| --- |
| language: |
| - kk |
| - ru |
| - en |
| license: apache-2.0 |
| task_categories: |
| - translation |
| tags: |
| - parallel-corpus |
| - kazakh |
| - russian |
| - english |
| - machine-translation |
| size_categories: |
| - 1M<n<10M |
| pretty_name: Til-Parallel-KK (Kazakh–Russian–English parallel corpus) |
| 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-Parallel-KK — Kazakh ↔ Russian ↔ English parallel corpus |
|
|
| Sentence pairs for **machine translation involving Kazakh**, in four directions: Kazakh to and |
| from Russian, and Kazakh to and from English. Each pair carries its translation direction, a |
| subject domain and a quality score. |
|
|
| Sentences are short — median 105 characters, roughly one sentence per pair. |
|
|
| ## Quick start |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("TilQazyna/Til-Parallel-KK", "clean", split="train") |
| |
| # one direction only |
| ru_kk = ds.filter(lambda r: r["task"] == "translate_ru_kk") |
| print(ru_kk[0]) |
| # {'input': 'Использование текущих и постоянных цен помогает нам прояснить это различие.', |
| # 'target': 'Ағымдағы және тұрақты бағаларды пайдалану бұл айырмашылықты нақтылауға көмектеседі.', |
| # 'task': 'translate_ru_kk', 'source': 'gec-mix', 'split': 'train', |
| # 'score': 4, 'category': 'economy_finance', 'judge_lang': 'kk'} |
| ``` |
|
|
| ## Configurations |
|
|
| Three quality tiers of the same collection, nested rather than disjoint. |
|
|
| | Config | Rows | Use it when | |
| |---|---:|---| |
| | `clean` *(default)* | 664 555 | General training | |
| | `premium` | 388 552 | Highest-confidence subset only | |
| | `raw` | 707 773 | You want to filter it yourself | |
|
|
| ## Translation directions |
|
|
| | Direction | `raw` | `clean` | `premium` | |
| |---|---:|---:|---:| |
| | `translate_en_kk` | 180 148 | 170 840 | 115 233 | |
| | `translate_kk_en` | 179 983 | 157 186 | 64 657 | |
| | `translate_ru_kk` | 174 233 | 169 281 | 115 590 | |
| | `translate_kk_ru` | 173 409 | 167 248 | 93 072 | |
|
|
| The four directions are close to balanced in `raw`. In `premium` the Kazakh→English side thins |
| out considerably, so if you are training that direction specifically, prefer `clean`. |
|
|
| ## Data fields |
|
|
| | Field | Type | Description | |
| |---|---|---| |
| | `input` | string | Source sentence | |
| | `target` | string | Translated sentence | |
| | `task` | string | Direction: `translate_ru_kk`, `translate_kk_ru`, `translate_en_kk`, `translate_kk_en` | |
| | `score` | int64 | Quality rating 0–5 assigned during curation | |
| | `category` | string | Subject domain, e.g. `everyday_life`, `literature` | |
| | `judge_lang` | string | Language detected in the target side | |
| | `source` | string | Always `gec-mix`, an artefact of the collection's history (see below) | |
| | `split` | string | Always `train` | |
| | `error_tags` | string | **Always `null`.** Left over from the schema this data used to live in; ignore it. | |
|
|
| The `source` and `error_tags` fields are kept only so that rows can still be traced back to the |
| predecessor dataset. Neither carries information here. |
|
|
| ## Domains and quality |
|
|
| **Domains** lean towards everyday and cultural text rather than officialese: |
| `everyday_life` (202 345), `literature` (173 862), `medicine_health` (167 447), |
| `history` (139 237), `technology_it` (117 234), `economy_finance` (95 420), |
| `entertainment` (87 866), `science_academic` (81 679). |
|
|
| **Scores**: 5 → 654 570, 4 → 511 086, 3 → 552 006, and 43 218 pairs at 0–2. Filtering on |
| `score >= 4` keeps roughly two thirds of the corpus. |
|
|
| ## How this dataset came to exist |
|
|
| These pairs were published inside |
| [`TilQazyna/Til-GEC`](https://huggingface.co/datasets/TilQazyna/Til-GEC), a grammatical error |
| correction dataset, where they made up about 39% of the rows. They had ended up there because |
| the collection that supplied them (`stukenov/sozkz-corpus-pretrain-gec-mix-v1`) was a |
| pretraining mixture rather than a correction set, and was merged in on the strength of its name. |
|
|
| The rows were always labelled correctly — every one of them carried a `translate_*` value in |
| `task` — so extracting them was an exact filter on that field, not a guess. They are published |
| here as their own dataset because a parallel corpus of this size for Kazakh is worth having on |
| its own terms, and because leaving it inside a GEC dataset actively harmed models trained there. |
|
|
| The correction half is published as |
| [`TilQazyna/Til-GEC-v2`](https://huggingface.co/datasets/TilQazyna/Til-GEC-v2). |
|
|
| ## Limitations |
|
|
| - **No held-out split.** Everything is `train`. Carve out your own evaluation set, and check for |
| overlap if you also evaluate on FLORES, NTREX or similar — provenance of the underlying text |
| is not fully documented. |
| - **Scores are machine-assigned**, not human-verified, and quality within a tier varies. |
| - **Direction is a label, not a guarantee.** Spot-checking is advisable before training a |
| production system on any single direction. |
| - **English↔Kazakh pairs may be pivoted** through a third language rather than translated |
| directly. This is not recorded per row. |
| - **Single reference** per source sentence. |
|
|
| ## Related datasets |
|
|
| | Dataset | What it is | |
| |---|---| |
| | [`TilQazyna/Til-GEC-v2`](https://huggingface.co/datasets/TilQazyna/Til-GEC-v2) | The grammatical-correction half of the same original collection | |
| | [`TilQazyna/Til-GEC`](https://huggingface.co/datasets/TilQazyna/Til-GEC) | The collection both were split out of, deprecated | |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{tilparallelkk_2026, |
| title = {Til-Parallel-KK: A Kazakh--Russian--English Parallel Corpus}, |
| author = {TilQazyna}, |
| year = {2026}, |
| url = {https://huggingface.co/datasets/TilQazyna/Til-Parallel-KK} |
| } |
| ``` |
|
|
| --- |
|
|
| ## По-русски, кратко |
|
|
| Параллельный корпус для **машинного перевода с казахским** в четырёх направлениях: kk↔ru и |
| kk↔en. У каждой пары есть направление, домен и оценка качества. Предложения короткие — медиана |
| 105 символов. |
|
|
| Три конфигурации: `clean` (664 555 пар, берите по умолчанию), `premium` (388 552, строгий отбор), |
| `raw` (707 773, всё). В `raw` направления почти сбалансированы, а в `premium` сильно проседает |
| kk→en — для этого направления лучше брать `clean`. |
|
|
| Корпус выделен из `TilQazyna/Til-GEC`, где он лежал под видом данных для исправления |
| грамматических ошибок и занимал там около 39% строк. Метка направления в поле `task` была |
| проставлена у всех строк, поэтому выделение — точный фильтр, а не догадка. |
|
|
| **О чём стоит знать заранее:** отложенной выборки нет, всё в `train` — свою придётся выделять |
| самим. Оценки проставлены машиной, а не людьми. Пары en↔kk могли получиться через третий язык, |
| и в данных это не отмечено. |
|
|
