MEYNG/sango-vocabulary

Sango Vocabulary Dataset

Dataset Description

An open, structured, machine-readable trilingual vocabulary dataset for Sango (ISO 639-1: sg, ISO 639-3: sag), the national and most widely spoken language of the Central African Republic. Sango is a creole language with over 5 million speakers, yet it remains severely underrepresented in NLP research and digital resources.

This dataset provides trilingual vocabulary entries (Sango-French-English) with semantic categorization and example sentences, along with Sango-French translation pairs extracted from grammar and dictionary references.

Supported Tasks

• Machine Translation: Sango-French and Sango-English translation
• Language Modeling: Pre-training or fine-tuning language models for Sango
• Cross-lingual Transfer: Leveraging French/English representations for Sango NLP
• Language Documentation: Digital preservation of Sango vocabulary and usage patterns
• Educational Technology: Building language learning applications for Sango

Languages

Language	ISO 639-1	ISO 639-3	Role
Sango	sg	sag	Primary target language
French	fr	fra	Primary bridge language
English	en	eng	Secondary bridge language

Dataset Structure

Vocabulary (vocabulary.jsonl)

607 verified trilingual vocabulary entries (v1.4.7), each containing:

Field	Type	Description	Coverage
word_id	string	Stable UUID for the entry	100%
sango	string	Sango word or phrase	100%
french	string	French translation	100%
english	string	English translation	100%
category	string	Semantic / grammatical category	100%
difficulty	string	Learning level (beginner/intermediate/advanced/expert)	100%
example_sango	string	Example sentence in Sango	~84%
example_french	string	Example sentence in French	~67%
confidence	float	Curation-pipeline confidence score (0–1)	100%
status	string	Curation status (currently all verified)	100%
source	string	Provenance tag (e.g. textbook-myc, nllb-enrichment, admin)	~99%
source_url	string	Source URL where applicable	partial

Example entry:

{
  "word_id": "a4dec056-d560-4733-a8cf-47514bedb410",
  "sango": "abata",
  "french": "garder, préserver, maintenir",
  "english": "keep, preserve, maintain",
  "category": "verb_action",
  "difficulty": "intermediate",
  "example_sango": "Zo so abata atënë ayeke na kube.",
  "example_french": "Celui qui garde les paroles est béni.",
  "confidence": 0.72,
  "status": "verified",
  "source": "nllb_en_corpus"
}

Training Pairs (training_pairs.jsonl)

360 Sango-French translation pairs extracted from grammar and dictionary reference materials.

Field	Type	Description
source_lang	string	Source language code (sg or fr)
target_lang	string	Target language code (sg or fr)
source	string	Source text
target	string	Target text

Note on quality: These pairs were extracted from a Sango grammar reference book. While many entries contain genuine Sango-French translations and vocabulary definitions, some are sentence fragments or contextual excerpts from the source material. Users should apply filtering for downstream tasks that require clean parallel sentences.

Example entry:

{
  "source_lang": "sg",
  "target_lang": "fr",
  "source": "Mbï yê Bêafrîka mîngi",
  "target": "j'aime beaucoup la Centrafrique"
}

Category Distribution

The vocabulary spans 42 semantic categories. The largest (v1.4.1): essential (173), verbs (69), places (53), numbers (25), actions (24), food (23), noun (22), time (20), adjectives (20), animals (20). For the exact, current per-category distribution, use the HuggingFace dataset viewer — counts shift as the dataset grows, so they are not hardcoded here.

Difficulty Distribution

Level	Count	Percentage
Beginner	338	51.8%
Intermediate	219	33.5%
Advanced	95	14.5%
Expert	1	0.2%

Dataset Creation

Source Data

• Vocabulary entries: Curated by MEYNG from Sango language-learning resources (including japprendslesango.com), a Sango grammar/dictionary reference, and a quality-filtered French-Sango parallel corpus (NLLB). Provenance is recorded per entry in the source field.
• Training pairs: Extracted from a comprehensive Sango grammar and dictionary reference work.
• Verification: Entries are cross-referenced against the above sources and flagged verified in the curation pipeline.

Annotation Process

Vocabulary entries were structured and categorized by MEYNG's founder, a native Sango speaker, drawing on textbook, dictionary, and parallel-corpus sources. Each entry carries a confidence score and a status flag from the curation pipeline. Most entries are pipeline-verified (cross-referenced against the sources above) rather than individually confirmed by a native speaker — broader native-speaker re-verification, via a community panel, is in progress.

Provenance breakdown (v1.4.7, all 607 entries)

status: verified is a pipeline flag. The table below shows how each entry actually entered the set, so users can judge reliability directly rather than trusting a single "verified" label. Only 1 of 607 entries has so far been individually confirmed by a native speaker (source: native_speaker); the rest are textbook-, corpus-, or AI-derived and cross-referenced. Growing the native-speaker-confirmed share is the dataset's active roadmap (see the SangoAI vocabulary-verification workflow).

Provenance group	Entries	Origin
NLLB parallel-corpus enrichment	316	nllb-enrichment, nllb_corpus, nllb_en_corpus
Textbook (CAR primer / dictionary)	174	textbook-myc-review, textbook-batch-import, textbook-myc
Admin / CSV import	88	admin, csv-import
AI prompt enrichment	18	prompt-vocabulary
Swadesh list	3	swadesh_list
Wiktionary	1	fr_wiktionary
Native speaker (individually confirmed)	1	native_speaker
Unlabelled	6	(blank source)

For the full question-by-question provenance and collection detail, see DATASHEET.md (Datasheet for Datasets, Gebru et al. 2021).

Considerations for Using the Data

Known Limitations

1. Dataset size: This release contains 607 verified vocabulary entries (v1.4.7) and 360 translation pairs, plus grammar_rules.json with 13 structured Sango grammar rules. The vocabulary was consolidated and deduplicated from a larger raw set (capitalization variants and wrong-spelling collisions removed). All 607 entries are unique by (sango, french); 1 Sango spelling (mû) recurs with distinct French senses — a genuine polyseme, glossed both "prendre, saisir" and "donner" with different argument structures (mû X vs mû X na Y). Corrected 2026-07-22: v1.4.0 stated 4 such spellings and called them all legitimate polysemes. Three were not — yongoro (same sense reworded), dê (a "winter" gloss lifted from 2 Timothy 4:21, not a Sango sense) and séléka (a "mariage" gloss; Séléka means alliance in Sango). All three were duplicates from a double corpus-import on 2026-05-21 and have been removed.

2. Verification semantics: status: verified is a curation-pipeline flag (cross-referenced against textbook, dictionary, and parallel-corpus sources). The majority of entries were machine-enriched or textbook-imported; only a small fraction have been individually confirmed by a native speaker so far. Treat confidence accordingly and apply your own filtering for high-stakes use.

3. Training pair quality: The translation pairs contain some fragmented text from the source material. Filtering is recommended for tasks requiring clean parallel data.

4. Example sentences: Roughly 504 of 607 entries (83.0%) include a Sango example sentence and ~419 (69.0%) include a French example; English examples are not yet included. Coverage fell from 84.8% in v1.4.1 on purpose: 18 example sentences that did not contain their own headword were removed on 2026-07-22 rather than left in place — séléka was illustrated by a sentence not containing the word séléka, doli (éléphant) by one about a monkey. They are not being replaced with generated text: writing plausible Sango to fill the gap would inject invented language into a corpus whose value is that it documents a real one. Attested replacement candidates have been mined from the aligned corpus and await native-speaker approval. Every remaining example now contains its headword, apart from two multi-word entries matched discontinuously (correct Sango).

5. Pronunciation: The current release does not include a pronunciation field. Learner-oriented phonetic guides are available in the SangoAI app and are planned for a future dataset version.

6. Dialect coverage: Sango has regional variations. This dataset primarily represents the standardized Sango used in Bangui (the capital).

7. Gloss reliability (new in v1.4.2): every textbook-sourced gloss was checked against a 45,451-pair aligned FR–Sango corpus. 41 entries whose gloss was directly contradicted by attested usage were withdrawn — e.g. bîa was glossed couper while every corpus occurrence aligns with chanson/chante/musique; nzë was glossed moustique against mois/lune/année. They are moved to pending re-glossing, not deleted. 12 entries could not be corroborated OR refuted and remain published, individually identified in the project's TERMINAL-TRIAGE.csv. They are everyday concrete vocabulary — éléphant, pioche, piège — occurring 11–42 times in the aligned corpora against 1,721–18,531 for register-typical words. The corpus is structurally blind to this vocabulary class, so more of the same text would not settle them; they need a native speaker or a non-religious corpus. Treat those 12 as unverified rather than verified. A further 3 previously flagged entries were cleared by the corpus; the audit deliberately reports a review queue, not verdicts, because the method produces false positives (it flagged yeke=être, which is correct — French inflection defeats the matcher). Roughly a third of this dataset is textbook-sourced and had never been corpus-checked before v1.4.2. Extended in v1.4.7: the classifier behind that audit promoted an entry to "corroborated" on a single agreeing sentence, which removed 83 entries from review permanently. Re-running it with a rate threshold surfaced 45 entries agreeing on under 5% of their own attested occurrences, and two of them reproduced withdrawals already made on the same evidence: nze (sueur/transpirer, 0.0% in-context against lune/mois) and nzere (rire/sourire, 1.3% against plaire/agréable). Their spelling twins nzë and anzere had been withdrawn in earlier rounds while these were left published. Both are now withdrawn to pending, 609 → 607. The remaining 43 are a review sheet, not a queue of errors: several are synonyms the matcher cannot see (bâgara = vache/boeuf against taureaux) and one is a demonstrative particle with no competing sense at all.

Recommendations

• Use as a seed dataset to bootstrap Sango NLP systems
• Access the full dataset directly via the SangoAI API at https://sangoai.sbs (the live verified count grows over time and may exceed this file's snapshot)
• Apply quality filtering on training_pairs.jsonl (and on confidence) before fine-tuning
• Consider data augmentation techniques for downstream tasks

Citation

If you use this dataset in your research, please cite:

@dataset{meyng_sango_vocabulary_2026,
  title={Sango Vocabulary Dataset: A Trilingual Lexical Resource for an Underrepresented African Language},
  author={MEYNG},
  year={2026},
  url={https://huggingface.co/datasets/MEYNG/sango-vocabulary},
  license={CC-BY-SA-4.0},
  language={sg, fr, en}
}

License

This dataset is released under the Creative Commons Attribution-ShareAlike 4.0 International License (CC-BY-SA-4.0).

You are free to:

• Share -- copy and redistribute the material in any medium or format
• Adapt -- remix, transform, and build upon the material for any purpose, including commercial

Under the following terms:

• Attribution -- You must give appropriate credit to MEYNG, provide a link to the license, and indicate if changes were made
• ShareAlike -- If you remix, transform, or build upon the material, you must distribute your contributions under the same license

Acknowledgments

This dataset was created by MEYNG as part of the SangoAI project, an AI-powered language platform dedicated to the preservation and digitization of Sango.

We acknowledge:

• The people of the Central African Republic, whose language and culture this dataset aims to preserve and promote
• The Sango-speaking community worldwide for their contributions to language documentation
• The creators of japprendslesango.com and other Sango learning resources that informed this work
• The open-source NLP community working on low-resource African languages

Contact

• Organization: MEYNG
• Website: meyng.com
• Platform: sangoai.sbs
• Email: contact@meyng.com
• GitHub: github.com/meyng-hub/sangoai