---
dataset_info:
  features:
  - name: en
    dtype: string
  - name: th
    dtype: string
  splits:
  - name: train
    num_bytes: 15352952
    num_examples: 31102
  download_size: 6074585
  dataset_size: 15352952
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: unlicense
task_categories:
- translation
- text-generation
- text-classification
language:
- en
- th
tags:
- bilingual
- parallel-corpus
- machine-translation
- text-generation
- text-classification
- religious-text
- English
- Thai
- Bible
- King-James-Version
- nlp
- sequence-to-sequence
- translation-en-th
pretty_name: Bible Thai English Translation
size_categories:
- 10K<n<100K
---

# bible-en-th

## Overview
The `bible-en-th` dataset is a bilingual corpus containing English and Thai translations of the Bible, specifically the King James Version (KJV) translated into Thai. This dataset is designed for various natural language processing tasks, including translation, language modeling, and text analysis.

* **Languages**: English (en) and Thai (th)
* **Total Rows**: 31,102

## Dataset Structure
The dataset consists of two main features:
* **en**: English text from the Bible.
* **th**: Corresponding Thai text from the King James Version.

## Sample Data
The dataset includes the following sections of the Bible:

<details>
<summary>Click to show/hide Bible sections</summary>

* Genesis
* Exodus
* Leviticus
* Numbers
* Deuteronomy
* Joshua
* Judges
* Ruth
* 1 Samuel
* 2 Samuel
* 1 Kings
* 2 Kings
* 1 Chronicles
* 2 Chronicles
* Ezra
* Nehemiah
* Esther
* Job
* Psalms
* Proverbs
* Ecclesiastes
* Song of Solomon
* Isaiah
* Jeremiah
* Lamentations
* Ezekiel
* Daniel
* Hosea
* Joel
* Amos
* Obadiah
* Jonah
* Micah
* Nahum
* Habakkuk
* Zephaniah
* Haggai
* Zechariah
* Malachi

</details>

## Installation
To access this dataset, you can load it using the Hugging Face `datasets` library:

```python
from datasets import load_dataset

dataset = load_dataset("hf_datasets/bible-en-th")
```

## Streaming the Dataset
For efficient memory usage, especially when working with large datasets, you can stream the dataset:
```python
from datasets import load_dataset

streamed_dataset = load_dataset("hf_datasets/bible-en-th", streaming=True)

for example in streamed_dataset["train"]:
    print(example["en"])
```

Streaming is particularly useful when you want to process the dataset sequentially without loading it entirely into memory.

## License
This dataset is released as unlicensed.