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 or LLM-translated from English.

Experiment and proposed paper title: Language Decoded: Exploring the Impact of Native Code on Multilingual Models

This data serves as the corpus for Condition 3 ("Mixed Native Sources") and is intended to serve as the corpus for Condition 4 ("Community-Contributed Native Code") in the Language Decoded experimental ladder. See legesher/language-decoded-experiments for the canonical project description.

How Condition 3 and Condition 4 differ

Both conditions deal with native-language code, but they ask different questions:

Condition 3 ("Mixed Native Sources") uses code pulled from real-world public-source repositories — incidentally available code that humans wrote in or with the target language. Phase 3 trained condition-3-zh-5k from data assembled here.
Condition 4 ("Community-Contributed Native Code")'s design goal is 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. Currently pending sufficient direct community contributions to assemble a stable training corpus; in neither Phase 2 nor Phase 3 evaluation. Cond-5's fully-translated data served as Phase 3's practical proxy because gathering native-authored code at scale proved difficult.

If you'd like to contribute Python (or other-language) code where you approached the problem in your native target language, the contribution interface is the legesher/legesher-native-code HF Space — contributions there feed into the cond-4 corpus.

Available Configs

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

Native code for Spanish and Urdu is not yet available.

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 training data hub. Holds the per-condition training corpora (cond-1 raw English, cond-2 Legesher-transpiled, cond-3 mixed native sources, cond-4 native-authored, cond-5 fully translated via c4ai-aya-expanse-32b).
legesher/language-decoded-experiments: The canonical project source-of-truth — experiment tracking, evaluation results, analysis, and the full experimental ladder.
legesher/language-decoded-lora: LoRA adapters trained on the per-condition corpora.
This repo stores the raw native code with full metadata. The blended and native training datasets used for fine-tuning 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 Phase 3 training corpora for cond-1, cond-2, and cond-5 — which are Python-only — this dataset includes code in multiple programming languages (Python, Java, JavaScript, Wenyan, Qi, Mulan, etc.). This reflects the reality of native-language programming ecosystems and is intentional for cond-3.
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.
Native-authored ≠ scraped: Although this corpus comes closer to native-authored code than the transpiled (cond-2) or fully-translated (cond-5) corpora, the inclusion criteria are based on CJK presence and source provenance, not on whether the original author was thinking in the target language while writing the code. Cond-4 will eventually distinguish that more cleanly via direct contribution.

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-community}
}

License

Apache 2.0

Attribution & takedown

Portions of this dataset derive from publicly available code repositories (natively-authored code scraped from public GitHub and Gitee repositories, plus community contributions collected with consent via the collection Space), collected under a best-effort license review. If you are the author of code included here and believe it was included improperly, or you would like attribution added or your code removed:

open a discussion on this repository's Community tab, or
email Madi Edgar at support@legesher.com (the maintainer listed in CITATION.cff).

We commit to citing, and on request from authors removing, any source identified as improperly included. Removals are propagated in a new dataset revision; prior revisions remain in git history unless a takedown requires otherwise.