---
tags:
- text-classification
- mental-health
- transformers
- pytorch
- huggingface
---

# 🧠 mindBERT - Mental Health Text Classification

![mindBERT UI](https://huggingface.co/DrSyedFaizan/mindBERT/resolve/main/mindBERTUI.png)

## 📌 Model Description
**mindBERT** is a fine-tuned **BERT-based** model designed for **mental health text classification**. It can classify text into **stress, depression, bipolar disorder, personality disorder, and anxiety** with high accuracy. The model was trained on **real-world mental health discussions** from Reddit.

🔗 **Try the Interactive UI**: [Hugging Face Spaces](https://huggingface.co/spaces/DrSyedFaizan/mindBERT)

---

## 📊 Training and Evaluation

### **Training Loss & Learning Rate**
![Training Loss & Learning Rate](https://huggingface.co/DrSyedFaizan/mindBERT/resolve/main/traininglossandlearningrate.png)

### **Training Summary**
| Epoch | Training Loss | Validation Loss | Accuracy |
|-------|--------------|----------------|----------|
| 1     | 0.359400     | 0.285864       | 89.61%   |
| 2     | 0.210500     | 0.224632       | 92.03%   |
| 3     | 0.177800     | 0.217146       | 92.83%   |
| 4     | 0.089200     | 0.249640       | 93.23%   |
| 5     | 0.087600     | 0.282782       | 93.39%   |

### **Confusion Matrix**
![Confusion Matrix](https://huggingface.co/DrSyedFaizan/mindBERT/resolve/main/confusionmatrix.png)

### **Dataset Label Distribution**
![Dataset Labels](https://huggingface.co/DrSyedFaizan/mindBERT/resolve/main/datasetlabelsbarh.png)

### **Evaluation Metrics (Loss & Accuracy)**
![Evaluation Results](https://huggingface.co/DrSyedFaizan/mindBERT/resolve/main/evalpics.png)

### **🔬 Full Weights & Biases Evaluation**
[🔗 View Detailed W&B Logs](https://wandb.ai/drsyedfaizan1987-northeastern-university/huggingface/runs/f3w7nhbd?nw=nwuserdrsyedfaizan1987)

---

## 🛠 How to Use
To use this model for inference:

```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

model_name = "DrSyedFaizan/mindBERT"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

text = "I feel so anxious and stressed all the time."
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
with torch.no_grad():
    logits = model(**inputs).logits
    prediction = torch.argmax(logits, dim=1).item()

labels = ["Stress", "Depression", "Bipolar", "Personality Disorder", "Anxiety"]
print(f"Predicted Category: {labels[prediction]}")
```

---

## 🔧 Training Parameters
```python
training_args = TrainingArguments(
    output_dir="./results",          # Output directory
    evaluation_strategy="epoch",     # Evaluate once per epoch
    save_strategy="epoch",           # Save at each epoch
    learning_rate=2e-5,               # Learning rate
    per_device_train_batch_size=16,   # Training batch size
    per_device_eval_batch_size=16,    # Evaluation batch size
    num_train_epochs=5,               # Training epochs
    weight_decay=0.01,                # Weight decay
    logging_steps=10,                 # Logging frequency
    lr_scheduler_type="linear",      # Learning rate scheduler
    warmup_steps=500,                 # Warmup steps
    load_best_model_at_end=True,      # Load best model after training
    metric_for_best_model="eval_loss",
    save_total_limit=3,               # Save up to 3 checkpoints
    gradient_accumulation_steps=2,    # Larger batch size simulation
    report_to="wandb"                 # Log to Weights & Biases
)
```

---

## 📌 Future Improvements
- Train with larger datasets like **CLPsych, eRisk**.
- Expand categories for broader **mental health classification**.
- Deploy as an **API** for real-world use cases.

💡 **mindBERT - Advancing AI for Mental Health Research!** 🚀