metadata

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

Dataset Summary

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

Dataset Instances

An example of 'train' looks as follows:

{
  "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

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