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Datasets:
legesher
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language-decoded-data

Tasks:
Text Generation
Modalities:
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Text
Formats:
parquet
Languages:
English
Chinese
Spanish
Size:
100K - 1M
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Tags:
code
multilingual
legesher
transpilation
tiny-aya-expedition
language-decoded
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metadata 
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:
  - 100K<n<1M
configs:
  - config_name: condition-1-en-32k
    data_files:
      - split: train
        path: data/condition-1-en-32k/train-*
      - split: validation
        path: data/condition-1-en-32k/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-32k
    data_files:
      - split: train
        path: data/condition-2-es-32k/train-*
      - split: validation
        path: data/condition-2-es-32k/validation-*
  - 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-32k
    data_files:
      - split: train
        path: data/condition-2-ur-32k/train-*
      - split: validation
        path: data/condition-2-ur-32k/validation-*
  - 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-32k
    data_files:
      - split: train
        path: data/condition-2-zh-32k/train-*
      - split: validation
        path: data/condition-2-zh-32k/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-*
  - config_name: condition-4-zh-5k
    data_files:
      - split: train
        path: data/condition-4-zh-5k/train-*
      - split: validation
        path: data/condition-4-zh-5k/validation-*
dataset_info:
  - config_name: condition-1-en-32k
    features:
      - name: file_path
        dtype: string
      - name: code
        dtype: string
      - name: code_en
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      - name: language
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      - name: license
        dtype: string
      - name: token_count
        dtype: int32
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        num_examples: 31818
      - name: validation
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    dataset_size: 446345172
  - config_name: condition-1-en-5k
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      - name: code
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      - name: code_en
        dtype: string
      - name: language
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      - name: license
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      - name: token_count
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  - config_name: condition-2-es-32k
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      - name: code
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      - name: code_en
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      - name: language
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      - name: validation
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      - name: code
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      - name: code_en
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      - name: language
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      - name: license
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      - name: token_count
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      - name: validation
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  - config_name: condition-2-ur-32k
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      - name: validation
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      - name: file_path
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      - name: code
        dtype: string
      - name: code_en
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      - name: language
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      - name: license
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  - config_name: condition-2-zh-32k
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      - name: file_path
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      - name: code
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      - name: code_en
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      - name: language
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      - name: license
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        num_examples: 31818
      - name: validation
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    dataset_size: 450581642
  - config_name: condition-2-zh-5k
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      - name: code
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      - name: code_en
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      - name: language
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      - name: license
        dtype: string
      - name: token_count
        dtype: int32
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      - name: validation
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      - name: code_en
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      - name: language
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      - name: license
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      - name: content
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      - name: extension
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      - name: source
        dtype: string
      - name: quality_tier
        dtype: string
      - name: sha256
        dtype: string
      - name: byte_size
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      - name: total_lines
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      - name: cjk_ratio
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      - name: has_cjk
        dtype: bool
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      - name: train
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        num_examples: 6553
      - name: validation
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        num_examples: 729
    download_size: 18300000
    dataset_size: 51768984

Language Decoded | Multilingual Code Dataset

Multilingual Python code datasets for the Language Decoded project (part of Cohere's Tiny Aya Expedition), 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") 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 multiple configurations: the original English, three keyword-swapped variants (Chinese, Spanish, Urdu), a blended native+transpiled mix, and strictly native Chinese code. The source data is drawn from 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 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

Each condition is available in two sizes: -32k (full filtered corpus, ~31.8k train + ~3.5k validation) and -5k (stratified subset, 4.5k train + 500 validation). The -5k subsets are used for QLoRA fine-tuning on consumer GPUs.

Config Condition Language Description Train Val
condition-1-en-32k 1 (control) English Unmodified filtered Python from The Stack Dedup 31,818 3,536
condition-1-en-5k 1 (control) English Stratified 5k subset of condition-1 4,500 500
condition-2-zh-32k 2 Chinese Keyword-swapped Python via Legesher v0.7.3 31,818 3,536
condition-2-zh-5k 2 Chinese Stratified 5k subset of condition-2-zh 4,500 500
condition-2-es-32k 2 Spanish Keyword-swapped Python via Legesher v0.7.3 31,818 3,536
condition-2-es-5k 2 Spanish Stratified 5k subset of condition-2-es 4,500 500
condition-2-ur-32k 2 Urdu Keyword-swapped Python via Legesher v0.7.3 31,818 3,536
condition-2-ur-5k 2 Urdu Stratified 5k subset of condition-2-ur 4,500 500
condition-3-zh-5k 3 Chinese Blended: 3,486 native Chinese code + 1,514 transpiled Python 4,500 500
condition-4-zh-5k 4 Chinese Strictly native Chinese code (no transpiled code) 6,553 729

Schema

Conditions 1--2

Used by: condition-1-en-*, condition-2-zh-*, condition-2-es-*, condition-2-ur-*

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

Used by: condition-3-zh-5k

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)

Condition 4

Used by: condition-4-zh-5k

Condition 4 contains strictly native Chinese code -- code written by developers who think and code in Chinese. This uses the same schema as the language-decoded-community dataset rather than the transpilation schema, since there is no English original to reference.

Column Type Description
filename string Original filename
content string The code content
extension string File extension (e.g., .py, .c, .wenyan)
source string Data source (e.g., thestack, wenyan, program_in_chinese)
quality_tier string Quality rating: A (highest) through D (lowest)
sha256 string SHA-256 hash for deduplication
byte_size int64 File size in bytes
total_lines int64 Total line count
cjk_ratio float64 Ratio of CJK characters in the file
has_cjk bool Whether the file contains CJK characters

Experimental Conditions

The Language Decoded experiment uses a ladder of conditions to isolate the mechanism behind code's reasoning benefit:

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
Condition 4 Strictly native code Tests whether code authored by native speakers carries unique signal beyond transpilation

The Experimental Ladder

  • Baseline --> 1: Does code help at all?
  • 1 --> 2: Does the language of keywords matter?
  • 2 --> 3: Does diversity of native-language sources add value beyond keyword swap?
  • 3 --> 4: Does code written in the cultural context of a language carry something that transpiled+mixed can't?

Usage 

from datasets import load_dataset

# Load full-size English code (control)
ds = load_dataset("legesher/language-decoded-data", "condition-1-en-32k")

# Load 5k subset (for QLoRA fine-tuning)
ds = load_dataset("legesher/language-decoded-data", "condition-1-en-5k")

# Load keyword-swapped variants
ds = load_dataset("legesher/language-decoded-data", "condition-2-zh-5k")
ds = load_dataset("legesher/language-decoded-data", "condition-2-es-5k")
ds = load_dataset("legesher/language-decoded-data", "condition-2-ur-5k")

# Load blended native + transpiled (condition 3)
ds = load_dataset("legesher/language-decoded-data", "condition-3-zh-5k")

# Load strictly native code (condition 4)
ds = load_dataset("legesher/language-decoded-data", "condition-4-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 (Python subset)
Transpilation tool Legesher v0.7.3 (legesher-core, legesher-i18n)
Tokenizer CohereLabs/tiny-aya-base
Base model 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

Limitations

  • Source bias: The Stack Dedup skews toward popular, well-starred GitHub repositories, which may not represent the full diversity of Python code in the wild.
  • Keyword-only transpilation: Legesher translates Python reserved words (keywords, builtins, exceptions) but leaves comments, docstrings, string literals, and variable/function names in their original language (typically English). This means condition-2 code is a hybrid of translated keywords and English identifiers.
  • Token count variation: Transpiled code may have different token counts than the English original due to multi-byte characters (especially for Chinese and Urdu), even though the code structure is identical.
  • Single programming language: Currently limited to Python. Results may not generalize to other programming languages.
  • Condition 4 scope: Native Chinese code is limited to publicly available sources (The Stack, Wenyan, Program-in-Chinese, Qi, Mulan) and may not represent the full spectrum of Chinese-language programming.

Citation 

@misc{language-decoded-2026,
  title={Language Decoded: Investigating Language-Dependent vs. Structure-Dependent Reasoning Benefits of Code},
  author={Madison Edgar and Saad Ahmed Bazaz and Tom Sherborne and Rashik Shahjahan and Khojasteh Mirza and Sarah Jawaid and Rafay Mustafa and Sohaib Ahmed Bazaz},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/legesher/language-decoded-data}
}

Links

License

Apache 2.0