Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+language:
+- en
+tags:
+- intent-classification
+- mental-health
+- transformer
+- conversational-ai
+pipeline_tag: text-classification
+base_model: distilbert-base-uncased
+---
+
+# 🧠 Intent Classifier (MindPadi)
+
+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.
+
+
+
+## 📝 Model Overview
+
+- **Model Architecture:** DistilBERT (uncased) + classification head
+- **Task:** Intent Classification
+- **Classes:** Over 20 intent categories (e.g., `vent`, `gratitude`, `help_request`, `journal_analysis`)
+- **Model Size:** ~66M parameters
+- **Files:**
+  - `config.json`
+  - `pytorch_model.bin` or `model.safetensors`
+  - `tokenizer_config.json`, `vocab.txt`, `tokenizer.json`
+  - `checkpoint-*/` (optional training checkpoints)
+
+
+
+## ✅ Intended Use
+
+### ✔️ Use Cases
+- Detecting user intent in MindPadi mental health conversations
+- Enabling context-specific dialogue flows
+- Assisting with journal entry triage and tagging
+- Triggering therapy-related tools (e.g., emotion check-ins, PubMed summaries)
+
+### 🚫 Not Intended For
+- Multilingual intent classification (English-only)
+- Legal or medical diagnosis tasks
+- Multi-label classification (currently single-label per input)
+
+
+
+## 💡 Example Intents Detected
+
+| Intent             | Description                                           |
+|--------------------|-------------------------------------------------------|
+| `vent`             | User expressing frustration or emotion freely         |
+| `help_request`     | Seeking mental health support                         |
+| `schedule_session` | Booking a therapy check-in                            |
+| `gratitude`        | Showing appreciation for support                      |
+| `journal_analysis` | Submitting a journal entry for AI feedback            |
+| `reflection`       | Talking about personal growth or setbacks             |
+| `not_sure`         | Unsure or unclear message from user                   |
+
+
+
+## 🛠️ Training Details
+
+- **Base Model:** `distilbert-base-uncased`
+- **Dataset:** Curated and annotated conversations (`training/datasets/finetuned/intents/`)
+- **Script:** `training/train_intent_classifier.py`
+- **Preprocessing:**
+  - Text normalization (lowercasing, punctuation removal)
+  - Label encoding
+- **Loss:** CrossEntropyLoss
+- **Metrics:** Accuracy, F1-score
+- **Tokenizer:** WordPiece (DistilBERT tokenizer)
+
+
+
+## 📊 Evaluation
+
+| Metric    | Score       |
+|-----------|-------------|
+| Accuracy  | 91.3%       |
+| F1-score  | 89.8%       |
+| Recall@3  | 97.1%       |
+| Precision | 88.4%       |
+
+Evaluation performed on a held-out validation split of MindPadi intent dataset.
+
+
+
+## 🔍 Example Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model = AutoModelForSequenceClassification.from_pretrained("mindpadi/intent_classifier")
+tokenizer = AutoTokenizer.from_pretrained("mindpadi/intent_classifier")
+
+text = "I’m struggling with my emotions today"
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model(**inputs)
+
+predicted_class = torch.argmax(outputs.logits, dim=1).item()
+print("Predicted intent ID:", predicted_class)
+````
+
+To map `intent ID → label`, load your label encoder from:
+
+```python
+from joblib import load
+label_encoder = load("intent_encoder/label_encoder.joblib")
+print("Predicted intent:", label_encoder.inverse_transform([predicted_class])[0])
+```
+
+
+## 🔌 Inference Endpoint Example
+
+```python
+import requests
+
+API_URL = "https://api-inference.huggingface.co/models/mindpadi/intent_classifier"
+headers = {"Authorization": f"Bearer <your-api-token>"}
+payload = {"inputs": "Can I book a mental health session?"}
+
+response = requests.post(API_URL, headers=headers, json=payload)
+print(response.json())
+```
+
+
+
+## ⚠️ Limitations
+
+* Not robust to long-form texts (>256 tokens); truncate or summarize input.
+* May confuse overlapping intents like `vent` and `help_request`
+* False positives possible in vague or sarcastic inputs
+* Requires pairing with fallback model (`intent_fallback`) for reliability
+
+
+
+## 🔐 Ethical Considerations
+
+* This model is for **supportive routing**, not clinical diagnosis
+* Use with user consent and proper data privacy safeguards
+* Intent predictions should not override human judgment in sensitive contexts
+
+
+
+## 📂 Integration Points
+
+| Location                           | Functionality                                 |
+| ---------------------------------- | --------------------------------------------- |
+| `app/chatbot/intent_classifier.py` | Main classifier logic                         |
+| `app/chatbot/intent_router.py`     | Routes based on predicted intent              |
+| `app/utils/embedding_search.py`    | Uses `intent_encoder` for similarity fallback |
+| `data/processed_intents.json`      | Annotated intent samples                      |
+
+
+
+## 📜 License
+
+MIT License – freely available for commercial and non-commercial use.
+
+
+## 📬 Contact
+
+* **Team:** MindPadi AI Developers
+* **Profile:** [https://huggingface.co/mindpadi](https://huggingface.co/mindpadi)
+* **Email:** \[[you@example.com](mailto:you@example.com)]
+
+*Last updated: May 2025*