README.md

---
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`](https://huggingface.co/datasets/djelia/text-normalization-benchmark).

## Load

```python
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.