---
license: mit
language:
- en
tags:
- emotion-detection
- mental-health
- classification
pipeline_tag: text-classification
---

# Emotion Detection Model for MindPadi (`emotion_model`)

This model is part of the **MindPadi** ecosystem — a mental health chatbot designed to offer empathetic, context-aware responses. `emotion_model` is a transformer-based sequence classification model trained to detect a range of emotional states from user input. It helps personalize chatbot responses by understanding the emotional tone of each message.



## 🧠 Model Summary

- **Task:** Emotion Classification
- **Architecture:** Transformer-based (likely BERT or DistilBERT)
- **Labels:** `happy`, `sad`, `angry`, `neutral`, `fearful`, `disgust`, `surprised`, etc.
- **Framework:** 🤗 Transformers (PyTorch backend)
- **Use Case:** Core emotion recognition module in `app/chatbot/emotion.py`



## 🧾 Intended Use

### ✔️ Primary Use Cases
- Detect user emotions in chat messages.
- Adjust response tone and therapy prompts in MindPadi.
- Support emotional trend tracking in mood analytics.

### 🚫 Not Recommended For
- Clinical diagnosis or treatment decisions.
- Emotion detection in highly formal or technical language (e.g., legal, medical).
- Non-English inputs (English-only training data).



## 📚 Training Details

- **Training Script:** `training/train_emotion_model.py`
- **Datasets:** A mix of publicly available emotion corpora (e.g., GoEmotions) and proprietary datasets stored in `training/datasets/`
- **Preprocessing:**
  - Cleaned for offensive language and class imbalance.
  - Tokenized using `AutoTokenizer` from Hugging Face Transformers.
- **Hyperparameters:**
  - Epochs: ~4–6
  - Batch Size: 16–32
  - Learning Rate: 2e-5 to 3e-5
- **Loss Function:** CrossEntropyLoss
- **Optimizer:** AdamW


## ✅ Evaluation

- **Metrics:** Accuracy, F1-score (micro, macro), confusion matrix
- **Evaluation Script:** `training/evaluate_model.py`
- **Performance:**
  - Accuracy: ~87%
  - Macro F1: ~85%
  - Robust across common emotional states like `sad`, `happy`, `angry`
- **Visualization:** See `lstm_accuracy_bert.png` for comparisons



## 📦 Files

The model directory includes:

| File | Purpose |
|------|---------|
| `config.json` | Model architecture configuration |
| `model.safetensors` | Trained model weights |
| `tokenizer.json`, `vocab.txt` | Tokenizer config |
| `merges.txt` (if BPE-based) | Byte-pair encoding rules |
| `checkpoint-*/` (optional) | Intermediate training checkpoints |



## 🔍 Example Usage

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "mindpadi/emotion_model"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

text = "I feel so overwhelmed and tired."
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
predicted_class = torch.argmax(outputs.logits, dim=1).item()

print("Predicted emotion class:", predicted_class)
````



## 💡 Integration

Integrated in:

* `app/chatbot/emotion.py`: Emotion detection during each chat turn.
* `app/utils/analytics.py`: Aggregates emotions for weekly mood charts.
* `LangGraph`: Used in flow state personalization nodes.



## ⚠️ Limitations

* **Bias:** May inherit cultural or gender biases from training data.
* **Language:** English only.
* **False Positives:** Sarcasm or ambiguous text may confuse predictions.
* **Not Clinical:** Should not be relied upon for medical-level emotional assessments.



## 🧑‍⚖️ Ethical Considerations

* MindPadi informs users that they are interacting with AI.
* Emotion analysis is used only to guide and personalize chatbot responses.
* All usage must respect user privacy (see `app/tools/encryption.py` for encryption methods).



## 🧩 License

MIT License. You are free to use, modify, and distribute the model with attribution.



## 📬 Contact

* **Project:** [MindPadi Mental Health Chatbot](https://huggingface.co/mindpadi)
* **Author:** MindPadi Team
* **Email:** \[[cloud@mindpadi.com](mailto:cloud@mindpadi.com)]
* **GitHub:** \[github.com/mindpadi]

*Last updated: May 2025*