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maitry30/
mindsense-bert

Name: mindsense-bert
Availability: InStock

kaggle-sentiment-analysis-mental-health

mit

text-embeddings-inference

endpoints_compatible

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🧠 MindSense-BERT — AI-Powered Mental Health Detection

Python

BERT

Accuracy

Streamlit

HuggingFace

License

MindSense-BERT is a fine-tuned BERT-based model designed to classify text into 7 mental health categories using real-world Reddit data. It combines state-of-the-art NLP, class imbalance handling, and explainable AI (SHAP) to create a transparent and practical mental health analysis system.

📌 Model Overview

Model Type: Transformer (BERT)
Base Model: bert-base-uncased
Task: Multi-class Text Classification
Classes (7):
- Normal
- Depression
- Anxiety
- Bipolar
- PTSD
- Stress
- Personality Disorder
Framework: PyTorch + Hugging Face Transformers
Deployment: Streamlit + Hugging Face Hub

🎯 Problem Statement

Mental health conditions affect 1 in 4 people globally, but early detection is difficult due to stigma and lack of awareness.

This model aims to:

Analyze user-written text
Detect potential mental health conditions
Enable early awareness and intervention

📊 Dataset

Source: Kaggle — Sentiment Analysis for Mental Health
Size: 53,000+ Reddit posts
Input: statement (text)
Label: status (mental health category)

⚙️ Preprocessing

Removed URLs, special characters, duplicates
Handled missing values
Tokenized using BERT tokenizer
Lowercasing (uncased model)
Padding & truncation applied

🔬 Training Methodology

Phase 1 — Baseline Models

TF-IDF (10k features, uni + bi-grams)
Logistic Regression, Random Forest, SVM

Phase 2 — BERT Fine-tuning

Pretrained bert-base-uncased
3 epochs
Learning rate: 1e-5
Batch size: 16
Trained on Google Colab T4 GPU

Phase 3 — Class Imbalance Handling

Weak classes identified: Stress, Personality Disorder
Techniques used:
- Word swap
- Random deletion
- Key phrase duplication
Applied custom class weights

📈 Model Performance

Accuracy Comparison

Model	Accuracy
Logistic Regression	~78%
Random Forest	~74%
SVM	~82%
BERT (initial)	~83%
BERT (final)	~87–89%

Per-Class F1 Score

Category	F1 Score
Normal	0.91
Depression	0.89
Anxiety	0.86
Bipolar	0.84
PTSD	0.87
Stress	0.82
Personality Disorder	0.80

🔍 Explainability (SHAP)

This model integrates SHAP (SHapley Additive Explanations) for transparency:

🔴 Red words → push prediction toward a class
🔵 Blue words → push prediction away

This improves trust and interpretability — critical in healthcare AI.

🚀 Usage

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "maitry30/mindsense-bert"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

text = "I feel completely hopeless and empty."

inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
outputs = model(**inputs)

prediction = torch.argmax(outputs.logits, dim=1).item()
print(prediction)

🧪 Example Predictions

Input Text	Predicted Class
"I feel hopeless and empty"	Depression
"I keep having nightmares"	PTSD
"My mood swings a lot"	Bipolar
"I feel fine today"	Normal

🛠 Tech Stack

Python 3.10
PyTorch
Hugging Face Transformers
Scikit-learn
SHAP
Pandas, NumPy
Matplotlib, Seaborn
Streamlit (UI)
Hugging Face Hub (model hosting)

⚠️ Limitations

Limited to English text
May struggle with sarcasm or slang
Depends on dataset quality
Not suitable for real clinical diagnosis

⚖️ Ethical Considerations

This model is for educational purposes only
Not a replacement for mental health professionals
Should not be used for medical decisions
Predictions require human interpretation

🔐 Safety Notice

❌ Not a diagnostic tool
❌ Not for emergency use
✅ Can assist awareness and research

🚀 Future Work

Multilingual support (Hindi + regional languages)
Voice-based mental health detection
Multi-modal AI (text + physiological signals)
Explainable AI improvements
Production deployment (AWS/Azure)

👤 Author

Maitry

GitHub: https://github.com/Maitry09/mindsense-mental-health
Hugging Face: https://huggingface.co/maitry30
Live App: https://mindsense.streamlit.app

📄 License

MIT License

⭐ If you found this model useful, consider giving the project a star!

maitry30/mindsense-bert