---
task_categories:
- fill-mask
language:
- en
tags:
- medical
pretty_name: MentalReddit
size_categories:
- 1M<n<10M
---

# MentalReddit

This dataset, `dlb/mentalreddit`, was created by the DeepLearningBrasil team for the pre-training of their `MentalBERTa` model. 
This model secured the first position in the [DepSign-LT-EDI@RANLP-2023 shared task](https://arxiv.org/abs/2311.05047/), which focused on classifying social media texts into three levels of depression.

## Dataset Description

The MentalReddit dataset is a large collection of English-language comments sourced from Reddit. The data was specifically curated to provide a rich resource for understanding mental health discourse, as well as general language patterns. The dataset is composed of two main parts:

*   **Mental Health-Related Subreddits:** 3.4 million comments from communities focused on mental health topics.
*   **General Subreddits:** 3.2 million comments from a variety of non-depression-related subreddits to provide a broad base of general language.

In total, the dataset contains approximately 7.31 million comments, occupying about 1.4 GB of disk space.

## Data Fields

The dataset consists of the following fields:

*   `body`: The text content of the Reddit comment.
*   `subreddit`: The name of the subreddit from which the comment was sourced.
*   `id`: A unique identifier for the comment.

## Usage

You can load the dataset using the Hugging Face `datasets` library:

```python
from datasets import load_dataset

dataset = load_dataset("dlb/mentalreddit")
```

### Citation
```bibtex
@inproceedings{garcia-etal-2023-deeplearningbrasil,
    title = "{D}eep{L}earning{B}rasil@{LT}-{EDI}-2023: Exploring Deep Learning Techniques for Detecting Depression in Social Media Text",
    author = "Garcia, Eduardo  and
      Gomes, Juliana  and
      Barbosa Junior, Adalberto  and
      Borges, Cardeque  and
      da Silva, N{\'a}dia",
    booktitle = "Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion",
    month = sep,
    year = "2023",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://aclanthology.org/2023.ltedi-1.42",
    pages = "272--278",
}
```

## Acknowledgments

This work has been supported by the [AI Center of Excellence (Centro de Excelência em Inteligência Artificial – CEIA)](https://www.linkedin.com/company/inteligencia-artificial-deep-learning-brasil) of the Institute of Informatics at the Federal University of Goiás (INF-UFG).