BERT-finetuned Mental Health Open-source Model - Free Deployment for Classifying Text Mental Health Categories

Bert Finetuned Mental Health

Developed by Elite13

A mental health text classification model fine-tuned based on BERT-base-uncased, which can classify text into seven mental health categories.

Text Classification

Transformers

EnglishOpen Source License:Apache-2.0 #Mental health classification #High-precision sentiment analysis #Reddit text detection

Downloads 897

Release Time : 6/5/2025

Model Overview

This model provides sentiment analysis support for mental health applications by analyzing signs of psychological distress in user-generated content and can accurately identify various psychological states such as anxiety and depression.

Model Features

Multi-category classification

Able to accurately classify text into seven mental health categories, including anxiety, depression, bipolar disorder, etc.

Rich data

Trained using datasets of posts from multiple Reddit mental health forums, with high data coverage and diversity.

High precision

Performs excellently on evaluation metrics, with an accuracy rate of 0.9656.

Model Capabilities

Mental health text classification

Sentiment analysis

Psychological distress detection

Use Cases

Mental health monitoring

Social media mental state screening

Analyze users' posts on social media to identify potential psychological problems at an early stage

Can automatically mark user content that needs attention

Psychological counseling auxiliary tool

Help psychological counselors quickly understand the main problem types of clients

Provide preliminary classification suggestions

🚀 BERT Fine-Tuned for Mental Health Classification

This model fine-tunes the bert-base-uncased transformer to classify text inputs into seven mental health categories, supporting emotional analysis in mental health applications by detecting psychological distress signs in user-generated content.

🚀 Quick Start

Try It Out

You can interact with the model in real-time via this Streamlit-powered Hugging Face Space:
👉 Live Demo on Hugging Face Spaces

✨ Features

This model is designed to classify text inputs into seven mental health categories, which can effectively support emotional analysis in mental health-related applications and detect signs of psychological distress in user-generated content.

📦 Installation

No installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

from transformers import pipeline

classifier = pipeline("text-classification", model="Elite13/bert-finetuned-mental-health")

text = "I'm tired of everything. Nothing makes sense anymore."
result = classifier(text)
print(result)

📚 Documentation

Datasets Used

sai1908/Mental_Health_Condition_Classification
Reddit posts from mental health forums
~80,000 cleaned entries from the original 100,000
kamruzzaman-asif/reddit-mental-health-classification
Additional Reddit mental health posts to improve coverage and diversity

Model Overview

Property	Details
Base Model	`bert-base-uncased`
Model Type	Multi-class text classification (7 labels)
Framework	Hugging Face Transformers
Training Method	Trainer API (PyTorch backend)

Target Labels

Anxiety
Depression
Bipolar
Normal
Personality Disorder
Stress
Suicidal

Training Configuration

Parameter	Value
Epochs	3
Learning Rate	2e-5
Batch Size	16
Max Token Length	256
Optimizer	AdamW
Hardware	2x NVIDIA Tesla T4 GPUs
Total FLOPs	25,605,736,040,851,200

Evaluation Metrics

Metric	Value
Accuracy	0.9656
Validation Loss	0.1513
Training Loss	0.0483
Samples/sec	65.354
Training Time	~1.65 hrs

📄 License

This project is licensed under the Apache 2.0 license.

Featured Recommended AI Models

Empowering the Future, Your AI Solution Knowledge Base

English 简体中文繁體中文にほんご