README.md

---
language:
  - zh
license: apache-2.0
task_categories:
  - text-generation
tags:
  - code
  - multilingual
  - legesher
  - tiny-aya-expedition
  - language-decoded
  - native-code
  - 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 — 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](https://aya.for.ai)). 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](https://huggingface.co/datasets/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`](https://huggingface.co/spaces/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](https://github.com/program-in-chinese) — code with Chinese identifiers                  |
| `qi`                 | 239   | .qi                | [Qi](https://github.com/nicevoice/qi) — Chinese-syntax programming language                                  |
| `mulan`              | 166   | .ul                | [Mulan](https://github.com/MulanRevive/mulan-rework) — Chinese programming language                          |
| `wenyan`             | 81    | .wy                | [Wenyan](https://github.com/wenyan-lang/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

```python
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](https://huggingface.co/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](https://huggingface.co/datasets/legesher/language-decoded-experiments)**: The canonical project source-of-truth — experiment tracking, evaluation results, analysis, and the full experimental ladder.
- **[legesher/language-decoded-lora](https://huggingface.co/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

```bibtex
@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.