---
language:
- kk
license: apache-2.0
task_categories:
- text-generation
tags:
- grammatical-error-correction
- kazakh
- gec
- text-editing
size_categories:
- 1M<n<10M
pretty_name: Til-GEC-v2 (Kazakh Grammatical Error Correction)
configs:
- config_name: clean
  default: true
  data_files:
  - split: train
    path: data/clean/*.parquet
- config_name: premium
  data_files:
  - split: train
    path: data/premium/*.parquet
- config_name: raw
  data_files:
  - split: train
    path: data/raw/*.parquet
---

# Til-GEC-v2 — Kazakh grammatical error correction

Sentence pairs for **correcting grammar and spelling in Kazakh**: an `input` sentence that
contains an error and a `target` sentence that fixes it. Each pair is labelled with the type of
error and the subject domain it came from.

About **14%** of the pairs (401 661) are **identity pairs** — input and target are the same
sentence, labelled `identity`. They are there on purpose: a corrector that cannot leave a
correct sentence alone is useless, and a model trained without them rewrites everything.

## Quick start

```python
from datasets import load_dataset

ds = load_dataset("TilQazyna/Til-GEC-v2", "clean", split="train")
print(ds[0])
# {'input':  'Өзен сағасы Вига өзенынің сол жағалауынан 66 км қашықтықта орналасқан.',
#  'target': 'Өзен сағасы Вига өзенінің сол жағалауынан 66 км қашықтықта орналасқан.',
#  'error_tags': '["orthography/vowel_harmony"]',
#  'task': 'gec', 'source': 'gec-rulebased', 'split': 'train',
#  'score': 5, 'category': 'geography_travel', 'judge_lang': 'kk'}

# The fix above is a single letter: өзенынің -> өзенінің. Kazakh suffixes must agree
# with the vowels of the stem, and that is the single most common real error class here.
```

## Configurations

Three quality tiers of the same collection. They are **nested**, not disjoint — `premium` is the
strictest selection, `raw` is everything.

| Config | Rows | Use it when |
|---|---:|---|
| `clean` *(default)* | 1 018 673 | General training. Deduplicated, filtered. |
| `premium` | 758 974 | You want the highest-confidence subset only. |
| `raw` | 1 082 457 | You want to apply your own filters from scratch. |

## Data fields

| Field | Type | Description |
|---|---|---|
| `input` | string | Kazakh sentence, possibly containing an error |
| `target` | string | The corrected sentence |
| `error_tags` | string | A **JSON-encoded array** of error labels, e.g. `'["morphosyntax/case"]'`. Parse it with `json.loads`. |
| `task` | string | Always `gec` in this dataset |
| `source` | string | Which sub-collection the pair came from (see below) |
| `split` | string | Always `train` — no official test split ships here, see *Evaluation* |
| `score` | int64 | Quality rating 0–5 assigned during curation |
| `category` | string | Subject domain, e.g. `politics`, `medicine_health` |
| `judge_lang` | string | Language detected in the pair, `kk` for almost all rows |

## What is in the data

**Error types** (top labels, `clean` + `premium` + `raw` combined):

| Label | Pairs |
|---|---:|
| `orthography/spelling` | 470 122 |
| `identity` (no error — leave unchanged) | 401 661 |
| `morphosyntax/case` | 335 021 |
| `orthography/vowel_harmony` | 261 253 |
| `orthography/spacing` | 226 561 |
| `orthography/punctuation` | 173 581 |
| `syntax_discourse/word_order` | 111 674 |
| `morphosyntax/plural` | 90 445 |
| `syntax_discourse/missing_element` | 88 386 |
| `syntax_discourse/redundant_element` | 66 115 |
| `morphosyntax/agreement` | 53 224 |
| `morphosyntax/possessive` | 37 402 |
| `morphosyntax/tense` | 28 731 |
| `morphosyntax/postposition` | 19 001 |

`orthography/vowel_harmony` is specific to Kazakh: suffixes must agree with the vowels of the
stem, and getting that wrong is one of the most common real mistakes in written Kazakh.

**Domains** — news and public-sector text dominate: `politics` (367 109), `economy_finance`
(240 215), `education` (216 839), `news_media` (203 412), `medicine_health` (202 092),
`geography_travel` (188 053), `everyday_life` (148 236), `government` (137 481), `law` (134 892).

**Quality scores** — most pairs sit at the top of the scale: 5 → 1 498 308, 4 → 778 614,
3 → 519 398, and 63 784 pairs at 0–2. Filter on `score >= 4` if you want the stricter half
without switching configs.

**Sub-collections** (`source` field): `gec-rulebased` 2 807 468, `gec-synth` 52 630,
`gec-gpt4o` 6.

## How this dataset was built

This is a filtered version of [`TilQazyna/Til-GEC`](https://huggingface.co/datasets/TilQazyna/Til-GEC).
Two groups of rows were removed from the predecessor:

1. **707 773 translation pairs.** The predecessor was assembled from several sources, and one of
   them (`sozkz-corpus-pretrain-gec-mix-v1`) was a *pretraining mixture*, not a correction set.
   Those rows carried `task = translate_ru_kk` / `translate_kk_ru` / `translate_en_kk` /
   `translate_kk_en` and had no error labels at all. Training on them teaches a model to
   translate rather than to correct. They were not discarded — they are published separately as
   [`TilQazyna/Til-Parallel-KK`](https://huggingface.co/datasets/TilQazyna/Til-Parallel-KK).
2. **1 436 benchmark rows.** The predecessor contained rows from the public evaluation set
   [`stukenov/sozkz-corpus-gec-benchmark-kk-v1`](https://huggingface.co/datasets/stukenov/sozkz-corpus-gec-benchmark-kk-v1).
   An evaluation set inside a training set silently inflates every score measured against it.

Rows were routed by the `task` field, which was intact in the source data, so the split is
exact rather than heuristic. Nothing else was modified: the remaining rows, their fields and
their values are byte-identical to the predecessor.

## Evaluation

There is no test split here on purpose. Evaluate against
[`stukenov/sozkz-corpus-gec-benchmark-kk-v1`](https://huggingface.co/datasets/stukenov/sozkz-corpus-gec-benchmark-kk-v1),
which has `test`, `organic_social` (real mistakes collected from social media) and `synthetic`
splits. Report `organic_social` alongside `test` — the two behave very differently, and a model
that looks strong on synthetic errors can be weak on real ones.

## Limitations

- **The errors are mostly generated, not observed.** `gec-rulebased` accounts for 98% of the
  data: real Kazakh sentences with errors introduced by rule. The distribution of these errors
  does not match how people actually write, and models trained on this data tend to do markedly
  worse on genuine human mistakes than on synthetic ones.
- **Domain skew.** News, politics and public-sector text are heavily over-represented.
  Conversational Kazakh is thin.
- **Single reference.** Each input has exactly one accepted correction, though several may be
  valid. Exact-match scoring will therefore understate real performance.
- **Scores are machine-assigned**, not human-verified.

## Related datasets

| Dataset | What it is |
|---|---|
| [`TilQazyna/Til-Parallel-KK`](https://huggingface.co/datasets/TilQazyna/Til-Parallel-KK) | The translation pairs removed from this collection, kk↔ru and kk↔en |
| [`stukenov/sozkz-corpus-gec-benchmark-kk-v1`](https://huggingface.co/datasets/stukenov/sozkz-corpus-gec-benchmark-kk-v1) | Evaluation benchmark, excluded from this dataset |
| [`TilQazyna/Til-GEC`](https://huggingface.co/datasets/TilQazyna/Til-GEC) | Predecessor, deprecated |

## Citation

```bibtex
@misc{tilgec_v2_2026,
  title  = {Til-GEC-v2: A Kazakh Grammatical Error Correction Corpus},
  author = {TilQazyna},
  year   = {2026},
  url    = {https://huggingface.co/datasets/TilQazyna/Til-GEC-v2}
}
```

---

## По-русски, кратко

Пары предложений для **исправления грамматики и орфографии в казахском языке**: на входе
предложение с ошибкой, на выходе — исправленное, плюс метка типа ошибки и домен. Около 14% пар
(401 661) — это пары без ошибки, где вход и выход совпадают: корректор, который не умеет
оставлять правильный текст в покое, бесполезен и правит всё подряд.

Три конфигурации — тиры качества одной и той же коллекции: `clean` (1 018 673 строки, берите
её по умолчанию), `premium` (758 974, самый строгий отбор), `raw` (1 082 457, всё сырьё).

Это очищенная версия `TilQazyna/Til-GEC`. Из предшественника убрано 707 773 переводные пары,
попавшие туда из претрейн-микса `sozkz-corpus-pretrain-gec-mix-v1` — они вынесены в отдельный
датасет `TilQazyna/Til-Parallel-KK`, — и 1 436 строк публичного бенчмарка, которым эту же задачу
и меряют.

**О чём стоит знать заранее:** 98% ошибок здесь порождены правилами, а не собраны у живых людей.
Их распределение не совпадает с тем, как люди ошибаются на самом деле, поэтому модель, обученная
только на этих данных, обычно заметно слабее на настоящих ошибках, чем на синтетических.
Меряйте на сплите `organic_social` бенчмарка, а не только на `test`.