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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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- intent-classification |
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- mental-health |
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- transformer |
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- conversational-ai |
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pipeline_tag: text-classification |
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base_model: distilbert-base-uncased |
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--- |
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# 🧠 Intent Classifier (MindPadi) |
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The `intent_classifier` is a transformer-based text classification model trained to detect **user intents** in a mental health support setting. It powers the MindPadi assistant's ability to route conversations to the appropriate modules—like emotional support, scheduling, reflection, or journal analysis—based on the user’s message. |
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## 📝 Model Overview |
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- **Model Architecture:** DistilBERT (uncased) + classification head |
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- **Task:** Intent Classification |
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- **Classes:** Over 20 intent categories (e.g., `vent`, `gratitude`, `help_request`, `journal_analysis`) |
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- **Model Size:** ~66M parameters |
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- **Files:** |
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- `config.json` |
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- `pytorch_model.bin` or `model.safetensors` |
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- `tokenizer_config.json`, `vocab.txt`, `tokenizer.json` |
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- `checkpoint-*/` (optional training checkpoints) |
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## ✅ Intended Use |
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### ✔️ Use Cases |
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- Detecting user intent in MindPadi mental health conversations |
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- Enabling context-specific dialogue flows |
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- Assisting with journal entry triage and tagging |
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- Triggering therapy-related tools (e.g., emotion check-ins, PubMed summaries) |
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### 🚫 Not Intended For |
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- Multilingual intent classification (English-only) |
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- Legal or medical diagnosis tasks |
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- Multi-label classification (currently single-label per input) |
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## 💡 Example Intents Detected |
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| Intent | Description | |
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|--------------------|-------------------------------------------------------| |
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| `vent` | User expressing frustration or emotion freely | |
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| `help_request` | Seeking mental health support | |
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| `schedule_session` | Booking a therapy check-in | |
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| `gratitude` | Showing appreciation for support | |
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| `journal_analysis` | Submitting a journal entry for AI feedback | |
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| `reflection` | Talking about personal growth or setbacks | |
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| `not_sure` | Unsure or unclear message from user | |
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## 🛠️ Training Details |
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- **Base Model:** `distilbert-base-uncased` |
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- **Dataset:** Curated and annotated conversations (`training/datasets/finetuned/intents/`) |
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- **Script:** `training/train_intent_classifier.py` |
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- **Preprocessing:** |
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- Text normalization (lowercasing, punctuation removal) |
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- Label encoding |
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- **Loss:** CrossEntropyLoss |
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- **Metrics:** Accuracy, F1-score |
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- **Tokenizer:** WordPiece (DistilBERT tokenizer) |
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## 📊 Evaluation |
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| Metric | Score | |
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|-----------|-------------| |
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| Accuracy | 91.3% | |
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| F1-score | 89.8% | |
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| Recall@3 | 97.1% | |
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| Precision | 88.4% | |
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Evaluation performed on a held-out validation split of MindPadi intent dataset. |
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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 = AutoModelForSequenceClassification.from_pretrained("mindpadi/intent_classifier") |
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tokenizer = AutoTokenizer.from_pretrained("mindpadi/intent_classifier") |
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text = "I’m struggling with my emotions today" |
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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 intent ID:", predicted_class) |
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```` |
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To map `intent ID → label`, load your label encoder from: |
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```python |
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from joblib import load |
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label_encoder = load("intent_encoder/label_encoder.joblib") |
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print("Predicted intent:", label_encoder.inverse_transform([predicted_class])[0]) |
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``` |
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## 🔌 Inference Endpoint Example |
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```python |
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import requests |
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API_URL = "https://api-inference.huggingface.co/models/mindpadi/intent_classifier" |
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headers = {"Authorization": f"Bearer <your-api-token>"} |
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payload = {"inputs": "Can I book a mental health session?"} |
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response = requests.post(API_URL, headers=headers, json=payload) |
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print(response.json()) |
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``` |
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## ⚠️ Limitations |
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* Not robust to long-form texts (>256 tokens); truncate or summarize input. |
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* May confuse overlapping intents like `vent` and `help_request` |
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* False positives possible in vague or sarcastic inputs |
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* Requires pairing with fallback model (`intent_fallback`) for reliability |
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## 🔐 Ethical Considerations |
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* This model is for **supportive routing**, not clinical diagnosis |
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* Use with user consent and proper data privacy safeguards |
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* Intent predictions should not override human judgment in sensitive contexts |
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## 📂 Integration Points |
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| Location | Functionality | |
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| ---------------------------------- | --------------------------------------------- | |
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| `app/chatbot/intent_classifier.py` | Main classifier logic | |
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| `app/chatbot/intent_router.py` | Routes based on predicted intent | |
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| `app/utils/embedding_search.py` | Uses `intent_encoder` for similarity fallback | |
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| `data/processed_intents.json` | Annotated intent samples | |
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## 📜 License |
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MIT License – freely available for commercial and non-commercial use. |
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## 📬 Contact |
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* **Team:** MindPadi AI Developers |
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* **Profile:** [https://huggingface.co/mindpadi](https://huggingface.co/mindpadi) |
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* **Email:** \[[[email protected]](mailto:[email protected])] |
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*Last updated: May 2025* |
