repo_name stringlengths 5 100 | path stringlengths 4 375 | copies stringclasses 991
values | size stringlengths 4 7 | content stringlengths 666 1M | license stringclasses 15
values |
|---|---|---|---|---|---|
ahmedbodi/AutobahnPython | examples/asyncio/websocket/echo/client_coroutines.py | 13 | 2044 | ###############################################################################
##
## Copyright (C) 2013-2014 Tavendo GmbH
##
## 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
##
## h... | apache-2.0 |
ifduyue/django | django/core/checks/registry.py | 13 | 3108 | from itertools import chain
from django.utils.itercompat import is_iterable
class Tags:
"""
Built-in tags for internal checks.
"""
admin = 'admin'
caches = 'caches'
compatibility = 'compatibility'
database = 'database'
models = 'models'
security = 'security'
signals = 'signals... | bsd-3-clause |
kmike/scikit-learn | sklearn/utils/__init__.py | 3 | 10094 | """
The :mod:`sklearn.utils` module includes various utilites.
"""
from collections import Sequence
import numpy as np
from scipy.sparse import issparse
import warnings
from .murmurhash import murmurhash3_32
from .validation import (as_float_array, check_arrays, safe_asarray,
assert_all_fini... | bsd-3-clause |
houlixin/BBB-TISDK | linux-devkit/sysroots/i686-arago-linux/usr/lib/python2.7/encodings/cp1250.py | 593 | 13942 | """ Python Character Mapping Codec cp1250 generated from 'MAPPINGS/VENDORS/MICSFT/WINDOWS/CP1250.TXT' with gencodec.py.
"""#"
import codecs
### Codec APIs
class Codec(codecs.Codec):
def encode(self,input,errors='strict'):
return codecs.charmap_encode(input,errors,encoding_table)
def decode(self,in... | gpl-2.0 |
dataxu/ansible | lib/ansible/modules/system/kernel_blacklist.py | 125 | 4009 | #!/usr/bin/python
# encoding: utf-8 -*-
# Copyright: (c) 2013, Matthias Vogelgesang <matthias.vogelgesang@gmail.com>
# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)
from __future__ import absolute_import, division, print_function
__metaclass__ = type
ANSIBLE_METADATA = {'... | gpl-3.0 |
163gal/Time-Line | libs_arm/wx/_controls.py | 2 | 332374 | "# This file was created automatically by SWIG 1.3.29.\n# Don't modify this file, modify the SWIG in(...TRUNCATED) | gpl-3.0 |
blackbliss/callme | flask/lib/python2.7/site-packages/werkzeug/contrib/cache.py | 306 | 23519 | "# -*- coding: utf-8 -*-\n\"\"\"\n werkzeug.contrib.cache\n ~~~~~~~~~~~~~~~~~~~~~~\n\n The (...TRUNCATED) | mit |
pipet/pipet | pipet/sources/zendesk/tasks.py | 2 | 1544 | "from contextlib import contextmanager\nfrom datetime import datetime\nfrom inspect import isclass\n(...TRUNCATED) | apache-2.0 |
tomchristie/django | django/apps/config.py | 55 | 8047 | "import os\nfrom importlib import import_module\n\nfrom django.core.exceptions import ImproperlyConf(...TRUNCATED) | bsd-3-clause |
prutseltje/ansible | test/units/modules/network/f5/test_bigip_gtm_datacenter.py | 23 | 6819 | "# -*- coding: utf-8 -*-\n#\n# Copyright (c) 2017 F5 Networks Inc.\n# GNU General Public License v3.(...TRUNCATED) | gpl-3.0 |
End of preview. Expand in Data Studio
CodeParrot 🦜 Dataset
What is it?
This is the full CodeParrot dataset. It contains Python files used to train the code generation model in Chapter 10: Training Transformers from Scratch in the NLP with Transformers book. You can find the full code in the accompanying Github repository.
Creation
It was created with the GitHub dataset available via Google's BigQuery. It contains approximately 22 million Python files and is 180 GB (50 GB compressed) big. The SQL query to create the dataset is the following:
SELECT
f.repo_name, f.path, c.copies, c.size, c.content, l.license
FROM
`bigquery-public-data.github_repos.files` AS f
JOIN
`bigquery-public-data.github_repos.contents` AS c
ON
f.id = c.id
JOIN
`bigquery-public-data.github_repos.licenses` AS l
ON
f.repo_name = l.repo_name
WHERE
NOT c.binary
AND ((f.path LIKE '%.py')
AND (c.size BETWEEN 1024 AND 1048575))
Duplication
Note that about 70% of the dataset is duplicated. If you use the dataset make sure to deal with them appropriately. See codeparrot-clean for a deduplicated version of this dataset.
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