language:
- bm
task_categories:
- text-generation
pretty_name: Bambara Text Normalization Benchmark
size_categories:
- n<1K
tags:
- bambara
- text-normalization
- benchmark
bm-text-normalization-benchmark
A small human-annotated evaluation set for Bambara (Bamanankan) orthographic normalisation:
96 real-world Bambara strings, each paired with a hand-written standard-orthography rewrite.
It is the cleaned export of the finished annotations from
djelia/text-normalization-benchmark.
Load
from datasets import load_dataset
# the current, whitespace-clean evaluation set
bench = load_dataset("djelia/bm-text-normalization-benchmark", "level-2", split="test")
# score one source pool at a time
asr_only = bench.filter(lambda row: row["source_dataset"] == "bambara-asr-v2")
Configs
| Config | Split | Rows |
|---|---|---|
level-2 |
test |
96 |
default |
train |
96 |
level-1 |
test |
81 |
Prefer level-2. default holds the same 96 rows but 33 of its targets carry a trailing
newline; level-1 is an earlier 81-row variant.
Fields
| Field | Description |
|---|---|
source_dataset |
Source pool: transcription.txt (39), Denube-final (24), bambara-asr-v2 (19), kunkado (14) |
source_text |
Raw Bambara string, 3-243 characters (median 43) |
target_text |
Human-written standard-orthography rewrite |
Example: iyere lafia sa thie -> I yɛrɛ lafiya sa, cɛ́.
Notes
The task is broader than diacritic restoration: it covers punctuation, capitalisation, word re-segmentation, French code-switched material, and lexical correction where the input was garbled.
20 of the 96 rows are identity pairs, so a model that rewrites everything is penalised on a fifth of the set.
At 96 items one row is roughly one point of exact-match accuracy. Report a character-level metric (CER or normalised edit distance) alongside exact match.