RyanGao/mental-roberta-depression-onnx
This is an ONNX conversion of karangupta224/mental_roberta_depression for use with Transformers.js and browser/serverless deployments.
Model Description
Task: Text classification for depression detection
Base Model: Based on mental/mental-roberta-base, a RoBERTa model pretrained on mental health-related Reddit posts.
Labels: depression / non-depression
Intended Use
This model is designed for:
- Mental health content analysis
- Crisis detection in text
- Support systems for identifying individuals who may need help
- Research purposes in mental health NLP
⚠️ Important: This model is NOT a replacement for professional mental health diagnosis. It should be used as a screening tool only, and anyone showing signs of mental distress should be referred to qualified mental health professionals.
Performance
Original PyTorch model metrics:
- See original model for detailed metrics
Usage
With Transformers.js
import { pipeline } from '@xenova/transformers';
const classifier = await pipeline('text-classification', 'RyanGao/mental-roberta-depression-onnx');
const result = await classifier('I feel very sad and hopeless');
console.log(result);
With Python (ONNX Runtime)
from optimum.onnxruntime import ORTModelForSequenceClassification
from transformers import AutoTokenizer
model = ORTModelForSequenceClassification.from_pretrained("RyanGao/mental-roberta-depression-onnx")
tokenizer = AutoTokenizer.from_pretrained("RyanGao/mental-roberta-depression-onnx")
inputs = tokenizer("I feel very sad and hopeless", return_tensors="pt")
outputs = model(**inputs)
Training Data
The original model was fine-tuned on mental health-related datasets. See the original model card for details.
Limitations and Bias
- Domain-specific: Trained on Reddit mental health posts, may not generalize to other platforms
- Language: English only
- Bias: May reflect biases present in the training data
- Not diagnostic: Cannot and should not be used for clinical diagnosis
Ethical Considerations
- Privacy: Be cautious with personal mental health data
- Harm prevention: Use as part of a larger system that includes human oversight
- False negatives: The model may miss some cases of distress
- False positives: May flag content that doesn't indicate actual distress
Citation
If you use this model, please cite the original MentalRoBERTa paper:
@inproceedings{ji2022mentalbert,
title = {{MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare}},
author = {Shaoxiong Ji and Tianlin Zhang and Luna Ansari and Jie Fu and Prayag Tiwari and Erik Cambria},
year = {2022},
booktitle = {Proceedings of LREC}
}
Contact
- Converted by: RyanGao
- Original model: karangupta224/mental_roberta_depression
- Issues: Please report any issues on the model repository
License
This model is licensed under CC-BY-NC-4.0, same as the original model.