README.md

metadata

language: en
license: mit
tags:
  - text-classification
  - depression
  - mental-health
  - huggingface
datasets:
  - thePixel42/depression-detection
  - infamouscoder/depression-reddit-cleaned
model-index:
  - name: DistilBERT for Depression Detection
    results:
      - task:
          name: Text Classification
          type: text-classification
        metrics:
          - name: Evaluation Loss
            type: loss
            value: 0.0631

DistilBERT for Depression Detection

This model is a fine-tuned version of distilbert-base-uncased for binary depression classification based on Reddit and mental health-related posts.

📊 Training Details

• Base model: distilbert-base-uncased
• Epochs: 3
• Batch size: 8 (train), 16 (eval)
• Optimizer: AdamW with weight decay
• Loss function: CrossEntropyLoss
• Hardware: Trained using GPU acceleration

🧾 Datasets Used

• thePixel42/depression-detection
• infamouscoder/depression-reddit-cleaned

The datasets were cleaned to remove rows with missing text, labels were binarized (0 = not depressed, 1 = depressed), and duplicates were removed.

🧪 Evaluation

Metric	Value
Loss	0.0631
Samples/sec	85.56
Steps/sec	5.35

🚀 Usage

from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

model = AutoModelForSequenceClassification.from_pretrained("your-username/depression-detection-model")
tokenizer = AutoTokenizer.from_pretrained("your-username/depression-detection-model")

inputs = tokenizer("I feel sad and hopeless", return_tensors="pt")
with torch.no_grad():
    logits = model(**inputs).logits
    predicted_class = torch.argmax(logits).item()

print("Prediction:", predicted_class)