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--- |
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license: apache-2.0 |
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language: |
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- en |
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tags: |
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- child-psychology |
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- emotion-recognition |
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- offline-ai |
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- education |
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- onnx |
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- ethical-ai |
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- chromebook |
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- no-pii |
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--- |
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# 🧠 MindSpark-1.0 |
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A **tiny, ethical AI model** that understands **child emotions, mindset, and risk signals** from short journal entries — **100% offline**, **zero PII**, and **Chromebook-ready**. |
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> 🔒 Built for schools, counselors, and parents who care about **privacy, safety, and emotional insight** — without surveillance. |
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--- |
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## 📦 Model Details |
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- **Architecture**: Google BERT-Tiny (4-layer, 256 hidden) + custom multi-task heads |
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- **Tasks**: |
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- **Emotion classification**: 7 classes (`happy`, `sad`, `anxious`, `angry`, `lonely`, `scared`, `confused`) |
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- **Mindset detection**: 6 types (`growth`, `fixed`, `resilient`, `helpless`, `optimistic`, `pessimistic`) |
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- **Risk flag**: Binary (flags phrases like *"want to disappear"*, *"nobody would care"*) |
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- **Format**: ONNX (CPU-optimized) |
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- **Size**: ~9 MB |
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- **License**: Apache 2.0 |
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- **Offline**: Runs on **$150 Chromebooks** with no internet |
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--- |
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## ⚡ Inference (Python + ONNX Runtime) |
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```python |
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from onnxruntime import InferenceSession |
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import json |
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# Load |
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session = InferenceSession("mindspark-1.0.onnx") |
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with open("label_maps.json") as f: |
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label_maps = json.load(f) |
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# Tokenize input (use BERT tokenizer with max_length=128, padding, truncation) |
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# Then run: |
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emotion_logits, mindset_logits, risk_logits = session.run( |
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None, |
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{"input_ids": input_ids, "attention_mask": attention_mask} |
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) |
