---
dataset_info:
  features:
  - name: text
    dtype: string
  - name: summary
    dtype: string
  splits:
  - name: train
    num_bytes: 1398134525
    num_examples: 5330689
  - name: test
    num_bytes: 174587594
    num_examples: 666336
  download_size: 904533210
  dataset_size: 1572722119
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
---

# Script normalization CKB — noisy → standard Sorani (script_normalization_ckb)

Nearly 6M sentence pairs: the `text` column holds Central Kurdish written with
non-standard or distorted characters, `summary` holds the same sentence in standard
Sorani orthography. Use it for text normalisation, spell correction, or to learn the
character-level mapping rules.

## At a glance

| | |
|---|---|
| Rows | 5,997,025 — `train` 5,330,689 / `test` 666,336 |
| Columns | `text` (noisy input), `summary` (normalised target) |
| Parquet on disk | 904.5 MB (1.57 GB uncompressed) |
| Row groups | 667 in the test shard, ~1,000 rows each |
| Language | Central Kurdish / Sorani (ckb) |

> **Naming note:** despite the column name, `summary` is **not** a summary — it is
> the normalised rewrite of `text`. Both sides carry the same content.

## How the two sides differ

Measured on 1,000 rows of the `test` split:

| | |
|---|---|
| Rows where `text` == `summary` | 20 (2%) |
| Mean character-level edit distance | 21.5 |
| Max edit distance | 85 |
| Mean length `text` / `summary` | 69.3 / 67.3 chars |

Which characters get fixed (rows affected out of 1,000):

| Change | Rows |
|---|---|
| `ي` (U+064A Arabic yeh) → `ی` (U+06CC Farsi yeh) | 682 |
| `ة` (U+0629 teh marbuta) → `ە` (U+06D5) | 384 |
| `ه` (U+0647) → `ە` (U+06D5) | 397 |
| `ث` (U+062B) → `س` (U+0633) | 114 |
| `ذ` (U+0630) → `ز` (U+0632) | 68 |

Example pair:

```
text    : يابان بؤ خؤي بش ئه و قه يرانه  طه نگوجه له مه ي ئابووري هه ره فراواني ...
summary : یابان بۆ خۆی پێش ئەو قەیڕانە تەنگوچەلەمەی ئابووری هەرەفراوانی ...
```

Some rows are far noisier than character substitution:

```
text    : اهي اهگهر پئشمهرگه نهبايه حكمهطي بهعث عثهي پاقلاه ...
summary : ئەی ئەگەر پێشمەرگە نەبووایە حکومەتی بەعس عوسەی پاقلاوەو ...
```

## Usage

```python
from datasets import load_dataset

ds = load_dataset("razhan/script_normalization_ckb", split="test")
print(ds[0]["text"])      # noisy
print(ds[0]["summary"])   # standard

# fine-tune any encoder-decoder for normalisation, or mine substitution rules:
pairs = ds.select(range(200_000))
```

## Known limitations

- The corpus is synthetic in construction: noise was introduced into clean text, so
  the error distribution reflects the generator, not real user typos.
- `summary` is the target and the column name is misleading (left in place for
  backwards compatibility).
- Only character/orthography-level repair is targeted; it does not correct grammar
  or word choice, and heavily distorted rows can still normalise to a different
  token sequence.
- Rows are single sentences — no document context.

## Related

- `razhan/riste` — line-by-line Sorani sentences (a likely source pool).
- `razhan/kteb`, `razhan/kteb-dataset` — book text in mixed orthography.