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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
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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
- 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
 path: data/phase-2-the-stack-v1/condition-3-zh-5k/train-*
 - split: validation
 path: data/phase-2-the-stack-v1/condition-3-zh-5k/validation-*
- config_name: phase-2-the-stack-v1-condition-4-zh-5k
 data_files:
 - split: train
 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:
- config_name: condition-1-en-103k
 features:
 - name: code
 dtype: string
 - name: code_en
 dtype: string
 - name: language
 dtype: string
 - name: file_path
 dtype: string
 - name: license
 dtype: string
 - name: token_count
 dtype: int64
 splits:
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 num_bytes: 1219853377.6640787
 num_examples: 93549
 - name: validation
 num_bytes: 135547957.33592126
 num_examples: 10395
 download_size: 523449014
 dataset_size: 1355401335.0
- config_name: condition-1-en-20k
 features:
 - name: code
 dtype: string
 - name: code_en
 dtype: string
 - name: language
 dtype: string
 - name: file_path
 dtype: string
 - name: license
 dtype: string
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 splits:
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 num_bytes: 236244972
 num_examples: 18000
 - name: validation
 num_bytes: 25420094
 num_examples: 2000
 download_size: 98501038
 dataset_size: 261665066
- config_name: condition-1-en-5k
 features:
 - name: code
 dtype: string
 - name: code_en
 dtype: string
 - name: language
 dtype: string
 - name: file_path
 dtype: string
 - name: license
 dtype: string
 - name: token_count
 dtype: int64
 splits:
 - name: train
 num_bytes: 58678769.40948973
 num_examples: 4500
 - name: validation
 num_bytes: 6519863.26772108
 num_examples: 500
 download_size: 24617109
 dataset_size: 65198632.67721081
- config_name: condition-2-es-103k
 features:
 - name: code
 dtype: string
 - name: code_en
 dtype: string
 - name: language
 dtype: string
 - name: file_path
 dtype: string
 - name: license
 dtype: string
 - name: idx
 dtype: int64
 - name: token_count
 dtype: int64
 splits:
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 num_bytes: 1217884000
 num_examples: 93547
 - name: validation
 num_bytes: 134733742
 num_examples: 10395
 download_size: 511222413
 dataset_size: 1352617742
- config_name: condition-2-es-20k
 features:
 - name: code
 dtype: string
 - name: code_en
 dtype: string
 - name: language
 dtype: string
 - name: file_path
 dtype: string
 - name: license
 dtype: string
 - name: idx
 dtype: int64
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 splits:
 - name: train
 num_bytes: 235645466
 num_examples: 18000
 - name: validation
 num_bytes: 24275395
 num_examples: 2000
 download_size: 97703744
 dataset_size: 259920861
- config_name: condition-2-es-5k
 features:
 - name: code
 dtype: string
 - name: code_en
 dtype: string
 - name: language
 dtype: string
 - name: file_path
 dtype: string
 - name: license
 dtype: string
 - name: idx
 dtype: int64
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 dtype: int64
 splits:
 - name: train
 num_bytes: 58682347
 num_examples: 4500
 - name: validation
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 num_examples: 500
 download_size: 24738568
 dataset_size: 65358369
- config_name: condition-2-es-5k-token-translator
 features:
 - name: code
 dtype: string
 - name: code_en
 dtype: string
 - name: language
 dtype: string
 - name: file_path
 dtype: string
 - name: license
 dtype: string
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 splits:
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 num_bytes: 58682347
 num_examples: 4500
 - name: validation
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 num_examples: 500
 download_size: 24738568
 dataset_size: 65358369
- config_name: condition-2-ur-103k
 features:
 - name: code
 dtype: string
 - name: code_en
 dtype: string
 - name: language
 dtype: string
 - name: file_path
 dtype: string
 - name: license
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 splits:
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 num_examples: 93547
 - name: validation
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 download_size: 514285138
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- config_name: condition-2-ur-103k-token-translator
 features:
 - name: code
 dtype: string
 - name: code_en
 dtype: string
 - name: language
 dtype: string
 - name: file_path
 dtype: string
 - name: license
 dtype: string
 - name: idx
 dtype: int64
 - name: token_count
 dtype: int64
 splits:
 - name: train
 num_bytes: 1245009266
 num_examples: 93547
 - name: validation
 num_bytes: 137722573
 num_examples: 10395
 download_size: 514285138
 dataset_size: 1382731839
- config_name: condition-2-ur-20k
 features:
 - name: code
 dtype: string
 - name: code_en
 dtype: string
 - name: language
 dtype: string
 - name: file_path
 dtype: string
 - name: license
 dtype: string
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 splits:
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 num_bytes: 240838990
 num_examples: 18000
 - name: validation
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 num_examples: 2000
 download_size: 98295520
 dataset_size: 265655832
- config_name: condition-2-ur-5k
 features:
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 dtype: string
 - name: code_en
 dtype: string
 - name: language
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 splits:
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 num_examples: 4500
 - name: validation
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 num_examples: 500
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 dataset_size: 66809430
- config_name: condition-2-ur-5k-token-translator
 features:
 - name: code
 dtype: string
 - name: code_en
 dtype: string
 - name: language
 dtype: string
 - name: file_path
 dtype: string
 - name: license
 dtype: string
 - name: idx
 dtype: int64
 - name: token_count
 dtype: int64
 splits:
 - name: train
 num_bytes: 59974860
 num_examples: 4500
 - name: validation
 num_bytes: 6834570
 num_examples: 500
 download_size: 24875614
 dataset_size: 66809430
- config_name: condition-2-zh-103k
 features:
 - name: code
 dtype: string
 - name: code_en
 dtype: string
 - name: language
 dtype: string
 - name: file_path
 dtype: string
 - name: license
 dtype: string
 - name: idx
 dtype: int64
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 dtype: int64
 splits:
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 num_bytes: 1218763208
 num_examples: 93547
 - name: validation
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 num_examples: 10395
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- config_name: condition-2-zh-103k-token-translator
 features:
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 dtype: string
 - name: code_en
 dtype: string
 - name: language
 dtype: string
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 dtype: string
 - name: license
 dtype: string
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 dtype: int64
 - name: token_count
 dtype: int64
 splits:
 - name: train
 num_bytes: 1218763208
 num_examples: 93547
 - name: validation
 num_bytes: 134838084
 num_examples: 10395
 download_size: 510104801
 dataset_size: 1353601292
- config_name: condition-2-zh-20k
 features:
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 dtype: string
 - name: code_en
 dtype: string
 - name: language
 dtype: string
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 features:
 - name: code
 dtype: string
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 dtype: string
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 dtype: string
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 splits:
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 num_bytes: 58727467
 num_examples: 4500
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 features:
 - name: code
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 splits:
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 features:
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 dtype: string
 - name: code_en
 dtype: string
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 dtype: string
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 splits:
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 num_examples: 329
 download_size: 30639217
 dataset_size: 98728742
- config_name: condition-5-ur-5k-c4ai-aya-expanse-32b
 features:
 - name: code
 dtype: string
 - name: code_en
 dtype: string
 - name: language
 dtype: string
 - name: file_path
 dtype: string
 - name: license
 dtype: string
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 dtype: int64
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 dtype: int64
 splits:
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 num_bytes: 62839812
 num_examples: 4088
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 num_examples: 381
 download_size: 24290156
 dataset_size: 67037222
- config_name: condition-5-zh-5k-c4ai-aya-expanse-32b
 features:
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 dtype: string
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 splits:
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 features:
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 splits:
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 dataset_size: 446345172
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 features:
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 dtype: string
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 dtype: string
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 dtype: string
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 dtype: int32
 splits:
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 num_bytes: 55261555
 num_examples: 4500
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 download_size: 22897728
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- config_name: phase-2-the-stack-v1-condition-2-es-32k
 features:
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 dtype: string
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 splits:
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- config_name: phase-2-the-stack-v1-condition-2-ur-32k
 features:
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 dtype: string
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 dtype: string
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 splits:
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 download_size: 166000000
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 features:
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 features:
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 features:
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 features:
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 dtype: large_string
 - name: code
 dtype: large_string
 - name: code_en
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 features:
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 dtype: string
 - name: byte_size
 dtype: int64
 - name: total_lines
 dtype: int64
 - name: cjk_ratio
 dtype: float64
 - name: has_cjk
 dtype: bool
 splits:
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 num_bytes: 44246508
 num_examples: 6553
 - name: validation
 num_bytes: 7522476
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 download_size: 18300000
 dataset_size: 51768984
---
# 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 include `103k`, `20k`, and `5k` sizes for Conditions 1--2. Phase 2 configs remain available under the `phase-2-the-stack-v1-*` namespace for reproducibility.
Multilingual Python code datasets for the **Language Decoded** project (part of [Cohere's Tiny Aya Expedition](https://aya.for.ai)). See [legesher/language-decoded-experiments](https://huggingface.co/datasets/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"](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: 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](https://huggingface.co/datasets/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](https://huggingface.co/datasets/bigcode/the-stack) (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](https://github.com/legesher/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`](https://huggingface.co/spaces/legesher/legesher-native-code) HF Space.
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. For the full ladder including future directions, see [legesher/language-decoded-experiments](https://huggingface.co/datasets/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
```python
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](https://huggingface.co/datasets/bigcode/the-stack-v2-dedup) (Python subset) |
| Source dataset (Phase 2, `phase-2-the-stack-v1-*`) | [bigcode/the-stack](https://huggingface.co/datasets/bigcode/the-stack) (The Stack v1) |
| 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) |
| 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-code`](https://huggingface.co/spaces/legesher/legesher-native-code) HF Space have been insufficient for stable training. The existing `condition-4-zh-5k` data 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
```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-data}
}
```
## Links
- [Legesher on GitHub](https://github.com/legesher/legesher)
- [Tiny Aya Expedition](https://aya.for.ai)
- [bigcode/the-stack-v2-dedup](https://huggingface.co/datasets/bigcode/the-stack-v2-dedup) (Phase 3 source)
- [bigcode/the-stack](https://huggingface.co/datasets/bigcode/the-stack) (The Stack v1 — Phase 2 source)
- [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