Datasets:
metadata
language:
- en
- zh
- es
- ur
license: apache-2.0
task_categories:
- text-generation
tags:
- code
- multilingual
- legesher
- transpilation
- tiny-aya-expedition
- language-decoded
- arxiv:2408.10914
- arxiv:2603.11510
- arxiv:2211.15533
- arxiv:2510.09591
- arxiv:1809.05053
- arxiv:2308.16884
- arxiv:2106.06937
- arxiv:2210.03057
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
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- split: validation
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- config_name: phase-2-the-stack-v1-condition-4-zh-5k
data_files:
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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:
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Language Decoded | Multilingual Code Dataset
Experiment and proposed paper title: Language Decoded: Exploring the Impact of Native Code on Multilingual Models
Note (2026-05-18): Current Phase 3 configs use the short
condition-*namespace and include103k,20k, and5ksizes for Conditions 1--2. Phase 2 configs remain available under thephase-2-the-stack-v1-*namespace for reproducibility.
Multilingual Python code datasets for the Language Decoded project (part of Cohere's Tiny Aya Expedition). See legesher/language-decoded-experiments for the canonical project description, the full experimental ladder, and the paper-grade evaluation results.
Research Question
How does fine-tuning on non-English code — whether transpiled, mixed-native, or fully translated — affect a model's multilingual reasoning, and how does that impact differ from fine-tuning on English code?
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: how does the impact of non-English code differ from English code, and how does that vary by language, structure, and corpus construction?
Dataset Description
This dataset provides filtered, quality-controlled Python source code in multiple configurations: the original English (cond-1); three Legesher-transpiled variants (cond-2 zh/es/ur, with Python's reserved words translated to the target language); a community-collected raw native-source corpus (cond-3); strictly native code (cond-4, pending); and a model-translated set (cond-5, where c4ai-aya-expanse-32b translates everything translatable inside the file). 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 (non-dedup). Conditions 3 and 4 draw on natively-authored or community-contributed code (see those conditions below).
Source-file control
Cond-1, cond-2, and cond-5 all train on the same 5,000-file subset drawn from bigcode/the-stack-v2-dedup (with a parallel 20k subset for the 20k tier). Differences across these conditions reflect the processing pipeline (raw / transpiled / fully translated), not file-quality or content drift. Cond-3 is the deliberate exception — its source files are a different population by design (community-collected from varied online sources, potentially including non-Python files).
Source files for cond-1/2/5 are filtered using:
- 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
Cond-2 variants are produced using Legesher v0.7.3, which translates Python's reserved words (37 keywords, 72 built-in functions, 66 exceptions, plus the numerical system for some target languages) into the target language while preserving code structure and user logic. Cond-5 takes the Legesher-transpiled output and runs it through c4ai-aya-expanse-32b via the Cohere API to translate the remaining content — identifiers, comments, docstrings, string literals, and any other natural-language wording — into the target language. Logic and structure are preserved throughout.
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 | Legesher v0.7.3 reserved-word translation | 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 | Legesher v0.7.3 reserved-word translation | 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 | Legesher v0.7.3 reserved-word translation | 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 Legesher-transpiled (reserved-word translated) 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
In Phase 3, Condition 3 ("Mixed Native Sources") refers to community-collected raw Chinese code from varied online public-source repositories — reflecting how non-English Python is actually used in real-world projects. The "Mixed Native Sources" name carries from Phase 2, where it originally referred to a planned composite (native code padded with cond-2 transpiled files); in Phase 3 the "mixed" refers to the diversity of source locations, not a cond-2/native composite. The physical dataset has not changed across phases.
The schema includes a source_type column from the Phase 2 composite design, which remains "native" or "transpiled" depending on each row's origin. 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" (Legesher reserved-word translation of English) |
Condition 4
Used by: condition-4-zh-5k
Condition 4 ("Community-Contributed Native Code") is intended to contain code whose problem-solving logic is itself native — written as if a native speaker were approaching the problem, not English code that was later translated. The current dataset reflects an earlier Phase 2 attempt to assemble this corpus; community contributions were insufficient for stable training, so cond-4 was not evaluated in either Phase 2 or Phase 3. Cond-5's fully-translated data served as Phase 3's practical proxy because gathering native-authored code at scale proved difficult. Direct contributions to the cond-4 corpus are open at the legesher/legesher-native-code HF Space.
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. For the full ladder including future directions, see legesher/language-decoded-experiments.
| 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 | Reserved-Word Translation (Legesher) | Tests whether translating Python's reserved words (keywords, exceptions, built-in functions, numerical system for some target languages) into the target language matters |
| Condition 3 | Mixed Native Sources | Tests whether code pulled from real-world public-source repositories (humans actually writing in the target language) adds value beyond Legesher's mechanical translation |
| Condition 4 | Community-Contributed Native Code | Tests whether code whose problem-solving logic is itself native (not translated from English) carries unique signal — pending sufficient community contributions |
| Condition 5 | Synthesized Native Code | Tests whether full translation (Legesher reserved words + c4ai-aya-expanse-32b full file translation) changes the picture relative to Condition 2's partial translation |
The Experimental Ladder
- Baseline → 1: Does code help at all?
- 1 → 2: Does the language Python is written in matter? (Cond-2 translates Python's reserved words; user logic preserved.)
- 2 → 3: Does code humans actually wrote in or with the target language add value beyond Legesher's mechanical translation?
- 2 → 5: Cond-2 translates only Python's reserved words; cond-5 goes further by also translating identifiers, comments, docstrings, and string literals via
c4ai-aya-expanse-32b. Logic preserved. Does full translation produce different effects than partial translation? - 3 → 5 (implicit): Human-authored vs. machine-synthesized native code.
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 Legesher-transpiled variants (reserved-word translation)
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 not yet evaluated: Community contributions to the
legesher/legesher-native-codeHF Space have been insufficient for stable training. The existingcondition-4-zh-5kdata is a Phase 2 attempt limited to publicly available sources (The Stack, Wenyan, Program-in-Chinese, Qi, Mulan). Cond-5's fully-translated data served as the Phase 3 practical proxy for cond-4's "logic in the target language" goal. - 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: Exploring the Impact of Native Code on Multilingual Models},
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
- Legesher on GitHub
- Tiny Aya Expedition
- bigcode/the-stack-v2-dedup (Phase 3 source)
- bigcode/the-stack (The Stack v1 — Phase 2 source)
- Language Decoded Community (native code)
- Language Decoded Experiments (tracking)
- Language Decoded LoRA (model hub)
License
Apache 2.0