alakxender/dhivehi-english-translations

Dhivehi-English Translation Dataset

Dataset Description

This dataset contains 91,759 Dhivehi-English translation pairs extracted from news articles and other content. The dataset is designed for machine translation research, cross-lingual information retrieval, and Dhivehi language processing tasks.

Languages

• Source: Dhivehi (dv) - The official language of the Maldives
• Target: English (en)

Dataset Structure

Data Instances

{
  "dhivehi": "މަގުމަތީ އުޅޭ ބުޅާތަކަށް ހާނިއްކަކޮށް ނުމަރަން އިލްތިމާސްކޮށްފި",
  "english": "Appeal Made Not to Harm or Kill Stray Cats",
  "topic": "Environment"
}

Data Fields

• dhivehi (string): The original text in Dhivehi
• english (string): The corresponding English translation
• topic (string): The content category/topic

Data Splits

Split	Examples
Train	82,583
Test	9,176
Total	91,759

Topic Distribution

The dataset covers various topics with the following distribution:

Topic	Count	Percentage
Politics	22,145	24.1%
Local News	12,331	13.4%
Crime	11,430	12.5%
Business	10,356	11.3%
Sports	8,021	8.7%
International	6,593	7.2%
Health	5,358	5.8%
Entertainment	4,370	4.8%
Education	3,900	4.3%
Environment	2,545	2.8%
Others (236 topics)	4,710	5.1%

Dataset Creation

Source Data

The data was extracted and cleaned from Dhivehi news articles and other textual content.

Statistics

• Average Dhivehi text length: ~83 characters
• Average English text length: ~71 characters

Usage

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("alakxender/dhivehi-english-translations")

# Access train/test splits
train_data = dataset['train']
test_data = dataset['test']

# Example usage
for example in train_data.select(range(5)):
    print(f"Dhivehi: {example['dhivehi']}")
    print(f"English: {example['english']}")
    print(f"Topic: {example['topic']}")
    print("-" * 50)

Applications

This dataset can be used for:

• Machine Translation: Training Dhivehi ↔ English translation models
• Cross-lingual Research: Studying language transfer and multilingual representations
• Low-resource Language Processing: Dhivehi NLP research and development
• Evaluation: Benchmarking translation systems for Dhivehi-English

Considerations for Use

Limitations

• The dataset reflects the content and style of the source material (primarily news)
• Translation quality may vary across different topics and domains
• Dhivehi is a low-resource language with limited parallel data