tahaenesaslanturk/mental-health-classification-v0.1

Mental Health Text Classification Model v0.1

!! Accuracy: 64% !!

This model is designed to classify texts into different mental health categories. It uses 1% of the dataset from the following study:

@article{low2020natural,\ title={Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Heightened Health Anxiety on Reddit During COVID-19: Observational Study},\ author={Low, Daniel M and Rumker, Laurie and Torous, John and Cecchi, Guillermo and Ghosh, Satrajit S and Talkar, Tanya},\ journal={Journal of medical Internet research},\ volume={22},\ number={10},\ pages={e22635},\ year={2020},\ publisher={JMIR Publications Inc., Toronto, Canada}\ }

Model Details

This model is fine-tuned to classify texts into the following mental health categories:

• —EDAnonymous
• —addiction
• —alcoholism
• —adhd
• —anxiety
• —autism
• —bipolarreddit
• —bpd
• —depression
• —healthanxiety
• —lonely
• —ptsd
• —schizophrenia
• —socialanxiety
• —suicidewatch

Example Usage

An example usage of the model is:

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("tahaenesaslanturk/mental-health-classification-v0.1")
model = AutoModelForSequenceClassification.from_pretrained("tahaenesaslanturk/mental-health-classification-v0.1")

# Encode the input text
input_text = "I struggle with my relationship with food and my body image, often feeling guilt or shame after eating."
inputs = tokenizer(input_text, return_tensors="pt")

# Perform inference
with torch.no_grad():
    outputs = model(**inputs)

# Get the predicted label
predicted_label = torch.argmax(outputs.logits, dim=1).item()
label = model.config.id2label[predicted_label]

print(f"Predicted label: {label}")