This repository contains a text classification system designed to detect signs of depression in written language. The project leverages a pre-trained Bidirectional Encoder Representations from Transformers (BERT) model, fine-tuned on a specialized dataset to classify text into two categories: Depression and No Depression.

It utilizes Hugging Face's transformers and datasets libraries to provide an optimized workflow for training and inference.

• State-of-the-Art Modeling: Employs an English bert-base-uncased model for deep contextual understanding of text.
• Binary Classification: Fine-tuned specifically to classify text input efficiently and accurately.
• Automated Training Pipeline: train_bert.py handles dataset downloading, preprocessing, hyperparameter optimization, and model serialization.
• Interactive Inference: predict_bert.py allows real-time inference on custom user inputs using the fine-tuned model.

Python 3.7+ is recommended. To set up the environment and install dependencies, run:

pip install transformers torch pandas scikit-learn datasets numpy

git clone https://github.com/Adarsh-Aravind/Depression-Detection-Bert-.git
cd Depression-Detection-Bert-

The train_bert.py script automatically downloads the ShreyaR/DepressionDetection dataset from Hugging Face, processes it, and begins the fine-tuning process.

python train_bert.py

Configuration Details: The model trains for 10 epochs with a learning rate of 1.5e-5, weight decay of 0.01, and a batch size of 20 (with fp16 precision enabled). Results and checkpoints are saved to the ./bert_depression_model directory.

Once the model is trained and saved, you can use the predict_bert.py script for interactive testing.

python predict_bert.py

The script will prompt you to enter sentences and will output the model's classification. Type quit to exit the loop.

• train_bert.py: The complete training script including data tokenization, training configuration, and execution.
• predict_bert.py: An interactive CLI script to analyze text and predict outcomes.

• Dataset sourced from Hugging Face: ShreyaR/DepressionDetection.
• Powered by Hugging Face and PyTorch.