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---
tags:
- text-classification
- mental-health
- deberta-v3
- pytorch
- transformers
- sentiment-analysis
- healthcare
language:
- en
license: mit
datasets:
- AIMH/SWMH
metrics:
- accuracy
- f1
pipeline_tag: text-classification
---
# DeBERTa Mental Health Classification Model
A fine-tuned DeBERTa v3 small model for detecting mental health conditions from text.
## Model Description
This model is based on `microsoft/deberta-v3-small` and has been fine-tuned to classify text into 8 mental health categories.
## Training Data
This model was trained on the following datasets:
- **SWMH (Social Media Mental Health Dataset)**: [AIMH/SWMH](https://huggingface.co/datasets/AIMH/SWMH)
- **Sentiment Analysis for Mental Health**: [Kaggle Dataset](https://www.kaggle.com/datasets/suchintikasarkar/sentiment-analysis-for-mental-health)
## Labels
The model can classify text into the following categories:
| ID | Label | Description |
| --- | -------------------- | --------------------------------------------------- |
| 0 | Normal | No mental health concerns detected |
| 1 | Offmychest | General venting/sharing |
| 2 | Depression | Depression-related content |
| 3 | Anxiety | Anxiety-related content |
| 4 | Stress | Stress-related content |
| 5 | Bipolar | Bipolar disorder-related content |
| 6 | Personality disorder | Personality disorder-related content |
| 7 | Suicidal | Suicidal ideation (⚠️ requires immediate attention) |
## Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
# Load model and tokenizer
model_path = "deberta-illness"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForSequenceClassification.from_pretrained(model_path)
# Example text
text = "I've been feeling down lately and can't seem to enjoy anything anymore."
# Tokenize and predict
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
outputs = model(**inputs)
predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
# Get predicted label
predicted_class = torch.argmax(predictions, dim=-1).item()
confidence = predictions[0][predicted_class].item()
print(f"Predicted: {model.config.id2label[str(predicted_class)]}")
print(f"Confidence: {confidence:.2%}")
```
## Model Architecture
- **Base Model:** microsoft/deberta-v3-small
- **Hidden Size:** 768
- **Attention Heads:** 12
- **Hidden Layers:** 6
- **Max Sequence Length:** 512 tokens
- **Vocabulary Size:** 128,100
## License
Please refer to the original microsoft/deberta-v3-small license and any additional licensing terms from the fine-tuning dataset.
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