Language Decoded
Collection
4 items • Updated
Datasets:
file_path stringlengths 10 10 | code stringlengths 79 330k | code_en stringlengths 79 330k | language stringclasses 1
value | license stringclasses 0
values | token_count int32 24 158k |
|---|---|---|---|---|---|
0037437.py | from classes.Despachante import *
from classes.Sistema import *
from classes.Processo import *
from tkinter import *
from tkinter import ttk
from tkinter.filedialog import askopenfilename as fileChooser
class EscDeProcessos:
def __init__(self, master=None):
#Tamanho da janela
master.minsize(width=7... | from classes.Despachante import *
from classes.Sistema import *
from classes.Processo import *
from tkinter import *
from tkinter import ttk
from tkinter.filedialog import askopenfilename as fileChooser
class EscDeProcessos:
def __init__(self, master=None):
#Tamanho da janela
master.minsize(width=7... | en | null | 3,719 |
0036153.py | #!/usr/bin/env python
# Copyright 2020 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
"""Reports binary size metrics for LaCrOS build artifacts.
More information at //docs/speed/binary_size/metrics.md.
"""
import argpars... | #!/usr/bin/env python
# Copyright 2020 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
"""Reports binary size metrics for LaCrOS build artifacts.
More information at //docs/speed/binary_size/metrics.md.
"""
import argpars... | en | null | 3,750 |
0035653.py | """
Convert characters (chr) to integer (int) labels and vice versa.
REVIEW: index 0 bug, also see:
https://github.com/baidu-research/warp-ctc/tree/master/tensorflow_binding
`ctc_loss`_ maps labels from 0=<unused>, 1=<space>, 2=a, ..., 27=z, 28=<blank>
See: https://www.tensorflow.org/api_docs/python/tf/nn/ctc_loss
"... | """
Convert characters (chr) to integer (int) labels and vice versa.
REVIEW: index 0 bug, also see:
https://github.com/baidu-research/warp-ctc/tree/master/tensorflow_binding
`ctc_loss`_ maps labels from 0=<unused>, 1=<space>, 2=a, ..., 27=z, 28=<blank>
See: https://www.tensorflow.org/api_docs/python/tf/nn/ctc_loss
"... | en | null | 504 |
0037957.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | en | null | 474 |
0001021.py | # Write a Python program to get execution time for a Python method.
import time
def sum_of_n_numbers(x):
start_time = time.time()
s = 0
for i in range(1, x + 1):
s = s + i
end_time = time.time()
return s, end_time - start_time
n = 5
print("\nTime to sum of 1 to ", n, " and required time... | # Write a Python program to get execution time for a Python method.
import time
def sum_of_n_numbers(x):
start_time = time.time()
s = 0
for i in range(1, x + 1):
s = s + i
end_time = time.time()
return s, end_time - start_time
n = 5
print("\nTime to sum of 1 to ", n, " and required time... | en | null | 130 |
0017967.py | import re
import time
from django.conf import settings
from django.utils.timezone import make_aware, make_naive, utc
re_pattern = re.compile('[^\u0000-\uD7FF\uE000-\uFFFF]+', re.UNICODE)
def sanitize_unicode(u):
# We may not be able to store all special characters thanks
# to MySQL's boneheadedness, so acce... | import re
import time
from django.conf import settings
from django.utils.timezone import make_aware, make_naive, utc
re_pattern = re.compile('[^\u0000-\uD7FF\uE000-\uFFFF]+', re.UNICODE)
def sanitize_unicode(u):
# We may not be able to store all special characters thanks
# to MySQL's boneheadedness, so acce... | en | null | 371 |
0048230.py | """
Implements the DIAL-protocol to communicate with the Chromecast
"""
from collections import namedtuple
import json
import logging
import socket
import ssl
import urllib.request
from uuid import UUID
import zeroconf
from .const import CAST_TYPE_CHROMECAST, CAST_TYPES, SERVICE_TYPE_HOST
XML_NS_UPNP_DEVICE = "{urn:... | """
Implements the DIAL-protocol to communicate with the Chromecast
"""
from collections import namedtuple
import json
import logging
import socket
import ssl
import urllib.request
from uuid import UUID
import zeroconf
from .const import CAST_TYPE_CHROMECAST, CAST_TYPES, SERVICE_TYPE_HOST
XML_NS_UPNP_DEVICE = "{urn:... | en | null | 1,839 |
0019568.py | # coding: utf-8
"""
Mux API
Mux is how developers build online video. This API encompasses both Mux Video and Mux Data functionality to help you build your video-related projects better and faster than ever before. # noqa: E501
The version of the OpenAPI document: v1
Contact: devex@mux.com
Gener... | # coding: utf-8
"""
Mux API
Mux is how developers build online video. This API encompasses both Mux Video and Mux Data functionality to help you build your video-related projects better and faster than ever before. # noqa: E501
The version of the OpenAPI document: v1
Contact: devex@mux.com
Gener... | en | null | 1,109 |
0033287.py | import os
import markdown
import codecs
import difflib
try:
import nose
except ImportError as e:
msg = e.args[0]
msg = msg + ". The nose testing framework is required to run the Python-" \
"Markdown tests. Run `pip install nose` to install the latest version."
e.args = (msg,) + e.args[1:]
ra... | import os
import markdown
import codecs
import difflib
try:
import nose
except ImportError as e:
msg = e.args[0]
msg = msg + ". The nose testing framework is required to run the Python-" \
"Markdown tests. Run `pip install nose` to install the latest version."
e.args = (msg,) + e.args[1:]
ra... | en | null | 1,860 |
0018939.py | # -*- coding: utf-8 -*-
"""
Created on Mon Dec 21 16:44:36 2020
@author: wantysal
"""
# Standard library import
import numpy as np
# Local import
from mosqito.sound_level_meter.noct_spectrum._getFrequencies import _getFrequencies
def _spectrum_smoothing(freqs_in, spec, noct, low_freq, high_freq, freqs_out):
"""... | # -*- coding: utf-8 -*-
"""
Created on Mon Dec 21 16:44:36 2020
@author: wantysal
"""
# Standard library import
import numpy as np
# Local import
from mosqito.sound_level_meter.noct_spectrum._getFrequencies import _getFrequencies
def _spectrum_smoothing(freqs_in, spec, noct, low_freq, high_freq, freqs_out):
"""... | en | null | 914 |
0008742.py | #!/usr/bin/env python3
"""
An example script to send data to CommCare using the Submission API
Usage:
$ export CCHQ_PROJECT_SPACE=my-project-space
$ export CCHQ_CASE_TYPE=person
$ export CCHQ_USERNAME=user@example.com
$ export CCHQ_PASSWORD=MijByG_se3EcKr.t
$ export CCHQ_USER_ID=c0ffeeeeeb574eb8b5... | #!/usr/bin/env python3
"""
An example script to send data to CommCare using the Submission API
Usage:
$ export CCHQ_PROJECT_SPACE=my-project-space
$ export CCHQ_CASE_TYPE=person
$ export CCHQ_USERNAME=user@example.com
$ export CCHQ_PASSWORD=MijByG_se3EcKr.t
$ export CCHQ_USER_ID=c0ffeeeeeb574eb8b5... | en | null | 1,996 |
0010801.py | # Copyright (c) 2012 NTT DOCOMO, INC.
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requ... | # Copyright (c) 2012 NTT DOCOMO, INC.
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requ... | en | null | 881 |
0007285.py | import hashlib
from ecdsa.curves import Ed25519, SECP256k1
from .principal import Principal
import ecdsa
class Identity:
def __init__(self, privkey = "", type = "ed25519", anonymous = False):
privkey = bytes(bytearray.fromhex(privkey))
self.anonymous = anonymous
if anonymous:
r... | import hashlib
from ecdsa.curves import Ed25519, SECP256k1
from .principal import Principal
import ecdsa
class Identity:
def __init__(self, privkey = "", type = "ed25519", anonymous = False):
privkey = bytes(bytearray.fromhex(privkey))
self.anonymous = anonymous
if anonymous:
r... | en | null | 908 |
0019223.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# @Date : Feb-09-21 22:23
# @Author : Kelly Hwong (dianhuangkan@gmail.com)
import numpy as np
import tensorflow as tf
class XOR_Dataset(tf.keras.utils.Sequence):
"""XOR_Dataset."""
def __init__(
self,
batch_size=1,
shuffle=False,
... | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# @Date : Feb-09-21 22:23
# @Author : Kelly Hwong (dianhuangkan@gmail.com)
import numpy as np
import tensorflow as tf
class XOR_Dataset(tf.keras.utils.Sequence):
"""XOR_Dataset."""
def __init__(
self,
batch_size=1,
shuffle=False,
... | en | null | 600 |
0016186.py | """
This module is for managing OMERO imports, making use of the OMERO CLI,
which can be called from a Python script. Note that this code requires
a properly structured import.json file, which is produced during data
intake (using the intake.py module).
"""
import logging
from ezomero import post_dataset, post_projec... | """
This module is for managing OMERO imports, making use of the OMERO CLI,
which can be called from a Python script. Note that this code requires
a properly structured import.json file, which is produced during data
intake (using the intake.py module).
"""
import logging
from ezomero import post_dataset, post_projec... | en | null | 3,805 |
0026028.py | import librosa
import librosa.filters
import numpy as np
import tensorflow as tf
from scipy import signal
from scipy.io import wavfile
def load_wav(path, sr):
return librosa.core.load(path, sr=sr)[0]
def save_wav(wav, path, sr):
wav *= 32767 / max(0.01, np.max(np.abs(wav)))
#proposed by @dsmiller
wav... | import librosa
import librosa.filters
import numpy as np
import tensorflow as tf
from scipy import signal
from scipy.io import wavfile
def load_wav(path, sr):
return librosa.core.load(path, sr=sr)[0]
def save_wav(wav, path, sr):
wav *= 32767 / max(0.01, np.max(np.abs(wav)))
#proposed by @dsmiller
wav... | en | null | 2,853 |
0028922.py | from buildtest.cli.help import buildtest_help
def test_buildtest_help():
buildtest_help(command="build")
buildtest_help(command="buildspec")
buildtest_help(command="config")
buildtest_help(command="cdash")
buildtest_help(command="history")
buildtest_help(command="inspect")
buildtest_help(c... | from buildtest.cli.help import buildtest_help
def test_buildtest_help():
buildtest_help(command="build")
buildtest_help(command="buildspec")
buildtest_help(command="config")
buildtest_help(command="cdash")
buildtest_help(command="history")
buildtest_help(command="inspect")
buildtest_help(c... | en | null | 140 |
0037594.py | #!/usr/bin/env python2
# Copyright (c) 2014 The Bitcoin Core developers
# Distributed under the MIT software license, see the accompanying
# file COPYING or http://www.opensource.org/licenses/mit-license.php.
#
# Test resurrection of mined transactions when
# the blockchain is re-organized.
#
from test_framework impo... | #!/usr/bin/env python2
# Copyright (c) 2014 The Bitcoin Core developers
# Distributed under the MIT software license, see the accompanying
# file COPYING or http://www.opensource.org/licenses/mit-license.php.
#
# Test resurrection of mined transactions when
# the blockchain is re-organized.
#
from test_framework impo... | en | null | 1,050 |
0010807.py | #!/usr/bin/env python3
import logging
import sys
import subprocess
from taupage import configure_logging, get_config
def main():
"""Configure custom sysctl parameters
If a sysctl section is present, add the valid parameters to sysctl and reloads.
"""
CUSTOM_SYSCTL_CONF = '/etc/sysctl.d/99-custom.co... | #!/usr/bin/env python3
import logging
import sys
import subprocess
from taupage import configure_logging, get_config
def main():
"""Configure custom sysctl parameters
If a sysctl section is present, add the valid parameters to sysctl and reloads.
"""
CUSTOM_SYSCTL_CONF = '/etc/sysctl.d/99-custom.co... | en | null | 397 |
0017586.py | # flake8: noqa
from typing import Any
from fugue_version import __version__
from IPython import get_ipython
from IPython.display import Javascript
from fugue_notebook.env import NotebookSetup, _setup_fugue_notebook
_HIGHLIGHT_JS = r"""
require(["codemirror/lib/codemirror"]);
function set(str) {
var obj = {}, wor... | # flake8: noqa
from typing import Any
from fugue_version import __version__
from IPython import get_ipython
from IPython.display import Javascript
from fugue_notebook.env import NotebookSetup, _setup_fugue_notebook
_HIGHLIGHT_JS = r"""
require(["codemirror/lib/codemirror"]);
function set(str) {
var obj = {}, wor... | en | null | 1,006 |
End of preview. Expand in Data Studio
Language Decoded | Multilingual Code Dataset
Multilingual Python code datasets for the Language Decoded project (part of Cohere's Tiny Aya Expedition), 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") 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 (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 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 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
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 (Python subset) |
| Transpilation tool | Legesher v0.7.3 (legesher-core, legesher-i18n) |
| Tokenizer | CohereLabs/tiny-aya-base |
| Base model | 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
@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
- Tiny Aya Expedition
- bigcode/the-stack-dedup
- Language Decoded Community (native code)
- Language Decoded Experiments (tracking)
- Language Decoded LoRA (model hub)
License
Apache 2.0
- Downloads last month
- 140
Size of downloaded dataset files:
790 MB
Size of the auto-converted Parquet files:
790 MB
Number of rows:
173,698