---
license: unknown
language: 
- fil
pretty_name: Balita Nlp
task_categories: 
- image-captioning
tags: 
- image-captioning
---

BalitaNLP is a dataset for image-conditional language generation and text-conditional image generation. It consists of 300k Filipino news
articles and images gathered from Filipino news outlets. News articles are categorized into five possible classes: News, Sports, Entertainment,
Crime, and Other. Some articles were removed from the SEACrowd `imtext` schema, as their corresponding image files do not exist:
- `train` split (262480 total articles): from the original 281403 articles, 18923 (~6.72%) had missing images
- `test` split (32821 total articles): from the original 35177 articles, 2356 (~6.70%) had missing images
- `validation` split (32806 total articles): from the original 35175 articles, 2369 (~6.73%) had missing images


## Languages

fil

## Supported Tasks

Image Captioning
    
## Dataset Usage
### Using `datasets` library
```
    from datasets import load_dataset
    dset = datasets.load_dataset("SEACrowd/balita_nlp", trust_remote_code=True)
```
### Using `seacrowd` library
```import seacrowd as sc
# Load the dataset using the default config
    dset = sc.load_dataset("balita_nlp", schema="seacrowd")
# Check all available subsets (config names) of the dataset
    print(sc.available_config_names("balita_nlp"))
# Load the dataset using a specific config
    dset = sc.load_dataset_by_config_name(config_name="<config_name>")
```
    
    More details on how to load the `seacrowd` library can be found [here](https://github.com/SEACrowd/seacrowd-datahub?tab=readme-ov-file#how-to-use).
    

## Dataset Homepage

[https://github.com/KenrickLance/BalitaNLP-Dataset](https://github.com/KenrickLance/BalitaNLP-Dataset)

## Dataset Version

Source: 1.0.0. SEACrowd: 2024.06.20.

## Dataset License

Unknown (unknown)

## Citation

If you are using the **Balita Nlp** dataloader in your work, please cite the following:
```
@inproceedings{bunagtransformer,
  author={Bunag, Kenrick Lance T and Esquivel, Rosanna A}
  title={Transformer-Based Conditional Language Models to Generate Filipino News Articles},
  year = {2023},
  publisher = {IEOM Society International},
  url = {https://ieomsociety.org/proceedings/2023manila/595.pdf},
  booktitle = {Proceedings of the International Conference on Industrial Engineering and Operations Management},
  pages = {2231–2237},
  numpages = {7},
  location = {Manila, Philippines},
}


@article{lovenia2024seacrowd,
    title={SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages}, 
    author={Holy Lovenia and Rahmad Mahendra and Salsabil Maulana Akbar and Lester James V. Miranda and Jennifer Santoso and Elyanah Aco and Akhdan Fadhilah and Jonibek Mansurov and Joseph Marvin Imperial and Onno P. Kampman and Joel Ruben Antony Moniz and Muhammad Ravi Shulthan Habibi and Frederikus Hudi and Railey Montalan and Ryan Ignatius and Joanito Agili Lopo and William Nixon and Börje F. Karlsson and James Jaya and Ryandito Diandaru and Yuze Gao and Patrick Amadeus and Bin Wang and Jan Christian Blaise Cruz and Chenxi Whitehouse and Ivan Halim Parmonangan and Maria Khelli and Wenyu Zhang and Lucky Susanto and Reynard Adha Ryanda and Sonny Lazuardi Hermawan and Dan John Velasco and Muhammad Dehan Al Kautsar and Willy Fitra Hendria and Yasmin Moslem and Noah Flynn and Muhammad Farid Adilazuarda and Haochen Li and Johanes Lee and R. Damanhuri and Shuo Sun and Muhammad Reza Qorib and Amirbek Djanibekov and Wei Qi Leong and Quyet V. Do and Niklas Muennighoff and Tanrada Pansuwan and Ilham Firdausi Putra and Yan Xu and Ngee Chia Tai and Ayu Purwarianti and Sebastian Ruder and William Tjhi and Peerat Limkonchotiwat and Alham Fikri Aji and Sedrick Keh and Genta Indra Winata and Ruochen Zhang and Fajri Koto and Zheng-Xin Yong and Samuel Cahyawijaya},
    year={2024},
    eprint={2406.10118},
    journal={arXiv preprint arXiv: 2406.10118}
}

```