---
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:
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  - split: validation
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  - split: validation
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  - split: validation
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  - split: validation
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  - split: validation
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  - split: validation
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  data_files:
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  - split: validation
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dataset_info:
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  features:
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  - name: code
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  - name: language
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  - name: code
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  - name: code_en
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  - name: code_en
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  - name: code
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  - name: code
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---

# 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