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---
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](https://huggingface.co/datasets/thePixel42/depression-detection)
- [infamouscoder/depression-reddit-cleaned](https://www.kaggle.com/datasets/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
```python
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)
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