This model is designed for mental health journal analysis and should be used as a supportive tool, not as a replacement for professional mental health diagnosis.

How to Use

Using Transformers

from transformers import RobertaTokenizer, RobertaForSequenceClassification import torch

model = RobertaForSequenceClassification.from_pretrained("adicadi/medipal-mental-health") tokenizer = RobertaTokenizer.from_pretrained("adicadi/medipal-mental-health")

text = "I feel anxious and worried" inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)

with torch.no_grad(): outputs = model(**inputs) predictions = torch.nn.functional.softmax(outputs.logits, dim=-1) predicted_class = torch.argmax(predictions).item()

labels = ['Anxiety', 'Bipolar', 'Depression', 'Normal', 'Personality disorder', 'Stress', 'Suicidal'] print(f"Predicted: {labels[predicted_class]}") print(f"Confidence: {predictions[predicted_class].item():.2f}")

Using Inference API

curl https://api-inference.huggingface.co/models/adicadi/medipal-mental-health -H "Authorization: Bearer YOUR_HF_TOKEN" -H "Content-Type: application/json" -d '{"inputs": "I feel anxious and worried"}'

Limitations

Not a substitute for professional mental health diagnosis
May not capture cultural or linguistic nuances
Trained on English text only
Requires professional validation for clinical use

Ethical Considerations

This model deals with sensitive mental health data. Users should:

Handle predictions with care and empathy
Provide appropriate crisis resources for high-risk predictions
Not use as the sole basis for mental health decisions
Comply with local healthcare regulations

Contact

For questions or issues, please contact the model author.