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---
task_categories:
- text-classification
language:
- lus
pretty_name: Twirling Mizo News Dataset
---
# Twirling Mizo News Dataset
## Description
The **Twirling Mizo News Dataset** is a collection of 6,731 news articles written in the Mizo language. The dataset is categorized into six distinct categories, making it a versatile resource for various Natural Language Processing (NLP) tasks.
## Dataset Structure
- **Total Entries:** 6,731
- **Columns:**
- **Article:** Contains the news articles written in Mizo.
- **Category:** The category to which each article belongs.
- **Unique Categories:** 6
- Categories include:
- *tualchhung*
- *khawvel*
- *ramchhung*
- *infiamna*
- *thalai*
- *hmarchhak*
- **Largest Category:** *tualchhung* (1,686 articles)
- **Training Set (80%)**: This set contains 80% of the data for each category and will be used for training machine learning models.
- **Testing Set (20%)**: This set contains the remaining 20% of the data for each category and can be used for evaluating the performance of the models.
### Example Split
For each category, the dataset is split as follows:
1. **Category**: "tualchhung"
- **Training Set**: 80% of articles in this category.
- **Testing Set**: 20% of articles in this category.
2. **Category**: "khawvel"
- **Training Set**: 80% of articles in this category.
- **Testing Set**: 20% of articles in this category.
This pattern is applied to all categories in the dataset, ensuring that the splits are balanced and representative of each category.
## How to use
The `datasets` library allows you to load and pre-process your dataset. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function.
```python
from datasets import load_dataset
twirling_mizo_news_train = load_dataset("andrewbawitlung/twirling_mizo_news", split="train")
twirling_mizo_news_test = load_dataset("andrewbawitlung/twirling_mizo_news", split="test")
```
display 3 random indices
```python
import random
for split, dataset in [("train", twirling_mizo_news_train), ("test", twirling_mizo_news_test)]:
print(f"Random samples from the {split} dataset:")
for idx in random.sample(range(len(dataset)), 5):
print(f"Index: {idx}\n{dataset[idx]}\n{'-' * 50}")
```
## Potential Use Cases
This dataset is suitable for:
- **Text Classification:** Train models to classify news into the six predefined categories.
- **Language Modeling:** Build language models specifically for Mizo.
- **Topic Analysis:** Explore the distribution of news topics in the Mizo language.
## Sample Data
| Article | Category |
|---------|----------|
| Assam sorkar chuan hri leng dona kawng hnathawh zawng zawng an buatsaih leh mek thu an sawi | hmarchhak |
| Nagaland mi Covid-19 kai pakhat hmuh a nih thu chhuah nghal a ni | hmarchhak |
---
## Citation
**BibTeX entry and citation info:**
```
@inproceedings{bawitlung2023approach,
title={An Approach to Mizo Language News Classification Using Machine Learning},
author={Bawitlung, Andrew and Dash, Sandeep Kumar and Lalramhluna, Robert and Gelbukh, Alexander},
booktitle={International Conference on Data Science and Network Engineering},
pages={165--180},
year={2023},
organization={Springer}
} |