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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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- intent-classification |
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- emotion-detection |
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- mental-health |
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- lstm |
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- sentence-transformers |
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- sklearn |
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pipeline_tag: text-classification |
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--- |
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# 🧠 MindPadi: Hybrid Classifier Suite |
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This repository contains auxiliary models for intent and emotion classification used in the **MindPadi** mental health assistant. These models include rule-based, ML-based, and deep learning classifiers trained to detect emotional states, user intent, and conversational cues. |
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## 📦 Files |
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| File | Description | |
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|-------------------------------|----------------------------------------------------------| |
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| `intent_clf.joblib` | scikit-learn pipeline for intent classification (TF-IDF) | |
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| `intent_sentence_classifier.pkl` | Sentence-level intent classifier (pickle) | |
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| `lstm_tfidf.h5` | LSTM model trained on TF-IDF vectors | |
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| `lstm_bert.h5` | LSTM model trained on BERT embeddings | |
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| `tfidf_vectorizer.pkl` | TF-IDF vectorizer for preprocessing text | |
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| `tfidf_embeddings.pkl` | Cached TF-IDF embeddings for faster lookup | |
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| `bert_embeddings.npy` | Precomputed BERT embeddings used in training/testing | |
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| `lstm_accuracy_tfidf.png` | Evaluation plot (TF-IDF model) | |
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| `lstm_accuracy_bert.png` | Evaluation plot (BERT model) | |
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| `model_configs/` | JSON configs for training and architecture | |
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## 🎯 Tasks Supported |
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- **Intent Classification**: Understand what the user is trying to communicate. |
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- **Emotion Detection**: Identify the emotional tone (e.g., sad, angry). |
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- **Embedding Generation**: Support vector similarity or hybrid routing. |
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## 🔬 Model Overview |
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| Model Type | Framework | Notes | |
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|----------------|-------------|----------------------------------------| |
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| LSTM + TF-IDF | Keras | Traditional pipeline with good generalization | |
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| LSTM + BERT | Keras | Handles contextual sentence meanings | |
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| TF-IDF + SVM | scikit-learn | Lightweight and interpretable intent routing | |
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| Sentence Classifier | scikit-learn | Quick rule or decision-tree model for sentence-level labels | |
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--- |
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## 🛠️ Usage Example |
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### Intent Prediction (Joblib) |
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```python |
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from joblib import load |
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clf = load("intent_clf.joblib") |
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text = ["I feel really anxious today"] |
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pred = clf.predict(text) |
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print("Intent:", pred[0]) |
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```` |
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### LSTM Emotion Prediction |
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```python |
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from tensorflow.keras.models import load_model |
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import numpy as np |
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model = load_model("lstm_bert.h5") |
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embeddings = np.load("bert_embeddings.npy") # assuming aligned with test set |
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output = model.predict(embeddings) |
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print("Predicted emotion class:", output.argmax(axis=1)) |
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``` |
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## 📊 Evaluation |
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| Model | Accuracy | Dataset Size | Notes | |
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| ------------------- | -------- | ------------ | -------------------------------- | |
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| `lstm_bert.h5` | \~88% | 10,000+ | Best for nuanced emotional input | |
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| `lstm_tfidf.h5` | \~83% | 10,000+ | Lighter, faster | |
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| `intent_clf.joblib` | \~90% | 8,000+ | Works well with short queries | |
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Evaluation visualizations: |
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## ⚠️ Limitations |
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* English only |
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* May misclassify ambiguous or sarcastic phrases |
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* LSTM models require matching vectorizer or embeddings |
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## 🧩 Integration |
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These models are invoked in: |
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* `app/chatbot/intent_classifier.py` |
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* `app/chatbot/emotion.py` |
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* `app/utils/embedding_search.py` |
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## 🧠 Intended Use |
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* Mental health journaling feedback |
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* Chatbot-based emotion understanding |
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* Offline fallback for heavy transformer models |
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## 📄 License |
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MIT License – free for commercial and research use. |
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*Last updated: May 6, 2025* |
