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legesher
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language-decoded-data

Tasks:
Text Generation
Modalities:
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Formats:
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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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---
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
 dtype: string
 - name: language
 dtype: string
 - name: license
 dtype: string
 - name: token_count
 dtype: int32
 splits:
 - name: train
 num_bytes: 403718262
 num_examples: 31818
 - name: validation
 num_bytes: 42626910
 num_examples: 3536
 download_size: 164619518
 dataset_size: 446345172
 - config_name: condition-1-en-5k
 features:
 - name: file_path
 dtype: string
 - name: code
 dtype: string
 - name: code_en
 dtype: string
 - name: language
 dtype: string
 - name: license
 dtype: string
 - name: token_count
 dtype: int32
 splits:
 - name: train
 num_bytes: 55261555
 num_examples: 4500
 - name: validation
 num_bytes: 6365959
 num_examples: 500
 download_size: 22897728
 dataset_size: 61627514
 - config_name: condition-2-es-32k
 features:
 - name: file_path
 dtype: string
 - name: code
 dtype: string
 - name: code_en
 dtype: string
 - name: language
 dtype: string
 - name: license
 dtype: string
 - name: token_count
 dtype: int32
 splits:
 - name: train
 num_bytes: 408041994
 num_examples: 31818
 - name: validation
 num_bytes: 43090956
 num_examples: 3536
 download_size: 166000000
 dataset_size: 451132950
 - config_name: condition-2-es-5k
 features:
 - name: file_path
 dtype: string
 - name: code
 dtype: string
 - name: code_en
 dtype: string
 - name: language
 dtype: string
 - name: license
 dtype: string
 - name: token_count
 dtype: int32
 splits:
 - name: train
 num_bytes: 55864731
 num_examples: 4500
 - name: validation
 num_bytes: 6432095
 num_examples: 500
 download_size: 23031674
 dataset_size: 62296826
 - config_name: condition-2-ur-32k
 features:
 - name: file_path
 dtype: string
 - name: code
 dtype: string
 - name: code_en
 dtype: string
 - name: language
 dtype: string
 - name: license
 dtype: string
 - name: token_count
 dtype: int32
 splits:
 - name: train
 num_bytes: 415552907
 num_examples: 31818
 - name: validation
 num_bytes: 43879443
 num_examples: 3536
 download_size: 166000000
 dataset_size: 459432350
 - config_name: condition-2-ur-5k
 features:
 - name: file_path
 dtype: string
 - name: code
 dtype: string
 - name: code_en
 dtype: string
 - name: language
 dtype: string
 - name: license
 dtype: string
 - name: token_count
 dtype: int32
 splits:
 - name: train
 num_bytes: 56906247
 num_examples: 4500
 - name: validation
 num_bytes: 6545730
 num_examples: 500
 download_size: 23158039
 dataset_size: 63451977
 - config_name: condition-2-zh-32k
 features:
 - name: file_path
 dtype: string
 - name: code
 dtype: string
 - name: code_en
 dtype: string
 - name: language
 dtype: string
 - name: license
 dtype: string
 - name: token_count
 dtype: int32
 splits:
 - name: train
 num_bytes: 405515831
 num_examples: 31818
 - name: validation
 num_bytes: 45065811
 num_examples: 3536
 download_size: 165387142
 dataset_size: 450581642
 - config_name: 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
 dtype: string
 - name: token_count
 dtype: int32
 splits:
 - name: train
 num_bytes: 55793642
 num_examples: 4500
 - name: validation
 num_bytes: 6422792
 num_examples: 500
 download_size: 22978834
 dataset_size: 62216434
 - config_name: 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
 num_bytes: 4531385
 num_examples: 500
 download_size: 17299185
 dataset_size: 45313851
 - config_name: 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
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 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](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
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](https://huggingface.co/datasets/legesher/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
```python
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](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 |
## 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
```bibtex
@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
- [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)
- [Language Decoded Community (native code)](https://huggingface.co/datasets/legesher/language-decoded-community)
- [Language Decoded Experiments (tracking)](https://huggingface.co/datasets/legesher/language-decoded-experiments)
- [Language Decoded LoRA (model hub)](https://huggingface.co/legesher/language-decoded-lora)
## License
Apache 2.0