---
language:
- tl
pretty_name: Filipino multi-modal NLP dataset. Consists of 350k+ Filipino news articles
  and associated images
size_categories:
- 100K<n<1M
source_datasets:
- original
tags:
- news
task_categories:
- text-to-image
- image-to-text
- text-generation
- summarization
- text-classification
- text2text-generation
task_ids:
- news-articles-headline-generation
- news-articles-summarization
dataset_info:
- config_name: default
  features:
  - name: title
    dtype: string
  - name: body
    sequence: string
  - name: image
    dtype: image
  - name: website
    dtype: string
  - name: category_group
    dtype: string
  - name: category
    dtype: string
  - name: title_choice_first_paragraph
    dtype: string
  - name: title_choices
    sequence: string
  - name: title_choice_gold_idx
    dtype: int32
  - name: date
    dtype: string
  - name: author
    dtype: string
  - name: url
    dtype: string
  - name: img_url
    dtype: string
  splits:
  - name: train
    num_bytes: 39449948960.917
    num_examples: 281403
  - name: validation
    num_bytes: 5093856283.5
    num_examples: 35175
  - name: test
    num_bytes: 4923596011.806
    num_examples: 35177
  download_size: 39815624261
  dataset_size: 49467401256.223
- config_name: no-image
  features:
  - name: title
    dtype: string
  - name: body
    sequence: string
  - name: category_group
    dtype: string
  - name: category
    dtype: string
  - name: website
    dtype: string
  - name: title_choice_first_paragraph
    dtype: string
  - name: title_choices
    sequence: string
  - name: title_choice_gold_idx
    dtype: int32
  - name: date
    dtype: string
  - name: author
    dtype: string
  - name: url
    dtype: string
  - name: img_url
    dtype: string
  splits:
  - name: train
    num_bytes: 578462672
    num_examples: 281403
  - name: validation
    num_bytes: 74036069
    num_examples: 35175
  - name: test
    num_bytes: 73488921
    num_examples: 35177
  download_size: 427915786
  dataset_size: 725987662
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
- config_name: no-image
  data_files:
  - split: train
    path: no-image/train-*
  - split: validation
    path: no-image/validation-*
  - split: test
    path: no-image/test-*
---
A Filipino multi-modal language dataset for text+visual tasks. Consists of 351,755 Filipino news articles (w/ associated images) gathered from Filipino news outlets.


# Description

Total # of articles: **351,755**

80-10-10 split for training, validation, and testing.

Dataset field descriptions:  

```body           - Article text  
title                        - Article title  
image                        - Article image  
website                      - Name of the news outlet  
category_group               - Category grouped into 5 distinct classes. News, Sports, Entertainment, Crime, and Other  
category                     - News category name given by the news outlet  
date                         - Date published  
author                       - Article author  
url                          - URL of the article  
img_url                      - URL of the article image  
title_choice_first_paragraph - Opening paragraph of the article
title_choices                - 4 possible titles, one of them being the true one
title_choice_gold_idx        - Idx of the true title among the choices
```

title_choice_* fields are included to support the task of textual entailment — taking advantage of the "inverted pyramid" structure of news articles.

# Dataset Usage
Two dataset configurations: **default** (includes images) and **no-image** (excludes images)

**Using** `datasets` **library**

**default**
```
from datasets import load_dataset
dset = load_dataset('LanceBunag/BalitaNLP', streaming=True) # streaming recommended due to size of dataset w/ images
```

**no-image**
```
from datasets import load_dataset
dset = load_dataset('LanceBunag/BalitaNLP', 'no-image')
```

# Citation
Published in [Buñag & Esquivel, 2023](https://storage.googleapis.com/public-kenricklancebunag/Transformer-based%20Conditional%20Language%20Models%20-%20IEOM%20Submission.pdf).
If you are using **BalitaNLP** 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},
}
```