Datasets:
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language:
- fr
- mos
license: other
task_categories:
- translation
pretty_name: French–Mooré (Mossi) Parallel Corpus
size_categories:
- 1M<n<10M
tags:
- moore
- mossi
- mooré
- french
- parallel-corpus
- low-resource
- glosbe
- burkina-faso
configs:
- config_name: default
data_files:
- split: train
path: fr-mos-validated.parquet
---
# French → Mooré (Mossi) Parallel Corpus
Machine-translated parallel sentences from French (`fr`) to Mooré / Mossi (`mos`), produced by a public-web crawl + filtering + Glosbe translation pipeline.
## Snapshot
| Field | Value |
|-------|------:|
| Validated pairs | ~2.82 million |
| Source language | French |
| Target language | Mooré (Mossi) |
| Translator | Glosbe public MT |
| Export date | 2026-08-03 |
## Schema
| Column | Type | Description |
|--------|------|-------------|
| `id` | string (UUID) | Pair identifier |
| `source_domain` | string | Crawl source domain |
| `source_url` | string | Original page URL |
| `crawl_date` | string (ISO 8601) | When the page was crawled |
| `french` | string | Source sentence |
| `moore` | string | Mooré translation |
| `translator` | string | Provider id (e.g. `glosbe`) |
| `translation_confidence` | float | Provider confidence |
| `complexity_score` | float | Source complexity score |
| `quality_score` | float | Pipeline quality score |
## Files
- `fr-mos-validated.jsonl` — one JSON object per line
- `fr-mos-validated.parquet` — same rows, ZSTD-compressed Parquet
## Quality notes
- Only **accepted / validated** pairs are included (failed validation rows are excluded).
- Translations are **automatic (Glosbe)**; this is **not** a fully human-reviewed gold set.
- Source text was filtered for short/medium simple French sentences; exact duplicates were deduplicated.
- Soft confidence floor was applied during generation; average confidence on accepted pairs is roughly ~0.63.
- Dominant crawl sources include French Wikipedia and other public French sites; review licenses/terms for your use case.
## Intended use
- Pretraining / fine-tuning low-resource MT models for French ↔ Mooré
- Research on West African language technology
- Bootstrapping human post-editing workflows
## Limitations
- Automatic MT noise and occasional domain skew (encyclopedia, news, institutional)
- Not a substitute for native-speaker verified data for high-stakes applications
- Source provenance is retained in `source_url` / `source_domain` for auditability
## Citation
If you use this dataset, please cite the dataset page and note that translations were generated with Glosbe MT via an automated corpus pipeline.
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