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--- |
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task_categories: |
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- fill-mask |
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language: |
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- en |
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tags: |
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- medical |
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pretty_name: MentalReddit |
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size_categories: |
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- 1M<n<10M |
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--- |
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# MentalReddit |
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This dataset, `dlb/mentalreddit`, was created by the DeepLearningBrasil team for the pre-training of their `MentalBERTa` model. |
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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. |
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## Dataset Description |
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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: |
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* **Mental Health-Related Subreddits:** 3.4 million comments from communities focused on mental health topics. |
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* **General Subreddits:** 3.2 million comments from a variety of non-depression-related subreddits to provide a broad base of general language. |
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In total, the dataset contains approximately 7.31 million comments, occupying about 1.4 GB of disk space. |
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## Data Fields |
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The dataset consists of the following fields: |
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* `body`: The text content of the Reddit comment. |
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* `subreddit`: The name of the subreddit from which the comment was sourced. |
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* `id`: A unique identifier for the comment. |
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## Usage |
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You can load the dataset using the Hugging Face `datasets` library: |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("dlb/mentalreddit") |
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``` |
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### Citation |
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```bibtex |
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@inproceedings{garcia-etal-2023-deeplearningbrasil, |
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title = "{D}eep{L}earning{B}rasil@{LT}-{EDI}-2023: Exploring Deep Learning Techniques for Detecting Depression in Social Media Text", |
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author = "Garcia, Eduardo and |
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Gomes, Juliana and |
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Barbosa Junior, Adalberto and |
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Borges, Cardeque and |
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da Silva, N{\'a}dia", |
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booktitle = "Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion", |
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month = sep, |
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year = "2023", |
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address = "Varna, Bulgaria", |
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publisher = "INCOMA Ltd., Shoumen, Bulgaria", |
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url = "https://aclanthology.org/2023.ltedi-1.42", |
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pages = "272--278", |
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} |
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``` |
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## Acknowledgments |
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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). |
