Datasets:

razhan
/

script_normalization_ckb

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

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.

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

Script normalization CKB — noisy → standard Sorani (script_normalization_ckb)

At a glance

How the two sides differ

Usage

Known limitations

Related