elishaw/deberta_mental
127
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
- Sentiment Analysis for Mental Health: Kaggle Dataset
Labels
The model can classify text into the following categories:
Usage
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.