---
library_name: transformers
language:
- th
- en
pipeline_tag: translation
license: cc-by-nc-4.0
---

# NLLB-200 Distilled: English-Thai Bible Translation Model

## Introduction

The **NLLB-200 Distilled: English-Thai Bible Translation Model** is a fine-tuned version of the [facebook/nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M), designed specifically for **bidirectional Bible translation** between English and Thai. This model provides translations for both **English-to-Thai** and **Thai-to-English** directions, suitable for applications involving religious texts, with attention to context and meaning.

## Training Dataset

This model was fine-tuned using a combination of religious and general domain datasets to provide accurate and context-aware translations, especially for biblical texts:

1. **Bible-Specific English-Thai Datasets:**

   - [Tsunnami/en-th-bible](https://huggingface.co/datasets/Tsunnami/en-th-bible): Bible-focused dataset providing parallel English-Thai verses.
   - [Tsunnami/en-th-bible-splits](https://huggingface.co/datasets/Tsunnami/en-th-bible-splits): A split version optimized for training.

2. **General English-Thai Datasets for Broader Linguistic Coverage:**
   - [Tsunnami/who-en-th](https://huggingface.co/datasets/Tsunnami/who-en-th): General-purpose English-Thai data to improve the model's understanding of diverse vocabulary.
   - [scb10x/scb_mt_enth_2020_aqdf_1k](https://huggingface.co/datasets/scb10x/scb_mt_enth_2020_aqdf_1k): Additional English-Thai dataset to enhance linguistic robustness.

In total, the model was trained on **32,441 rows** of English-Thai text pairs, with a focus on both biblical and general language use.

## Training Methodology

The **NLLB-200 Distilled: English-Thai Bible Translation Model** integrates the NLLB-200 default tokenizer to process both English and Thai text effectively. Training for this bidirectional model, covering both English-to-Thai and Thai-to-English translations, was completed in **7 GPU hours** on **NVIDIA P100-16GB** (250W TDP) hardware, using **Python Lightning** for efficient model training and management. This training approach ensures the model can handle input in either language and generate accurate translations in the target direction.

## How to Use This Model for Bidirectional Bible Translation

The model can translate Bible verses in **both directions**: from **English to Thai** and from **Thai to English**. The following example demonstrates Thai-to-English translation; to reverse the translation direction, switch the `source_lang` and `target_lang` variables accordingly.

```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

# Load the model and tokenizer
model_name = "suchut/nllb-200-distilled-bible-en-th"
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Set source and target languages for bidirectional translation
# Example 1: Thai-to-English
source_lang = "tha_Thai"  # Thai as source language
target_lang = "eng_Latn"  # English as target language

# Example input text in Thai
input_text = "ในเริ่มแรกนั้นพระเจ้าทรงเนรมิตสร้างฟ้าและแผ่นดินโลก"

# Tokenize the input text with language prefix
inputs = tokenizer(f"{source_lang} {input_text}", return_tensors="pt")

# Generate the translation and force output to target language
translated_tokens = model.generate(
    **inputs,
    forced_bos_token_id=tokenizer.encode(target_lang)[0],
    max_length=128
)

# Decode and print the translated text
decoded_translation = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
print("Translated Text (Thai to English):", decoded_translation)

# To translate from English to Thai, simply switch source_lang and target_lang:
# source_lang = "eng_Latn"
# target_lang = "tha_Thai"
# input_text = "In the beginning, God created the heavens and the earth."
```

### Key Points to Note

1. **Bidirectional Translation:** This model’s bidirectional capability allows translation **both from English to Thai and from Thai to English**. Specify `source_lang` and `target_lang` parameters to choose the translation direction as needed.

2. **Language Codes:** Use `"eng_Latn"` for English and `"tha_Thai"` for Thai. Prefix the input text with the `source_lang` code to guide the model's translation processing.

3. **Forced BOS Token for Target Language:** To ensure correct output language, the model uses `forced_bos_token_id`, setting the beginning of the output to the specified target language.

This model provides an essential tool for bilingual Bible translation, suitable for applications in religious studies, cross-linguistic analysis, and more.