mindpadi/intent_classifier

🧠 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

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:

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

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
• Email: [[email protected]]

Last updated: May 2025