---
library_name: transformers
tags:
- text-classification
- sentiment-analysis
- depression
- BERT
- mental-health
model-index:
- name: Sentiment Classifier for Depression
  results:
  - task:
      type: text-classification
    dataset:
      name: Custom Depression Tweets Dataset
      type: custom
    metrics:
    - name: Accuracy
      type: accuracy
      value: 99.87
    - name: Precision
      type: precision
      value: 99.91
    - name: Recall
      type: recall
      value: 99.81
    - name: F1 Score
      type: f1
      value: 99.86
license: apache-2.0
language:
- en
base_model: google-bert/bert-base-uncased
metrics:
- Accuracy
- Recall
- Percision

widget:
  - text: "RT EichinChangLim In Talking About Adolescence Book you'll discover key strategies to tackle self-harm panic attacks bullies child"
    example_title: "Depression"
  - text: "RT levelsio One counterintuitive thing about seeing all these entrepreneurs going to gym etcnnI never went gym when I started doing star"
    example_title: "Depression"
  - text: "RT dlhampton The Neuroscience of How Affirmations Help Your Mental Health nnStudies show that positive affirmati"
    example_title: "Depression"
  - text: "autism adhd anxiety depression and bod I won the mental illnesses lottery"
    example_title: "Depression"
  - text: "JustJul GeoffField I know a colleague of mine who was having intense anxiety depression and PTSD until he started microdosing shrooms drtessporess was the mycologist and doctor who guided him text her here on twitter X"
    example_title: "Depression"
  - text: "SharronS Hello there Thanks for reaching out I can understand your frustration I would feel the same Id be happy to take a closer look Please feel free to send me a DM with your full name phone number and email address Lena"
    example_title: "Non-depression"
  - text: "RT ANI WATCH In Sandeshkhali West Bengal LoP and BJP MLA Suvendu Adhikari says I visited all villages of our BJP workers are"
    example_title: "Non-depression"
  - text: "RT AerthH Retinol Happy with Retinol Anti Aging"
    example_title: "Non-depression"
  - text: "Hahahahahahha happy ruharu aaaaa"
    example_title: "Non-depression"
  - text: "RT JacksonJason GM Happy Tuesday"
    example_title: "Non-depression"


---

# Model Card for Sentiment Classifier for Depression

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](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **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},
}