Abzalbek89/corpus_clean

Kazakh Cleaned Corpus

Cleaned and deduplicated Kazakh-language text corpus built from multiple open sources. Designed for pretraining and fine-tuning Kazakh language models.

Dataset Summary

	Count
Train	1,502,583
Validation	15,177
Total	1,517,760

The dataset was assembled from the kz-transformers/multidomain-kazakh-dataset collection and processed through a multi-stage cleaning pipeline.

Sources

Source	Raw texts	Clean texts	Description
md_oscar	269,047	239,807	OSCAR web crawl
md_kazakhNews	2,107,986	129,349	Kazakh news articles
md_kazakhBooks	8,423	20,482*	Kazakh books
md_leipzig	1,706,485	1,128,122	Leipzig corpora collection
Total	4,091,941	1,517,760

* Books are split into chunks of ≤50K characters, so the clean count exceeds raw count.

Cleaning Pipeline

Each text passes through 9 sequential filters (ordered fast → slow):

1. OSCAR dict fix — unwraps texts stored as {'text': '...'} Python dict literals
2. NFC normalization — Unicode NFC, control character removal, whitespace normalization
3. Minimum length — ≥50 characters, ≥10 words
4. Kazakh character check — must contain at least one Kazakh-specific character (Ә, Ғ, Қ, Ң, Ө, Ұ, Ү, Һ, І, etc.)
5. Script profile — Cyrillic ≥60%, Latin ≤25%
6. Junk filter — URL density ≤5/1K chars, ≤5 HTML tags, special char ratio ≤40%, no boilerplate patterns
7. Gzip repetition — compression ratio ≥0.20 (filters repetitive/degenerate text)
8. FastText LID — language identification: kk confidence ≥0.50, gap to nearest rival ≥0.10
9. Exact dedup — MD5-based deduplication across all sources (1,033 duplicates removed)

Rejection Statistics

Reason	Count
no_kaz_chars	1,888,456
too_few_words	323,914
too_short	255,993
lid_rejected	74,315
junk	45,297
script_profile	19,871
gzip_repetition	10,706
dedup	1,033

Fields

• text (string) — cleaned text content
• source (string) — origin dataset identifier (e.g., md_oscar, md_leipzig)

Usage

from datasets import load_dataset

ds = load_dataset("Abzalbek89/corpus_clean")

# Access splits
train = ds["train"]
val = ds["validation"]

print(train[0]["text"][:200])
print(train[0]["source"])

License

Apache 2.0