# Datasheet — Sango Vocabulary Dataset

Following the _Datasheets for Datasets_ framework (Gebru et al., 2021, _CACM_) and
informed by _Data Statements for NLP_ (Bender & Friedman, 2018). All counts are
computed directly from the published files — recounted 2026-09-04 from the shipped
`vocabulary.jsonl` — and are honest about what the `verified` flag does and does not mean.

- **Dataset**: `MEYNG/sango-vocabulary` — https://huggingface.co/datasets/MEYNG/sango-vocabulary
- **Version**: 1.5.5 · **Maintainer**: MEYNG (`contact@meyng.com`)
  <br><sub>*Corrected 2026-09-05: this field read `1.4.7` while the `metadata.json` shipped
  beside it read `1.5.4`. The version is taken from that file, which is regenerated from
  `vocabulary.jsonl` itself rather than written by hand. Some table captions in the dataset
  card still say `v1.5.0`; those label when a specific breakdown was computed and are being
  reviewed separately.*</sub>
- **Files**: `vocabulary.jsonl` (590 entries), `training_pairs.jsonl` (360 pairs), `grammar_rules.json` (13 rules)
- **Languages**: Sango (`sag`), French (`fra`), English (`eng`)
- **Licence**: CC-BY-SA-4.0 — ⚠️ **partly under review. Read [Provenance and license
  status](#provenance-and-license-status) before redistributing.**

---

## Provenance and license status

**The CC-BY-SA-4.0 claim over part of this dataset is under review.** This section states
the problem rather than leaving it to be discovered downstream.

**316 of the 590 entries (53.6%) are NLLB-derived** — `nllb-enrichment` (250),
`nllb_corpus` (39), `nllb_en_corpus` (27) — extracted from the web-mined Sango–French
bitext distributed by AllenAI. Counted directly from the shipped `vocabulary.jsonl` on
2026-09-04, not carried over from a previous release.

**The upstream chain is ODC-BY, and ODC-BY carries an attribution requirement.** That
attribution is set out in the dataset card under *Source attribution*, and any
redistributor must carry it forward. But ODC-BY governs **AllenAI's compilation**, not the
crawled pages the bitext was mined from — the AllenAI card also binds users to *"the
respective Terms of Use and License of the original source"*. Those upstream terms are
**not established**. Of the 70 entries carrying a `source_url`, **64 point at jw.org**, a
single publisher whose own terms do not permit redistribution.

**That chain is recorded as CONTESTED** in the SangoAI data-source registry
(`docs/SOURCES.md`, § CONTESTED, verified **2026-09-04**). MEYNG's internal
data-governance policy forbids publishing the underlying 45,451-pair corpus on the
compilation licence alone. **That prohibition was written for the corpus and does not
retroactively cover this derived lexicon, which was already public when the chain was
marked CONTESTED.** We are naming that gap rather than sheltering behind it.

**What resolves it:** whether AllenAI publishes **per-language provenance for `sag_Latn`**
identifying the upstream documents behind the Sango mine. That one check settles the
question for this dataset and for the corpus together.

**What we commit to once it resolves:**

| Outcome | What we do |
| --- | --- |
| Upstream **permissive** | Keep the dataset at full size and **carry the attribution chain explicitly**, naming the upstream terms alongside ODC-BY. |
| Upstream **restricted** | **Cut a clean release from the 274 non-NLLB entries** and withhold the 316 NLLB-derived rows until they can be re-derived from CLEARED sources. |
| Outcome **ambiguous** | Take **IP counsel before choosing** between the two. We will not settle an ambiguous licence question by picking whichever answer keeps the dataset larger. |

**If you need a licence chain clean to a named source today,** filter `source` to the
**264** entries with a named non-NLLB origin. The 6 `admin` and 4 blank rows are **not** a
clean subset — their origin is unrecorded, which is a separate problem that avoiding NLLB
does not solve.

**This is our reasoning, not legal advice,** and one question is genuinely open: the EU
*sui generis* database right protects substantial extraction from a database independently
of whether its contents are facts. We have not taken advice on whether it applies here.

---

## 1. Motivation

- **Why was the dataset created?** Sango is the co-official language of the Central
  African Republic (with French; ~5M speakers) and is severely under-represented in NLP. No open,
  structured, machine-readable trilingual Sango lexicon existed. This dataset is the
  seed lexical resource for SangoAI's vocabulary-augmented prompting and for any
  researcher/NGO building Sango NLP.
- **Who created it and for whom?** MEYNG, an African-language-AI project founded by a
  native Sango speaker from the CAR, for the open low-resource-NLP community.
- **Who funded it?** MEYNG (self-funded); AI enrichment used cloud LLM inference credits.

## 2. Composition

- **What do instances represent?** Two kinds:
  1. **Vocabulary** (590 rows): a Sango word/phrase with French + English translations,
     a semantic `category`, a `difficulty` level, optional example sentences, a
     pipeline `confidence` score (0–1, present on 100% of rows), a `status`, a
     `source` provenance tag, and `source_pages` — the page(s) of the CAR primer
     the headword came from, present on **210 of the 590** rows (new in v1.5.5).
     An empty `source_pages` means **not established**, never "no page": 46
     primer-sourced rows matched no OCR worksheet row, and the 334 non-primer
     rows do not come from a book. Where a headword is taught on several pages,
     **all** of them are listed — 13 rows — because choosing one would be a guess
     presented as provenance.
  2. **Training pairs** (360 rows): Sango→French text pairs extracted from a grammar/
     dictionary reference.
- **How many instances?** 590 vocabulary entries; 360 translation pairs; 13 grammar
  rules. Vocabulary is **unique by `(sango, french)`**; 4 Sango spellings recur with
  distinct French senses (**polysemes**, deliberately kept — not duplicates).
- **Is it a sample or complete?** It is the current verified subset of a larger,
  growing curation pipeline (4,995 additional candidates await native-speaker review).
- **What is the "verified" label?** `status: verified` is a **curation-pipeline** flag
  (cross-referenced against textbook/dictionary/corpus sources). It is **not**
  native-speaker sign-off. Provenance of the 590 entries:

  | Provenance group                            | Entries |
  | ------------------------------------------- | ------- |
  | NLLB parallel-corpus enrichment             | 316     |
  | Textbook (CAR primer / dictionary)          | 256     |
  | Origin not recorded (`admin`)               | 6       |
  | Unlabelled (blank `source`)                 | 4       |
  | Swadesh list                                | 3       |
  | Native-speaker audit session (2026-04-20)   | 2       |
  | **Native speaker (individually confirmed)** | **1**   |
  | Wiktionary                                  | 1       |
  | Community contributor                       | 1       |
  | **Total**                                   | **590** |

  ⚠️ **Corrected 2026-09-04.** The previous version of this table carried an "AI prompt
  enrichment — 18" row and counted unrecorded origins twice (6 + 6), summing to **610**
  against a 590-row file. The 18 AI-prompt-sourced entries were **withdrawn on 2026-09-04**
  — MEYNG's data-honesty rule forbids generated Sango entering the corpus as attested, and
  those rows had been published as `verified`. Unrecorded origins are 6 `admin` + 4 blank =
  10. Every figure above is recounted from the shipped file.

- **Are there labels/targets?** The French/English translations are the targets for MT;
  `category`/`difficulty` support classification and curriculum tasks.
- **Missing information?** Example sentences cover **81.7% (Sango, 482 of 590)** and
  **67.3% (French, 397 of 590)**; English examples are absent. *(Corrected 2026-09-04: this
  line read "~84.8% / ~68.1%" alongside its own "482 of 590", which is 81.7% — the
  percentages were stale against the counts beside them. Both recounted from the shipped
  file.)* **No pronunciation/audio field** (0/590) — a known gap for a
  tonal language. Some `training_pairs.jsonl` rows are fragments, not clean sentences.
- **Confidential / sensitive / personal data?** None. The data is general-domain
  vocabulary; it contains no personal, identifying, or sensitive information.
- **Dialect / register scope?** Primarily standardised Sango as used in Bangui.
  Regional variants and specialised registers (medical, legal, agricultural) are
  under-represented.

## 3. Collection process

- **How was the data acquired?** (a) OCR of the _Kîrîndönî_ CAR primary-school Sango
  primer (photo-captured pages, cleaned to CSV); (b) extraction/enrichment from the
  quality-filtered NLLB `fra_Latn–sag_Latn` / `eng_Latn–sag_Latn` corpora
  (LASER ≥ 1.0, target-LID ≥ 0.9); (c) AI (LLM) enrichment of candidate translations;
  (d) a Swadesh list and French Wiktionary; (e) admin/manual entry.
- **`japprendslesango.com` is NOT an acquisition source.** An earlier version of this
  list named it. No entry carries it as a `source` or `source_url` — checked across all
  607 entries on 2026-08-13 — and its terms reserve all rights ("toute reproduction sans
  autorisation est interdite", `conditions-generales`, last updated 2025-02-24). It is
  credited in the dataset card's Acknowledgments as a resource that **informed** this
  work, which is what it was. Do not re-add it here without evidence of an entry that
  came from it.
- **Who was involved?** MEYNG's founder (native Sango speaker) structured, categorised,
  and reviewed entries; automated pipelines performed extraction and enrichment.
- **Over what timeframe?** Curated 2026-04 onward; this snapshot is v1.4.1 (2026-07).
- **Ethical review?** No formal IRB; the data is non-personal public-domain vocabulary
  and educational material.

## 4. Preprocessing / cleaning / labelling

- **What was done?** Deduplication (capitalisation variants and wrong-spelling
  collisions removed; polysemes preserved on the `(sango, french)` key); orthographic
  normalisation toward the standardised CAR Sango orthography (Diki-Kidiri; seven
  phonemic vowels + tonal diacritics); trilingual alignment; category/difficulty
  labelling; per-entry `confidence` and `source` tagging.
- **Is raw data saved?** The candidate pool and intermediate CSVs are retained in the
  SangoAI repository (`scripts/data-collection/`).
- **Is the cleaning software available?** The pipeline scripts (prioritisation,
  enrichment, bulk import) are in the SangoAI repository.

## 5. Uses

- **Intended uses:** seed lexicon for Sango NLP; grounding for vocabulary-augmented
  prompting; language-learning/education apps; language documentation; cross-lingual
  transfer experiments.
- **Not (yet) suitable for, without added native verification:** high-stakes
  medical/legal/financial translation; clean-parallel-sentence MT training from
  `training_pairs.jsonl` without filtering.
- **Honest note on the companion model.** A fine-tuned NMT model
  (`MEYNG/nllb-sango-finetuned-600m`) was trained on the NLLB Sango corpus. It improves
  in-distribution BLEU/chrF but **regresses below its untrained baseline on
  out-of-distribution (FLORES-200) text** — it memorised a corpus domain rather than
  learning the language. Users should treat that model as domain-specific and prefer
  vocabulary-grounded prompting for out-of-domain and culturally-specific text.
- **What users should know to avoid harm:** do not present pipeline-verified entries as
  native-speaker-certified; apply `confidence` filtering; expect Bangui-standard
  register; the 1-native-confirmed count is stated so reliability is not overstated.

## 6. Distribution

- **How distributed?** HuggingFace Datasets Hub (`MEYNG/sango-vocabulary`), loadable via
  `datasets.load_dataset`.
- **Licence:** CC-BY-SA-4.0 (attribution + share-alike). Chosen deliberately so
  derivatives stay open — the verified lexicon cannot be enclosed in a closed product.
  (The companion model is CC-BY-NC-4.0; the licences differ by design — the model is
  non-commercial, the data is open share-alike.)
- **Fees / restrictions:** none beyond the licence.

## 7. Maintenance

- **Who maintains it?** MEYNG (`contact@meyng.com`).
- **How is it updated?** The verified count grows as native-speaker verification
  proceeds (target: 3,000 verified entries). Updates are pushed to the Hub with a
  version bump and a refreshed card + this datasheet.
- **How can others contribute?** Native Sango speakers, Ubangian-language linguists, and
  researchers can open PRs on the Hub; domain-specific additions (medical, legal,
  agricultural, financial) are specifically welcomed.
- **Erratum / versioning policy:** counts on the card and in `metadata.json` are
  recomputed from the shipped file at each release; the live SangoAI API count is a
  separate, growing metric and may differ from any file snapshot.

---

_Prepared 2026-07-07. If you find an error — especially a translation a native speaker
would correct — please open an issue or PR; correcting entries toward native-speaker
sign-off is the dataset's active roadmap._