bm-text-normalization

Bambara (Bamanankan) orthographic normalisation: map a non-standard spelling to its standard form. 4,877 short phrase-level pairs in a single config, bamadaba.

Load

from datasets import load_dataset

train = load_dataset("djelia/bm-text-normalization", "bamadaba", split="train")
dev   = load_dataset("djelia/bm-text-normalization", "bamadaba", split="dev")
test  = load_dataset("djelia/bm-text-normalization", "bamadaba", split="test")

# rows that are already correct — a model must not "fix" these
already_clean = train.filter(lambda row: row["noise_level"] == 0)

The validation split is named dev; split="validation" will fail.

Splits

Split	Rows
train	3,901
dev	488
test	488

Fields

Field	Description
source_text	Non-standard spelling
target_text	Standard-orthography form
noise_level	Character-level edit distance between the two, integer 0-25

Example rows:

source_text	target_text	noise_level
a bolira coyi !	a bolila coyi !	1
a be lajailen	a bɛɛ lajɛlen	4

Notes

419 rows are identity pairs at noise_level 0 — text already correct, to be left alone.

The noise is a small closed inventory: Bambara characters replaced by what a French keyboard produces (ɛ→e, ɔ→o, ɲ→gn), liquid confusion (r/l), plus deleted and inserted spaces and dropped apostrophes (k'a written ka). Word boundaries move in both directions, so a model has to join and split, not only map characters.

Sentences are short — median 19 characters. For running text such as ASR transcripts, see djelia/bm-text-normalization-benchmark.