---
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}
}