| --- |
| language: |
| - en |
| - zh |
| - es |
| - ur |
| license: apache-2.0 |
| task_categories: |
| - text-generation |
| tags: |
| - code |
| - multilingual |
| - legesher |
| - transpilation |
| - tiny-aya-expedition |
| - language-decoded |
| pretty_name: Language Decoded Data |
| size_categories: |
| - 10K<n<100K |
| configs: |
| - config_name: condition-1-en |
| data_files: |
| - split: train |
| path: data/condition-1-en/train-*.parquet |
| - split: validation |
| path: data/condition-1-en/validation-*.parquet |
| - config_name: condition-2-ur |
| data_files: |
| - split: train |
| path: data/condition-2-ur/train-*.parquet |
| - split: validation |
| path: data/condition-2-ur/validation-*.parquet |
| - config_name: condition-2-zh |
| data_files: |
| - split: train |
| path: data/condition-2-zh/train-*.parquet |
| - split: validation |
| path: data/condition-2-zh/validation-*.parquet |
| - config_name: condition-2-es |
| data_files: |
| - split: train |
| path: data/condition-2-es/train-*.parquet |
| - split: validation |
| path: data/condition-2-es/validation-*.parquet |
| dataset_info: |
| features: |
| - name: code |
| dtype: string |
| - name: code_en |
| dtype: string |
| - name: language |
| dtype: string |
| - name: file_path |
| dtype: string |
| - name: license |
| dtype: string |
| - name: token_count |
| dtype: int64 |
| --- |
| |
| # Language Decoded | Multilingual Code Dataset |
|
|
| Multilingual Python code datasets for the **Language Decoded** project (part of [Cohere's Tiny Aya Expedition](https://aya.for.ai)), investigating whether code's reasoning benefit for language models is **language-dependent** or **structure-dependent**. |
|
|
| ## Research Question |
|
|
| > Does fine-tuning on non-English code (Python with translated keywords) improve multilingual reasoning as much as English code does? |
|
|
| Prior work ([Aryabumi et al., 2024 -- "To Code or Not to Code"](https://arxiv.org/abs/2408.10914)) demonstrated that including English code in pre-training data improves downstream reasoning performance by approximately 8%. However, that study only tested English code. This dataset enables the natural follow-up: does the reasoning benefit come from the _structure_ of code, or from the _language_ of its keywords? |
|
|
| ## Dataset Description |
|
|
| This dataset provides filtered, quality-controlled Python source code in four configurations: the original English and three keyword-swapped variants (Chinese, Spanish, Urdu). The source data is drawn from [bigcode/the-stack-dedup](https://huggingface.co/datasets/bigcode/the-stack-dedup) (Python subset), filtered for quality using the following criteria: |
|
|
| - AST-valid Python only (must parse without errors) |
| - Permissive licenses only (MIT, Apache-2.0, BSD, etc.) |
| - 10--1000 lines of code |
| - Minimum 21 GitHub stars |
| - No autogenerated files |
| - SHA-256 deduplication |
|
|
| Keyword-swapped variants are produced using [Legesher](https://github.com/legesher/legesher) v0.7.3, which translates Python reserved words (37 keywords, 72 builtins, 66 exceptions) into the target language while preserving code structure and semantics. |
|
|
| ## Available Configs |
|
|
| | Config | Condition | Language | Description | |
| | ---------------- | --------------------- | -------- | ------------------------------------------------------------------------------------------------ | |
| | `condition-1-en` | Condition 1 (control) | English | Unmodified filtered Python from The Stack Dedup | |
| | `condition-2-ur` | Condition 2 | Urdu | Keyword-swapped Python -- 37 keywords, 72 builtins, 66 exceptions translated via Legesher v0.7.3 | |
| | `condition-2-zh` | Condition 2 | Chinese | Keyword-swapped Python -- same transpilation method | |
| | `condition-2-es` | Condition 2 | Spanish | Keyword-swapped Python -- same transpilation method | |
|
|
| ## Schema |
|
|
| | Column | Type | Description | |
| | ------------- | ------ | ---------------------------------------------------------------------------------------------------------------------------------------------------- | |
| | `code` | string | Python source code. For condition-2 configs, this is the transpiled (keyword-swapped) version. For condition-1, this is the original English source. | |
| | `code_en` | string | Original English Python source code. Identical to `code` for condition-1-en. | |
| | `language` | string | ISO 639-1 language code: `en`, `ur`, `zh`, or `es`. | |
| | `file_path` | string | Original file path in The Stack Dedup. | |
| | `license` | string | SPDX license identifier for the source file. | |
| | `token_count` | int64 | Token count computed using the CohereLabs/tiny-aya-base tokenizer. | |
|
|
| ## Experimental Conditions |
|
|
| The Language Decoded experiment uses a ladder of six conditions to isolate the mechanism behind code's reasoning benefit. This dataset currently provides data for conditions 1 and 2: |
|
|
| | Condition | Name | Purpose | |
| | --------------- | -------------------- | -------------------------------------------------------------------------- | |
| | Baseline | No fine-tuning | Establishes the performance floor | |
| | Condition 1 | English code | Tests whether code fine-tuning helps at all (replicates Aryabumi et al.) | |
| | Condition 2 | Keyword-swapped code | Tests whether the _language_ of keywords matters for the reasoning benefit | |
| | Conditions 3--6 | (planned) | Additional controls not yet included in this dataset | |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # Load English code (control) |
| ds = load_dataset("legesher/language-decoded-data", "condition-1-en") |
| |
| # Load a keyword-swapped variant |
| ds = load_dataset("legesher/language-decoded-data", "condition-2-ur") |
| ds = load_dataset("legesher/language-decoded-data", "condition-2-zh") |
| ds = load_dataset("legesher/language-decoded-data", "condition-2-es") |
| |
| # Access splits |
| train = ds["train"] |
| val = ds["validation"] |
| ``` |
|
|
| ## Technical Details |
|
|
| | Parameter | Value | |
| | ---------------------- | ------------------------------------------------------------------------------------------------------------------ | |
| | Source dataset | [bigcode/the-stack-dedup](https://huggingface.co/datasets/bigcode/the-stack-dedup) (Python subset) | |
| | Transpilation tool | [Legesher](https://github.com/legesher/legesher) v0.7.3 (legesher-core, legesher-i18n) | |
| | Tokenizer | CohereLabs/tiny-aya-base | |
| | Base model | [CohereLabs/tiny-aya-base](https://huggingface.co/CohereLabs/tiny-aya-base) (3.35B params) | |
| | Train/validation split | 90% / 10% (seed 42) | |
| | File format | Parquet (snappy compression) | |
| | Filtering criteria | AST-valid, permissive licenses, 10--1000 lines, min 21 GitHub stars, no autogenerated files, SHA-256 deduplication | |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{language-decoded-2026, |
| title={Language Decoded: Investigating Language-Dependent vs. Structure-Dependent Reasoning Benefits of Code}, |
| author={Madison Edgar and Saad Bazaz and Rafay Mustafa and Sarah Jawaid and Rashik Shahjahan and Khojasteh Mirza and Sohaib Bazaz}, |
| year={2026}, |
| publisher={Hugging Face}, |
| url={https://huggingface.co/datasets/legesher/language-decoded-data} |
| } |
| ``` |
|
|
| ## Links |
|
|
| - [Legesher on GitHub](https://github.com/legesher/legesher) |
| - [Tiny Aya Expedition](https://aya.for.ai) |
| - [bigcode/the-stack-dedup](https://huggingface.co/datasets/bigcode/the-stack-dedup) |
|
|
| ## License |
|
|
| Apache 2.0 |
|
|
