Datasets:

legesher
/

language-decoded-community

language:
  - zh
license: apache-2.0
task_categories:
  - text-generation
tags:
  - code
  - multilingual
  - legesher
  - tiny-aya-expedition
  - language-decoded
  - native-code
pretty_name: Language Decoded — Community Code
size_categories:
  - 1K<n<10K
configs:
  - config_name: zh
    data_files:
      - split: train
        path: data/zh/train-*.parquet
      - split: validation
        path: data/zh/validation-*.parquet
dataset_info:
  - config_name: zh
    features:
      - name: filename
        dtype: string
      - name: content
        dtype: string
      - name: extension
        dtype: string
      - name: source
        dtype: string
      - name: license
        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: 23921213
        num_examples: 3137
      - name: validation
        num_bytes: 2506431
        num_examples: 349
    download_size: 10076444
    dataset_size: 26427644

Language Decoded — Community Code

Natively-authored multilingual code for the Language Decoded project (part of Cohere's Tiny Aya Expedition). This dataset contains code written by developers in non-English programming languages and code with significant CJK content — not mechanically transpiled from English.

This data serves as a component of Condition 3 ("Mixed Native Sources") and Condition 4 ("Strictly Native Code") in the Language Decoded experiment, which tests whether native-language code improves multilingual reasoning beyond keyword swapping alone.

Available Configs

Config	Language	Files	Description
`zh`	Chinese	3,486	Natively Chinese-authored code from 5 sources

Schema

Column	Type	Description
`filename`	string	Unique file identifier
`content`	string	Full file content
`extension`	string	File extension (e.g., `.py`, `.java`, `.wy`, `.qi`)
`source`	string	Origin dataset or project
`license`	string	SPDX license identifier or `UNKNOWN`
`quality_tier`	string	Quality tier: A (highest), B, C, D
`sha256`	string	SHA-256 hash of file content for deduplication
`byte_size`	int64	File size in bytes
`total_lines`	int64	Number of lines in the file
`cjk_ratio`	float	Ratio of CJK characters to total non-whitespace chars
`has_cjk`	bool	Whether the file contains any CJK characters

Chinese (`zh`) Source Breakdown

Source	Files	Extensions	Description
`thestack`	1,948	.py, .js, .java, …	Code from The Stack with CJK in comments, strings, identifiers
`program_in_chinese`	703	.java, .js, .ts, …	Program in Chinese — code with Chinese identifiers
`qi`	239	.qi	Qi — Chinese-syntax programming language
`mulan`	166	.ul	Mulan — Chinese programming language
`wenyan`	81	.wy	Wenyan — Classical Chinese programming language (20K+ GitHub stars)

Quality Tier Distribution

Tier	Count	Description
A	778	High quality, rich CJK
B	1,158	Good quality
C	789	Moderate quality
D	412	Lower quality, sparse CJK

File Type Distribution

Extension	Count	Extension	Count
.py	2,003	.ul	166
.java	288	.wy	81
.qi	239	.ts	59
.js	205	.c	36
Others	59

Usage

from datasets import load_dataset

# Load Chinese native code
ds = load_dataset("legesher/language-decoded-community", "zh")
train = ds["train"]  # 3,137 files
val = ds["validation"]  # 349 files

# Filter by source
wenyan = train.filter(lambda x: x["source"] == "wenyan")

# Filter by quality
high_quality = train.filter(lambda x: x["quality_tier"] in ("A", "B"))

Relationship to Other Datasets

legesher/language-decoded-data: The main experiment dataset with transpiled code (conditions 1–2), blended datasets (condition 3), and strictly native code (condition 4). Conditions 3 and 4 use native code from this repo.
This repo stores the raw native code with full metadata. The blended and native training datasets live in language-decoded-data.

Limitations

Chinese only: Currently limited to Chinese-language code. Native code for Spanish and Urdu is not yet available.
License uncertainty: Some files (particularly from thestack) have UNKNOWN licenses. These were included because they appeared in The Stack's permissive-license subset, but individual file licenses could not always be verified.
Quality variation: Quality tiers are assigned heuristically based on CJK content ratio, file size, and structural indicators. Tier D files may contain minimal native-language content.
Non-Python files included: Unlike the transpiled datasets (conditions 1–2), this dataset includes code in multiple programming languages (Python, Java, JavaScript, Wenyan, Qi, Mulan, etc.), reflecting the reality of native-language programming ecosystems.
CJK-heavy bias: Files were selected partly based on CJK character presence, which may over-represent code with Chinese comments/strings rather than code with Chinese-language syntax.

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-community}
}

License

Apache 2.0