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Datasets:
legesher
/
language-decoded-data

Tasks:
Text Generation
Modalities:
Tabular
Text
Formats:
parquet
Languages:
English
Chinese
Spanish
Size:
100K - 1M
ArXiv:
Tags:
code
multilingual
legesher
transpilation
tiny-aya-expedition
language-decoded
Libraries:
Datasets
Dask
Polars
License:
Dataset card Data Studio Files
xet
 Community
13
language-decoded-data / README.md
madiedgar's picture
madiedgar
docs: correct source datasets (Phase 3 = the-stack-v2-dedup, Phase 2 = the-stack v1)
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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-103k
    data_files:
      - split: train
        path: data/condition-1-en-103k/train-*
      - split: validation
        path: data/condition-1-en-103k/validation-*
  - config_name: condition-1-en-20k
    data_files:
      - split: train
        path: data/condition-1-en-20k/train-*
      - split: validation
        path: data/condition-1-en-20k/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-103k
    data_files:
      - split: train
        path: data/condition-2-es-103k/train-*
      - split: validation
        path: data/condition-2-es-103k/validation-*
  - config_name: condition-2-es-20k
    data_files:
      - split: train
        path: data/condition-2-es-20k/train-*
      - split: validation
        path: data/condition-2-es-20k/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-103k
    data_files:
      - split: train
        path: data/condition-2-ur-103k/train-*
      - split: validation
        path: data/condition-2-ur-103k/validation-*
  - config_name: condition-2-ur-20k
    data_files:
      - split: train
        path: data/condition-2-ur-20k/train-*
      - split: validation
        path: data/condition-2-ur-20k/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-103k
    data_files:
      - split: train
        path: data/condition-2-zh-103k/train-*
      - split: validation
        path: data/condition-2-zh-103k/validation-*
  - config_name: condition-2-zh-20k
    data_files:
      - split: train
        path: data/condition-2-zh-20k/train-*
      - split: validation
        path: data/condition-2-zh-20k/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-5-es-5k-c4ai-aya-expanse-32b
    data_files:
      - split: train
        path: data/condition-5-es-5k-c4ai-aya-expanse-32b/train-*
      - split: validation
        path: data/condition-5-es-5k-c4ai-aya-expanse-32b/validation-*
  - config_name: condition-5-ur-5k-c4ai-aya-expanse-32b
    data_files:
      - split: train
        path: data/condition-5-ur-5k-c4ai-aya-expanse-32b/train-*
      - split: validation
        path: data/condition-5-ur-5k-c4ai-aya-expanse-32b/validation-*
  - config_name: condition-5-zh-5k-c4ai-aya-expanse-32b
    data_files:
      - split: train
        path: data/condition-5-zh-5k-c4ai-aya-expanse-32b/train-*
      - split: validation
        path: data/condition-5-zh-5k-c4ai-aya-expanse-32b/validation-*
  - config_name: phase-2-the-stack-v1-condition-1-en-32k
    data_files:
      - split: train
        path: data/phase-2-the-stack-v1/condition-1-en-32k/train-*
      - split: validation
        path: data/phase-2-the-stack-v1/condition-1-en-32k/validation-*
  - config_name: phase-2-the-stack-v1-condition-1-en-5k
    data_files:
      - split: train
        path: data/phase-2-the-stack-v1/condition-1-en-5k/train-*
      - split: validation
        path: data/phase-2-the-stack-v1/condition-1-en-5k/validation-*
  - config_name: phase-2-the-stack-v1-condition-2-es-32k
    data_files:
      - split: train
        path: data/phase-2-the-stack-v1/condition-2-es-32k/train-*
      - split: validation
        path: data/phase-2-the-stack-v1/condition-2-es-32k/validation-*
  - config_name: phase-2-the-stack-v1-condition-2-es-5k
    data_files:
      - split: train
        path: data/phase-2-the-stack-v1/condition-2-es-5k/train-*
      - split: validation
        path: data/phase-2-the-stack-v1/condition-2-es-5k/validation-*
  - config_name: phase-2-the-stack-v1-condition-2-ur-32k
    data_files:
      - split: train
        path: data/phase-2-the-stack-v1/condition-2-ur-32k/train-*
      - split: validation
        path: data/phase-2-the-stack-v1/condition-2-ur-32k/validation-*
  - config_name: phase-2-the-stack-v1-condition-2-ur-5k
    data_files:
      - split: train
        path: data/phase-2-the-stack-v1/condition-2-ur-5k/train-*
      - split: validation
        path: data/phase-2-the-stack-v1/condition-2-ur-5k/validation-*
  - config_name: phase-2-the-stack-v1-condition-2-zh-32k
    data_files:
      - split: train
        path: data/phase-2-the-stack-v1/condition-2-zh-32k/train-*
      - split: validation
        path: data/phase-2-the-stack-v1/condition-2-zh-32k/validation-*
  - config_name: phase-2-the-stack-v1-condition-2-zh-5k
    data_files:
      - split: train
        path: data/phase-2-the-stack-v1/condition-2-zh-5k/train-*
      - split: validation
        path: data/phase-2-the-stack-v1/condition-2-zh-5k/validation-*
  - config_name: phase-2-the-stack-v1-condition-3-zh-5k
    data_files:
      - split: train
        path: data/phase-2-the-stack-v1/condition-3-zh-5k/train-*
      - split: validation
        path: data/phase-2-the-stack-v1/condition-3-zh-5k/validation-*
  - config_name: phase-2-the-stack-v1-condition-4-zh-5k
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      - split: train
        path: data/phase-2-the-stack-v1/condition-4-zh-5k/train-*
      - split: validation
        path: data/phase-2-the-stack-v1/condition-4-zh-5k/validation-*
dataset_info:
  - config_name: condition-1-en-103k
    features:
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        dtype: string
      - name: code_en
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      - name: language
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      - name: file_path
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      - name: license
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      - name: token_count
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  - config_name: condition-1-en-20k
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      - name: code_en
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      - name: language
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      - name: file_path
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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-1-en-5k
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      - name: language
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      - name: token_count
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      - name: validation
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        num_examples: 500
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  - config_name: condition-2-es-103k
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      - name: language
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      - name: file_path
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      - name: validation
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  - config_name: condition-2-es-20k
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  - config_name: condition-2-es-5k
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  - config_name: condition-2-es-5k-token-translator
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  - config_name: condition-2-ur-103k
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      - name: validation
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  - config_name: condition-2-ur-103k-token-translator
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      - name: validation
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        num_examples: 10395
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  - config_name: condition-2-ur-20k
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  - config_name: condition-2-ur-5k
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  - config_name: condition-2-zh-103k
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  - config_name: condition-2-zh-20k
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  - config_name: condition-2-zh-5k
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  - config_name: condition-5-es-5k-c4ai-aya-expanse-32b
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  - config_name: condition-5-ur-5k-c4ai-aya-expanse-32b
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  - config_name: phase-2-the-stack-v1-condition-1-en-32k
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  - config_name: phase-2-the-stack-v1-condition-1-en-5k
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  - config_name: phase-2-the-stack-v1-condition-2-es-32k
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  - config_name: phase-2-the-stack-v1-condition-2-es-5k
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  - config_name: phase-2-the-stack-v1-condition-2-ur-32k
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  - config_name: phase-2-the-stack-v1-condition-2-ur-5k
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  - config_name: phase-2-the-stack-v1-condition-2-zh-32k
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  - config_name: phase-2-the-stack-v1-condition-2-zh-5k
    features:
      - name: file_path
        dtype: string
      - name: code
        dtype: string
      - name: code_en
        dtype: string
      - name: language
        dtype: string
      - name: license
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      - name: token_count
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    splits:
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        num_examples: 4500
      - name: validation
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    download_size: 22978834
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  - config_name: phase-2-the-stack-v1-condition-3-zh-5k
    features:
      - name: file_path
        dtype: large_string
      - name: code
        dtype: large_string
      - name: code_en
        dtype: string
      - name: language
        dtype: large_string
      - name: license
        dtype: large_string
      - name: token_count
        dtype: int64
      - name: source_type
        dtype: large_string
    splits:
      - name: train
        num_bytes: 40782466
        num_examples: 4500
      - name: validation
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        num_examples: 500
    download_size: 17299185
    dataset_size: 45313851
  - config_name: phase-2-the-stack-v1-condition-4-zh-5k
    features:
      - name: filename
        dtype: string
      - name: content
        dtype: string
      - name: extension
        dtype: string
      - name: source
        dtype: string
      - name: quality_tier
        dtype: string
      - name: sha256
        dtype: string
      - name: byte_size
        dtype: int64
      - name: total_lines
        dtype: int64
      - name: cjk_ratio
        dtype: float64
      - name: has_cjk
        dtype: bool
    splits:
      - name: train
        num_bytes: 44246508
        num_examples: 6553
      - name: validation
        num_bytes: 7522476
        num_examples: 729
    download_size: 18300000
    dataset_size: 51768984

Language Decoded | Multilingual Code Dataset

Note (2026-05-18): Current Phase 3 configs use the short condition-* namespace and include 103k, 20k, and 5k sizes for Conditions 1--2. Phase 2 configs remain available under the phase-2-the-stack-v1-* namespace for reproducibility.

⚠️ Note on the condition-5-* (model-translated) configs — raw, pre-cleanup: The condition-5-{ur,zh,es}-5k-c4ai-aya-expanse-32b configs are raw LLM translation output and carry known prompt-leakage contamination: translator-model preamble text, ```json wrappers, and Explanation: / Explicación: / 解释: commentary that leaked into string literals and identifier names. This affects rows that parse as valid Python as well as rows that do not. These rows are intentionally retained as-generated to support cleanup research; cleaned configs will be published separately. Treat condition-5-* as raw data.

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, strictly native Chinese code, and a model-translated set in which an LLM translates the full source (identifiers, strings, and comments, not just keywords). Python source for Conditions 1, 2, and 5 is drawn from bigcode/the-stack-v2-dedup (Python subset) for the current Phase 3 configs; the legacy phase-2-the-stack-v1-* configs are sourced from The Stack v1. Conditions 3 and 4 additionally draw on natively-authored code (see those conditions below). Source files are 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

Conditions 1--2 are available in three current Phase 3 sizes: -103k full corpora, -20k random subsets sampled from the corresponding -103k config with seed 42, and -5k compact subsets. Phase 2 -32k configs are still available with the phase-2-the-stack-v1-* prefix. Condition 5 (condition-5-*-c4ai-aya-expanse-32b) is the model-translated set — currently 5k only, and raw/pre-cleanup (see the note above).

Config Condition Language Description Train Val
condition-1-en-103k 1 (control) English Unmodified filtered Python 93,549 10,395
condition-1-en-20k 1 (control) English Random 20k subset of condition-1-en-103k 18,000 2,000
condition-1-en-5k 1 (control) English Compact 5k subset 4,500 500
condition-2-zh-103k 2 Chinese Keyword-swapped Python via Legesher v0.7.3 93,547 10,395
condition-2-zh-20k 2 Chinese Random 20k subset of condition-2-zh-103k 18,000 2,000
condition-2-zh-5k 2 Chinese Compact 5k subset 4,500 500
condition-2-es-103k 2 Spanish Keyword-swapped Python via Legesher v0.7.3 93,547 10,395
condition-2-es-20k 2 Spanish Random 20k subset of condition-2-es-103k 18,000 2,000
condition-2-es-5k 2 Spanish Compact 5k subset 4,500 500
condition-2-ur-103k 2 Urdu Keyword-swapped Python via Legesher v0.7.3 93,547 10,395
condition-2-ur-20k 2 Urdu Random 20k subset of condition-2-ur-103k 18,000 2,000
condition-2-ur-5k 2 Urdu Compact 5k subset 4,500 500
condition-3-zh-5k 3 Chinese Blended: native Chinese code + transpiled Python 4,500 500
condition-4-zh-5k 4 Chinese Strictly native Chinese code 6,553 729
condition-5-ur-5k-c4ai-aya-expanse-32b 5 Urdu Model-translated (full LLM translation via Cohere Aya) — raw, pre-cleanup 4,088 381
condition-5-zh-5k-c4ai-aya-expanse-32b 5 Chinese Model-translated (full LLM translation via Cohere Aya) — raw, pre-cleanup 4,052 381
condition-5-es-5k-c4ai-aya-expanse-32b 5 Spanish Model-translated (full LLM translation via Cohere Aya) — raw, pre-cleanup 4,032 329

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 source dataset.
license string SPDX license identifier for the source file.
token_count int64 Token count computed using the CohereLabs/tiny-aya-base tokenizer.

Condition 5

Used by: condition-5-ur-5k-c4ai-aya-expanse-32b, condition-5-zh-5k-c4ai-aya-expanse-32b, condition-5-es-5k-c4ai-aya-expanse-32b

Condition 5 uses the conditions 1--2 schema plus an idx column. code is the full LLM-translated source (identifiers, strings, comments, and keywords); code_en is the English original. These configs are raw model output — see the note at the top of this card.

Column Type Description
code string Model-translated Python source (full LLM translation via Cohere Aya).
code_en string Original English Python source code.
language string ISO 639-1 language code: ur, zh, or es.
file_path string Original file path in the source dataset.
license string SPDX license identifier for the source file.
idx int64 Source row index into condition-1-en-5k. Enables row-level joins across conditions.
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
Condition 5 Model-translated code Tests whether full LLM translation (identifiers, strings, comments -- not just keywords) changes the reasoning benefit, relative to Condition 2's keyword-only swap

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-103k")

# Load random 20k subsets
ds = load_dataset("legesher/language-decoded-data", "condition-1-en-20k")
ds = load_dataset("legesher/language-decoded-data", "condition-2-zh-20k")
ds = load_dataset("legesher/language-decoded-data", "condition-2-es-20k")
ds = load_dataset("legesher/language-decoded-data", "condition-2-ur-20k")

# 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")

# Load model-translated code (condition 5 -- raw, pre-cleanup)
ds = load_dataset("legesher/language-decoded-data", "condition-5-ur-5k-c4ai-aya-expanse-32b")
ds = load_dataset("legesher/language-decoded-data", "condition-5-zh-5k-c4ai-aya-expanse-32b")
ds = load_dataset("legesher/language-decoded-data", "condition-5-es-5k-c4ai-aya-expanse-32b")

# 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 (Phase 3, condition-*) bigcode/the-stack-v2-dedup (Python subset)
Source dataset (Phase 2, phase-2-the-stack-v1-*) bigcode/the-stack (The Stack v1)
Transpilation tool Legesher v0.7.3 (legesher-core, legesher-i18n)
Tokenizer CohereLabs/tiny-aya-base
Base model CohereLabs/tiny-aya-base (3.35B params)
Condition 5 translation model Cohere c4ai-aya-expanse-32b (Aya Expanse 32B, via the Cohere API)
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 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.
  • Condition 5 is raw model output: The condition-5-* configs contain prompt-leakage contamination -- translator-model preamble text, JSON wrappers, and explanation commentary leaked into string literals and identifier names, in AST-valid and AST-invalid rows alike. Cleaned configs will be published separately. See the note at the top of this card.

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