---
dataset_info:
  features:
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 1275158349
    num_examples: 15737126
  download_size: 862543908
  dataset_size: 1275158349
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
task_categories:
- text-generation
language:
- vi
tags:
- social media
pretty_name: ViSoBERT
size_categories:
- 10M<n<100M
---
# Dataset Card for ViSoBERT

## Dataset Description

- **Repository:** https://huggingface.co/uitnlp/visobert
- **Paper:** [ViSoBERT: A Pre-Trained Language Model for Vietnamese Social Media Text Processing](https://aclanthology.org/2023.emnlp-main.315/)

#### Dataset Summary

<!-- Provide a quick summary of the dataset. -->
**ViSoBERT Dataset Summary:**

ViSoBERT is the pre-training dataset for the ViSoBERT model. It contains social media texts from Facebook, Tiktok and YouTube collected between January 2020 and December 2022.


### Languages
The language in the dataset is Vietnamese.

## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
### Dataset Instances
An example of 'train' looks as follows:
```json
{
  "text": "cười thế này iz ))",
}
```

### Data Fields

Here's the Data Fields section for the ViSoBERT pre-training corpus based on the dataset features provided:

- `text`: the text, stored as a `string` feature.

## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**
```
@inproceedings{nguyen-etal-2023-visobert,
    title = "{V}i{S}o{BERT}: A Pre-Trained Language Model for {V}ietnamese Social Media Text Processing",
    author = "Nguyen, Nam  and
      Phan, Thang  and
      Nguyen, Duc-Vu  and
      Nguyen, Kiet",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.emnlp-main.315",
    pages = "5191--5207",
    abstract = "English and Chinese, known as resource-rich languages, have witnessed the strong development of transformer-based language models for natural language processing tasks. Although Vietnam has approximately 100M people speaking Vietnamese, several pre-trained models, e.g., PhoBERT, ViBERT, and vELECTRA, performed well on general Vietnamese NLP tasks, including POS tagging and named entity recognition. These pre-trained language models are still limited to Vietnamese social media tasks. In this paper, we present the first monolingual pre-trained language model for Vietnamese social media texts, ViSoBERT, which is pre-trained on a large-scale corpus of high-quality and diverse Vietnamese social media texts using XLM-R architecture. Moreover, we explored our pre-trained model on five important natural language downstream tasks on Vietnamese social media texts: emotion recognition, hate speech detection, sentiment analysis, spam reviews detection, and hate speech spans detection. Our experiments demonstrate that ViSoBERT, with far fewer parameters, surpasses the previous state-of-the-art models on multiple Vietnamese social media tasks. Our ViSoBERT model is available only for research purposes. Disclaimer: This paper contains actual comments on social networks that might be construed as abusive, offensive, or obscene.",
}
```

**APA:**
- Nguyen, N., Phan, T., Nguyen, D.-V., & Nguyen, K. (2023). **ViSoBERT: A pre-trained language model for Vietnamese social media text processing**. In H. Bouamor, J. Pino, & K. Bali (Eds.), *Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing* (pp. 5191-5207). Singapore: Association for Computational Linguistics. https://aclanthology.org/2023.emnlp-main.315

## Dataset Card Authors
[@phucdev](https://github.com/phucdev)