---
license: apache-2.0
language:
- en
tags:
- child-psychology
- emotion-recognition
- offline-ai
- education
- onnx
- ethical-ai
- chromebook
- no-pii
---
# 🧠 MindSpark-1.0

A **tiny, ethical AI model** that understands **child emotions, mindset, and risk signals** from short journal entries — **100% offline**, **zero PII**, and **Chromebook-ready**.

> 🔒 Built for schools, counselors, and parents who care about **privacy, safety, and emotional insight** — without surveillance.

---

## 📦 Model Details

- **Architecture**: Google BERT-Tiny (4-layer, 256 hidden) + custom multi-task heads
- **Tasks**:
  - **Emotion classification**: 7 classes (`happy`, `sad`, `anxious`, `angry`, `lonely`, `scared`, `confused`)
  - **Mindset detection**: 6 types (`growth`, `fixed`, `resilient`, `helpless`, `optimistic`, `pessimistic`)
  - **Risk flag**: Binary (flags phrases like *"want to disappear"*, *"nobody would care"*)
- **Format**: ONNX (CPU-optimized)
- **Size**: ~9 MB
- **License**: Apache 2.0
- **Offline**: Runs on **$150 Chromebooks** with no internet

---

## ⚡ Inference (Python + ONNX Runtime)

```python
from onnxruntime import InferenceSession
import json

# Load
session = InferenceSession("mindspark-1.0.onnx")
with open("label_maps.json") as f:
    label_maps = json.load(f)

# Tokenize input (use BERT tokenizer with max_length=128, padding, truncation)
# Then run:
emotion_logits, mindset_logits, risk_logits = session.run(
    None,
    {"input_ids": input_ids, "attention_mask": attention_mask}
)