Update README.md

README.md

@@ -14,4 +14,77 @@ configs:
   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)