Mental_BERT: A Binary Classification Model for Mental Health

Model Overview

Mental_BERT is a fine-tuned BERT model specifically designed for binary classification tasks within the mental health domain. The model is capable of distinguishing between the following states:

• Distress vs No Distress
• Depression vs No Depression
• Suicide vs No Suicide

This model leverages the robustness of BERT in understanding the context of mental health-related conversations, offering a valuable tool for identifying mental health concerns based on textual data.

Model Details

Model Description

• Developed by: Bhavya Shah
• Shared by: slimshady07
• Model type: BERT-based binary classification model
• Language(s) (NLP): English
• License: Apache-2.0
• Finetuned from model: mental/mental-bert-base-uncased

Model Sources

• Repository: https://huggingface.co./slimshady07/Mental_BERT

Uses

Direct Use

This model can be directly used in mental health support systems, research, and awareness tools to identify critical mental health states.

Out-of-Scope Use

This model should not be used as a replacement for professional mental health assessments. Predictions should always be interpreted by qualified individuals.

Bias, Risks, and Limitations

As with any model trained on human-generated data, biases present in the training data may influence the model's predictions. The model’s predictions are context-dependent and may require additional information for accurate classifications.

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases, and limitations of the model. The model should be used as a supplementary tool and not as a definitive diagnosis tool.