Mental Health Text Classification Model v0.2
!! Accuracy: 69.87% !!
This model is designed to classify texts into different mental health categories. It uses 2% 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:
1from transformers import AutoTokenizer, AutoModelForSequenceClassification
2import torch
3
4# Load the tokenizer and model
5tokenizer = AutoTokenizer.from_pretrained("tahaenesaslanturk/mental-health-classification-v0.2")
6model = AutoModelForSequenceClassification.from_pretrained("tahaenesaslanturk/mental-health-classification-v0.2")
7
8# Encode the input text
9input_text = "I struggle with my relationship with food and my body image, often feeling guilt or shame after eating."
10inputs = tokenizer(input_text, return_tensors="pt")
11
12# Perform inference
13with torch.no_grad():
14 outputs = model(**inputs)
15
16# Get the predicted label
17predicted_label = torch.argmax(outputs.logits, dim=1).item()
18label = model.config.id2label[predicted_label]
19
20print(f"Predicted label: {label}")
