This model is a fine-tuned version of BERT (bert-base-uncased) for classifying text as either Depression or Non-depression. The model was trained on a custom dataset of mental health-related social media posts and has shown high accuracy in sentiment classification.

Training Data

The model was trained on a custom dataset of tweets labeled as either depression-related or not. Data pre-processing included tokenization and removal of special characters.

Training Procedure

The model was trained using Hugging Face's transformers library. The training was conducted on a T4 GPU over 3 epochs, with a batch size of 16 and a learning rate of 5e-5.

Evaluation and Testing Data

The model was evaluated on a 20% holdout set from the custom dataset.

Results

Accuracy: 99.87%
Precision: 99.91%
Recall: 99.81%
F1 Score: 99.86%

Environmental Impact

The carbon emissions from training this model can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: T4 GPU
Hours used: 1 hour
Cloud Provider: Google Cloud (Colab)
Carbon Emitted: Estimated at 0.45 kg CO2eq

Technical Specifications

Architecture: BERT (bert-base-uncased)
Training Hardware: T4 GPU in Colab
Training Library: Hugging Face transformers

Citation

BibTeX:

bibtex

@misc{poudel2024sentimentclassifier,
  author = {Poudel, Ashish},
  title = {Sentiment Classifier for Depression},
  year = {2024},
  url = {https://huggingface.co/poudel/sentiment-classifier},
}

poudel/Depression_and_Non-Depression_Classifier

Model Card for Sentiment Classifier for Depression