Datasets:
File size: 14,812 Bytes
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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-*
- split: validation
path: data/condition-1-en/validation-*
- config_name: condition-1-en-5k
data_files:
- split: train
path: data/condition-1-en-5k/train-*
- split: validation
path: data/condition-1-en-5k/validation-*
- 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
- config_name: condition-2-es-5k
data_files:
- split: train
path: data/condition-2-es-5k/train-*
- split: validation
path: data/condition-2-es-5k/validation-*
- 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-ur-5k
data_files:
- split: train
path: data/condition-2-ur-5k/train-*
- split: validation
path: data/condition-2-ur-5k/validation-*
- config_name: condition-2-zh
data_files:
- split: train
path: data/condition-2-zh/train-*
- split: validation
path: data/condition-2-zh/validation-*
- config_name: condition-2-zh-5k
data_files:
- split: train
path: data/condition-2-zh-5k/train-*
- split: validation
path: data/condition-2-zh-5k/validation-*
- config_name: condition-3-zh-5k
data_files:
- split: train
path: data/condition-3-zh-5k/train-*
- split: validation
path: data/condition-3-zh-5k/validation-*
dataset_info:
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features:
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dtype: string
- name: code
dtype: string
- name: code_en
dtype: string
- name: language
dtype: string
- name: license
dtype: string
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dtype: int32
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- name: code_en
dtype: string
- name: language
dtype: string
- name: file_path
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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 |
| `condition-3-zh-5k` | Condition 3 | Chinese | Blended: 3,486 native Chinese code + 1,514 transpiled Python (see Condition 3 section below) |
## Schema
### Conditions 1--2
| 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. |
### Condition 3
Condition 3 blends native Chinese code with transpiled code and adds a `source_type` column to distinguish them. `code_en` is populated for transpiled rows (keeping them in sync with conditions 1--2) but null for native code rows, which have no English equivalent.
| Column | Type | Description |
| ------------- | ----------- | ---------------------------------------------------------------------------------- |
| `file_path` | string | File identifier (native filename or transpiled file path) |
| `code` | string | The code content (native or transpiled) |
| `code_en` | string/null | English original -- populated for transpiled rows, null for native code rows |
| `language` | string | ISO 639-1 language code (`zh`) |
| `license` | string | Source license (SPDX identifier, `UNKNOWN`, or `varies`) |
| `token_count` | int64 | Token count computed using the CohereLabs/tiny-aya-base tokenizer |
| `source_type` | string | `"native"` (natively Chinese-authored) or `"transpiled"` (keyword-swapped English) |
## 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 |
| Condition 3 | Mixed native sources | Tests whether diverse native-language code adds value beyond keyword swapping |
| Conditions 4--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")
# Load blended native + transpiled (condition 3)
ds = load_dataset("legesher/language-decoded-data", "condition-3-zh-5k")
# Access splits
train = ds["train"]
val = ds["validation"]
# Filter condition-3 by source type
native_only = train.filter(lambda x: x["source_type"] == "native")
```
## 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
|