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--- |
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license: mit |
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language: |
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- en |
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tags: |
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- emotion-detection |
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- mental-health |
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- classification |
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pipeline_tag: text-classification |
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--- |
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# Emotion Detection Model for MindPadi (`emotion_model`) |
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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. |
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## 🧠 Model Summary |
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- **Task:** Emotion Classification |
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- **Architecture:** Transformer-based (likely BERT or DistilBERT) |
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- **Labels:** `happy`, `sad`, `angry`, `neutral`, `fearful`, `disgust`, `surprised`, etc. |
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- **Framework:** 🤗 Transformers (PyTorch backend) |
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- **Use Case:** Core emotion recognition module in `app/chatbot/emotion.py` |
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## 🧾 Intended Use |
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### ✔️ Primary Use Cases |
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- Detect user emotions in chat messages. |
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- Adjust response tone and therapy prompts in MindPadi. |
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- Support emotional trend tracking in mood analytics. |
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### 🚫 Not Recommended For |
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- Clinical diagnosis or treatment decisions. |
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- Emotion detection in highly formal or technical language (e.g., legal, medical). |
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- Non-English inputs (English-only training data). |
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## 📚 Training Details |
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- **Training Script:** `training/train_emotion_model.py` |
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- **Datasets:** A mix of publicly available emotion corpora (e.g., GoEmotions) and proprietary datasets stored in `training/datasets/` |
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- **Preprocessing:** |
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- Cleaned for offensive language and class imbalance. |
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- Tokenized using `AutoTokenizer` from Hugging Face Transformers. |
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- **Hyperparameters:** |
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- Epochs: ~4–6 |
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- Batch Size: 16–32 |
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- Learning Rate: 2e-5 to 3e-5 |
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- **Loss Function:** CrossEntropyLoss |
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- **Optimizer:** AdamW |
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## ✅ Evaluation |
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- **Metrics:** Accuracy, F1-score (micro, macro), confusion matrix |
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- **Evaluation Script:** `training/evaluate_model.py` |
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- **Performance:** |
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- Accuracy: ~87% |
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- Macro F1: ~85% |
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- Robust across common emotional states like `sad`, `happy`, `angry` |
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- **Visualization:** See `lstm_accuracy_bert.png` for comparisons |
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## 📦 Files |
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The model directory includes: |
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| File | Purpose | |
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|------|---------| |
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| `config.json` | Model architecture configuration | |
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| `model.safetensors` | Trained model weights | |
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| `tokenizer.json`, `vocab.txt` | Tokenizer config | |
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| `merges.txt` (if BPE-based) | Byte-pair encoding rules | |
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| `checkpoint-*/` (optional) | Intermediate training checkpoints | |
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## 🔍 Example Usage |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch |
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model_name = "mindpadi/emotion_model" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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text = "I feel so overwhelmed and tired." |
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inputs = tokenizer(text, return_tensors="pt") |
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outputs = model(**inputs) |
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predicted_class = torch.argmax(outputs.logits, dim=1).item() |
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print("Predicted emotion class:", predicted_class) |
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```` |
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## 💡 Integration |
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Integrated in: |
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* `app/chatbot/emotion.py`: Emotion detection during each chat turn. |
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* `app/utils/analytics.py`: Aggregates emotions for weekly mood charts. |
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* `LangGraph`: Used in flow state personalization nodes. |
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## ⚠️ Limitations |
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* **Bias:** May inherit cultural or gender biases from training data. |
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* **Language:** English only. |
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* **False Positives:** Sarcasm or ambiguous text may confuse predictions. |
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* **Not Clinical:** Should not be relied upon for medical-level emotional assessments. |
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## 🧑⚖️ Ethical Considerations |
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* MindPadi informs users that they are interacting with AI. |
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* Emotion analysis is used only to guide and personalize chatbot responses. |
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* All usage must respect user privacy (see `app/tools/encryption.py` for encryption methods). |
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## 🧩 License |
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MIT License. You are free to use, modify, and distribute the model with attribution. |
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## 📬 Contact |
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* **Project:** [MindPadi Mental Health Chatbot](https://huggingface.co/mindpadi) |
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* **Author:** MindPadi Team |
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* **Email:** \[[[email protected]](mailto:[email protected])] |
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* **GitHub:** \[github.com/mindpadi] |
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*Last updated: May 2025* |
