but time runs out.']))" |
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``` |
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## Metrics (synthetic split) |
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- beats (subset accuracy): **0.717** |
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- style (accuracy): **1.000** |
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> ⚠️ Trained on synthetic data; not suitable for production. Educational / research use only. |
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