library_name: transformers
license: apache-2.0
datasets:
- Private
language:
- en
metrics:
- accuracy
- precision
- recall
- f1
base_model: google-bert/bert-base-uncased
pipeline_tag: text-classification
Model Card for Model ID
This is a fine-tuned BERT model (bert-base-uncased) used for classifying text into two categories: Depression or Non-depression. The model is designed for text classification and has been trained on a custom dataset of mental health-related posts from social media.
Model Description
This model aims to identify signs of depression in written text. It was trained on social media posts labeled as either indicative of depression or not. The model uses the BERT architecture for text classification and was fine-tuned specifically for this task.
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by: Ashish Poudel
- Model type: Text Classification
- Language(s) (NLP): English (
en) - License:
apache-2.0 - Finetuned from model:
apache-2.0
Model Sources [optional]
- Repository: Sentiment Classifier for Depression
- Demo [optional]: Live Gradio App
Use
This model is designed to classify text as either depression-related or non-depression-related. It can be used in social media sentiment analysis, mental health research, and automated text analysis systems.
Downstream Use
The model can be further fine-tuned for other types of sentiment analysis tasks related to mental health.
Out-of-Scope Use
The model should not be used for clinical diagnosis or decision-making without the input of medical professionals. It is also unsuitable for text that is not in English or very short/ambiguous inputs.