| --- |
| pretty_name: RuEn Code Curriculum |
| language: |
| - ru |
| - en |
| - code |
| license: other |
| license_name: multi-license |
| license_link: LICENSE.md |
| task_categories: |
| - text-generation |
| - question-answering |
| - summarization |
| - text-classification |
| size_categories: |
| - 100K<n<1M |
| tags: |
| - code |
| - russian |
| - english |
| - instruction-tuning |
| - continued-pretraining |
| - parquet |
| - datasets |
| configs: |
| - config_name: sft |
| default: true |
| data_files: |
| - split: train |
| path: "data/sft/train-*.parquet" |
| - split: reserve |
| path: "data/sft/reserve-*.parquet" |
| - split: validation |
| path: "data/sft/validation-*.parquet" |
| - config_name: cpt |
| data_files: |
| - split: train |
| path: "data/cpt/train-*.parquet" |
| - split: reserve |
| path: "data/cpt/reserve-*.parquet" |
| - split: validation |
| path: "data/cpt/validation-*.parquet" |
| --- |
| |
| # RuEn Code Curriculum |
|
|
| RuEn Code Curriculum is a curated Russian-English dataset for continued pretraining (CPT) and supervised fine-tuning (SFT) of small code-oriented language models. |
|
|
| This public release contains only records classified as redistributable. Local-training-only web and code sources used by the internal curriculum are intentionally excluded. |
|
|
| ## Dataset summary |
|
|
| | Configuration | Split | Records | Tokens | |
| |---|---:|---:|---:| |
| | `sft` | train | 53,278 | 13,997,239 | |
| | `sft` | reserve | 19,225 | 4,999,575 | |
| | `sft` | validation | 672 | 180,000 | |
| | `cpt` | train | 42,927 | 14,709,683 | |
| | `cpt` | reserve | 13,402 | 4,610,199 | |
| | `cpt` | validation | 225 | 129,597 | |
|
|
| Total: **129,729 records and 38,626,293 tokenizer tokens**. Token counts use the unmodified tokenizer from `Qwen/Qwen2.5-Coder-1.5B` at revision `df3ce67c0e24480f20468b6ef2894622d69eb73b`. |
|
|
| `train` is the default training split. `reserve` is a held-back replacement pool and should not normally be concatenated with `train`. `validation` is intended for pipeline validation and is not a standalone capability benchmark. |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| sft = load_dataset("sup2ch/ru-en-code-curriculum", "sft") |
| cpt = load_dataset("sup2ch/ru-en-code-curriculum", "cpt") |
| |
| print(sft["train"][0]["messages"]) |
| print(cpt["train"][0]["text"][:500]) |
| ``` |
|
|
| ## Configurations |
|
|
| ### `sft` |
|
|
| Chat-formatted instruction data with a `messages` column containing alternating `user` and `assistant` messages. Major task families include: |
|
|
| - grounded Russian Wikipedia question answering; |
| - repository code changes and patch-style instructions; |
| - OpenAssistant dialogue; |
| - general QA, classification, extraction, summarization and brainstorming; |
| - constrained responses and everyday conversation; |
| - a small project-authored identity and capability-honesty subset. |
|
|
| All prompt tokens are masked in the prepared training view; assistant responses are the supervised portion. The canonical release stores messages and token-accounting metadata rather than model-specific tokenized sequences. |
|
|
| ### `cpt` |
|
|
| Canonical documents for continued pretraining. The public configuration contains: |
|
|
| - allowlisted single-repository code/commit material from CommitPackFT with record-level repository, path and license provenance; |
| - Russian Wikipedia text with source attribution metadata. |
|
|
| ## Data fields |
|
|
| Both configurations include stable `record_id`, source revision and row identifiers, source URL, license, language, split, transformations, provenance hashes, and a `redistributable` flag. |
|
|
| SFT additionally includes `messages`, task family, operation, prompt/supervised token accounting and verification metadata. CPT additionally includes raw text, category and document token count. |
|
|
| The `source_split` column preserves the original canonical split name. The public-facing `split` column uses `train`, `reserve` or `validation`. |
|
|
| ## Curation and quality controls |
|
|
| The parent canonical dataset used: |
|
|
| - exact, normalized and near-duplicate checks; |
| - record-ID and split-isolation checks; |
| - PII redaction and secret scanning; |
| - benchmark-contamination checks focused on HumanEval, MBPP, EvalPlus, APPS and internal coding probes; |
| - source caps, repository caps and prompt-template caps; |
| - deterministic token accounting and SHA-256 manifests; |
| - a blind stratified manual review with local fixes and revalidation. |
|
|
| For this public export, twelve otherwise redistributable records containing Unicode replacement characters or unexpected control characters were excluded. Their IDs and exclusion reasons are listed in `metadata/excluded_records.json`; no excluded text is published. |
|
|
| Audit summaries, source cards, reviewed public examples and file hashes are included under `metadata/`, `samples/` and `SHA256SUMS.json`. |
|
|
| ## Known limitations |
|
|
| - This is a compact adaptation curriculum, not enough data to pretrain a language model from scratch. |
| - Context preparation targeted 512 tokens. Some long SFT prompts were left-truncated while preserving assistant responses. |
| - Code responses were not executed. The dataset teaches code-editing and response patterns but does not guarantee program correctness. |
| - SFT `quality_scores` are policy defaults and are not calibrated ranking scores. |
| - Grounded QA is derived partly from automatically generated WikiOmnia questions and an older Wikipedia snapshot. |
| - The contamination registry is strongest for coding benchmarks; users evaluating on general reasoning, math or knowledge benchmarks should perform additional leakage checks. |
| - The included validation splits are small and should not be treated as comprehensive model evaluations. |
| - A full toxicity, bias and safety taxonomy audit was not performed. |
|
|
| ## Licensing |
|
|
| This is a **multi-license, source-preserving release**. There is no single permissive license covering every record. The applicable license is stored in each row and summarized in [LICENSE.md](LICENSE.md). Required source acknowledgements are listed in [ATTRIBUTION.md](ATTRIBUTION.md). |
|
|
| Users are responsible for complying with the applicable record-level and upstream licenses, including attribution and share-alike requirements. This dataset card and the accompanying engineering audit are not legal advice. |
|
|
| ## Origin |
|
|
| The curriculum was originally assembled for experiments with the WexCode-RU model family. The dataset is published under a neutral name so it can be used independently of that model. |
|
|
