🧠 MindBridge-RoBERTa
Overview
MindBridge-RoBERTa is a fine-tuned RoBERTa-based text classification model developed for mental health text analysis. The model is designed to identify mental health-related conditions from user-written text by leveraging the strong emotional understanding of its base model and adapting it to mental health datasets.
Built as part of the MindBridge AI Project, this model aims to support research in Natural Language Processing (NLP), educational demonstrations, and AI-assisted mental health applications. It is intended for research and educational purposes only and is not a substitute for professional mental health assessment or diagnosis.
Base Model
This model is fine-tuned from:
SamLowe/roberta-base-go_emotions
The base model was originally trained for emotion recognition using the GoEmotions dataset. By further fine-tuning it on mental health-specific datasets, MindBridge-RoBERTa learns to recognize mental health-related patterns beyond general emotional expression.
Features
- Mental health text classification
- Emotion-aware language understanding
- Fast inference using Hugging Face Transformers
- Suitable for research, academic projects, and educational demonstrations
- Easily deployable in NLP applications
Model Details
| Attribute | Value |
|---|---|
| Model Name | MindBridge-RoBERTa |
| Architecture | RoBERTa |
| Base Model | SamLowe/roberta-base-go_emotions |
| Framework | Hugging Face Transformers |
| Task | Text Classification |
| Language | English |
Example Usage
from transformers import pipeline
classifier = pipeline(
"text-classification",
model="S-mukherjee/MindBridge-RoBERTa"
)
result = classifier("I feel hopeless and don't know what to do anymore.")
print(result)
Example Output
[{'label': 'depression', 'score': 0.83}]
Training
MindBridge-RoBERTa was fine-tuned on a curated mental health text dataset containing labelled examples from multiple mental health categories.
The training pipeline included:
- Data preprocessing and cleaning
- Tokenization using the RoBERTa tokenizer
- Fine-tuning with Hugging Face Transformers
- Model evaluation on a held-out test set
- Safe serialization using
model.safetensors
Intended Applications
This model can be used for:
- Mental health research
- Educational and academic projects
- NLP experimentation
- AI-assisted mental health screening
- Prototype conversational AI systems
- Text analytics involving emotional or psychological content
Limitations
- This model is intended only for research and educational purposes.
- It should not be used for medical diagnosis or clinical decision-making.
- Predictions are probabilistic and may be incorrect for ambiguous or out-of-distribution inputs.
- The model may inherit biases present in the datasets used during training.
- Human oversight is recommended when interpreting model predictions.
Ethical Considerations
Mental health is a sensitive domain. Predictions generated by this model should never replace qualified mental health professionals. Any decisions affecting an individual's well-being should always involve appropriate clinical expertise.
Citation
If you use this model in your research or projects, please cite this repository.
Related Models
This model is part of the MindBridge project.
If you're looking for a conversational mental health assistant built on a Large Language Model (LLM), check out:
🦙 MindBridge-Llama https://huggingface.co/S-mukherjee/MindBridge-Llama
Together, these models form a two-stage mental health AI pipeline:
- MindBridge-RoBERTa — Mental health text classification
- MindBridge-Llama — Context-aware conversational response generation
Author
S. Mukherjee
Developer of MindBridge AI – an AI-powered mental health support system integrating mental health classification and conversational assistance using Hugging Face Transformers and Large Language Models.
Academic Project: MindBridge – AI-Powered Mental Health Support System