suchut
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nllb-200-distilled-bible-en-th

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 ---
 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.