alaminerca/sango-french-bible-parallel

SFPC: Sango-French Parallel Corpus

The first quality-filtered, verse-aligned Sango-French parallel corpus, constructed for neural machine translation research. This dataset directly addresses the "Sango Problem" identified by Meta's NLLB-200 project — the failure of cross-lingual transfer for a linguistically isolated Creole language.

Associated resources:

• Model: alaminerca/nllb-sango-french
• Demo: Sango-French Translator
• Paper: SangoNMT: Parameter-Efficient Domain Adaptation of NLLB-200 for Sango, an Isolated Creole Language (Submitted, 2026)

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Dataset Summary

Statistic	Value
Total verse pairs	21,125
Bidirectional examples	42,250
Languages	Sango (sag_Latn) ↔ French (fra_Latn)
Domain	Biblical text
Source texts	Sango Bible (Tënë ti Nzâpä, 2010) + French Darby Bible
License	CC-BY-4.0

Splits

Split	Books	Verse Pairs	Bidirectional Examples
Train	Remaining (all genres)	18,423	36,846
Validation	Ruth, Jonah, Philippians, Ecclesiastes, Mark	862	1,724
Test	Esther, Amos, Galatians, Proverbs, John	1,840	3,680
Total	66 books	21,125	42,250

Splits are at book level (not random) to prevent data leakage. Adjacent Bible verses share vocabulary and themes; random splitting would inflate evaluation scores. Validation and test books cover all five major Biblical genres.

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Data Format

Each example is a JSON object with the following fields:

{
  "id": "genesis_1_1_fr2sg",
  "source_lang": "fra_Latn",
  "target_lang": "sag_Latn",
  "source_text": "Au commencement Dieu créa les cieux et la terre.",
  "target_text": "Na tongo nda ni, Nzapa asara yayu na sese.",
  "book": "Genesis",
  "chapter": 1,
  "verse": 1
}

Field	Description
id	Unique identifier: {book}_{chapter}_{verse}_{direction}
source_lang	Source language code (NLLB format): fra_Latn or sag_Latn
target_lang	Target language code (NLLB format): sag_Latn or fra_Latn
source_text	Source sentence
target_text	Target (reference) sentence
book	Biblical book name
chapter	Chapter number
verse	Verse number

The dataset is bidirectional: each verse pair appears twice (French→Sango and Sango→French), enabling a single model to translate in both directions.

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Construction Pipeline

The corpus was constructed through a four-stage pipeline:

1. PDF Extraction — Text extracted from Sango and French Bible PDFs using PyMuPDF with regex-based verse identification
2. Verse-Level Alignment — Verses aligned by standardized book-chapter-verse addresses across both Bibles
3. Encoding Normalization — Systematic UTF-8 mojibake patterns corrected (40+ known corruption patterns)
4. Quality Filtering — Three filters applied: minimum 10 characters per side, length ratio within 1:3 to 3:1, content presence in both languages. Removed 849 pairs (3.9%)

Alignment Statistics

Statistic	Value
Total verse addresses	27,768
Successfully aligned	20,690
Sango only (no French match)	5,081
French only (no Sango match)	1,997
Alignment rate	74.5%

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Genre Distribution

Genre	Train	Validation	Test
Narrative	15,842	144	250
Prophecy	7,944	82	210
Gospel	3,396	1,062	1,508
Poetry/Wisdom	2,300	344	1,502
Epistle	2,688	92	210
Apocalyptic	622	—	—
Law	4,054	—	—

Counts are bidirectional examples.

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Text Length Statistics

Split	Mean (chars)	Min	Max
Train	173	17	1,874
Validation	154	28	678
Test	135	12	648

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Usage

from datasets import load_dataset

dataset = load_dataset("alaminerca/sango-french-bible-parallel")

# Access splits
train = dataset["train"]
val = dataset["validation"]
test = dataset["test"]

# Example
print(train[0])

Use with NLLB-200

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model = AutoModelForSeq2SeqLM.from_pretrained("alaminerca/nllb-sango-french")
tokenizer = AutoTokenizer.from_pretrained("alaminerca/nllb-sango-french")

# Get a test example
example = dataset["test"][0]
tokenizer.src_lang = example["source_lang"]

inputs = tokenizer(example["source_text"], return_tensors="pt", max_length=256, truncation=True)
tgt_id = tokenizer.convert_tokens_to_ids(example["target_lang"])
output = model.generate(**inputs, forced_bos_token_id=tgt_id, max_new_tokens=256, num_beams=3)
print(tokenizer.decode(output[0], skip_special_tokens=True))

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About Sango

Sango (ISO 639-3: sag) is the national language of the Central African Republic, spoken by approximately 5.5 million people. It is a creole-like language derived from Ngbandi (Ubangian family), characterized by tonal distinctions, minimal morphology, and SVO word order.

The NLLB-200 project identified Sango as the only Creole language for which no similar high-resource language could be found for cross-lingual transfer, resulting in the lowest Creole performance (5.3 BLEU with 1.9M mined sentences). This dataset provides high-quality, human-translated parallel data as an alternative to noisy mined data.

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Limitations

• Domain: Biblical text only. The formal register of Biblical Sango differs from everyday conversational usage.
• Register: Written Sango is almost exclusively formal; this corpus reflects that bias.
• Tone: Sango is tonal but tone markings are inconsistent in the source texts.
• Coverage: 66 of 73 standard Biblical books (some books had 0% alignment rate).

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Citation

@article{mouhamad2026sangonmt,
  title={SangoNMT: Parameter-Efficient Domain Adaptation of NLLB-200 for Sango, an Isolated Creole Language},
  author={Mouhamad, Alim Al-Amine and Alkhodre, Ahmad B. and Alsaawy, Yazed},
  year={2026},
  note={Submitted}
}

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Authors

Alim Al-Amine Mouhamad, Ahmad B. Alkhodre, Yazed Alsaawy — Department of Computer Science, Islamic University of Madinah, KSA