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2.36k
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apple/cups
test/5.3-lpq.sh
Shell
80
179
22
Apache-2.0
2025-02-25T00:20:28.878501Z
2018-10-02T22:21:25+00:00Z
#!/bin/sh # # Test the lpq command. # # Copyright © 2007 by Apple Inc. # Copyright © 1997-2005 by Easy Software Products, all rights reserved. # # Licensed under Apache License v2.0. See the file "LICENSE" for more # information. # echo "LPQ Test" echo "" echo " lpq -P Test1" $runcups $VALGRIND ../berkeley/lpq -P ...
apple/cups
test/5.1-lpadmin.sh
Shell
80
630
63
Apache-2.0
2025-02-25T00:20:51.660213Z
2018-10-02T22:21:25+00:00Z
#!/bin/sh # # Test the lpadmin command. # # Copyright © 2007-2018 by Apple Inc. # Copyright © 1997-2005 by Easy Software Products, all rights reserved. # # Licensed under Apache License v2.0. See the file "LICENSE" for more # information. # echo "Add Printer Test" echo "" echo " lpadmin -p Test3 -v file:/dev/null ...
apple/cups
test/5.4-lpstat.sh
Shell
80
415
49
Apache-2.0
2025-02-25T00:20:52.642297Z
2019-04-26T21:40:54+00:00Z
#!/bin/sh # # Test the lpstat command. # # Copyright © 2007-2019 by Apple Inc. # Copyright © 1997-2005 by Easy Software Products, all rights reserved. # # Licensed under Apache License v2.0. See the file "LICENSE" for more # information. # echo "LPSTAT Basic Test" echo "" echo " lpstat -t" $runcups $VALGRIND ../sy...
apple/cups
test/5.8-cancel.sh
Shell
80
408
48
Apache-2.0
2025-02-25T00:20:58.086178Z
2019-04-26T21:40:54+00:00Z
#!/bin/sh # # Test the cancel command. # # Copyright © 2007-2019 by Apple Inc. # Copyright © 1997-2006 by Easy Software Products, all rights reserved. # # Licensed under Apache License v2.0. See the file "LICENSE" for more # information. # echo "Cancel Destination Test" echo "" echo " lp -d Test1 -o job-hold-until...
apple/cups
test/5.9-lpinfo.sh
Shell
80
370
46
Apache-2.0
2025-02-25T00:20:59.067376Z
2019-04-26T21:40:54+00:00Z
#!/bin/sh # # Test the lpinfo command. # # Copyright © 2007-2019 by Apple Inc. # Copyright © 1997-2005 by Easy Software Products, all rights reserved. # # Licensed under Apache License v2.0. See the file "LICENSE" for more # information. # echo "LPINFO Devices Test" echo "" echo " lpinfo -v" $runcups $VALGRIND ../...
apple/swift-lldb
scripts/analyze-project-deps.py
Python
80
2,361
208
NOASSERTION
2025-02-25T00:32:28.338543Z
2019-03-21T07:19:09+00:00Z
#! /usr/bin/env python import argparse import itertools import os import re import sys from collections import defaultdict from use_lldb_suite import lldb_root parser = argparse.ArgumentParser( description='Analyze LLDB project #include dependencies.') parser.add_argument('--show-counts', default=False, action='...
apple/swift-lldb
lit/lit.cfg.py
Python
80
1,156
106
NOASSERTION
2025-02-25T00:32:30.631002Z
2019-09-18T19:20:58+00:00Z
# -*- Python -*- import os import platform import re import shutil import site import sys import lit.formats from lit.llvm import llvm_config from lit.llvm.subst import FindTool from lit.llvm.subst import ToolSubst site.addsitedir(os.path.dirname(__file__)) from helper import toolchain # name: The name of this test...
apple/swift-lldb
scripts/install-lldb-swift.py
Python
95
2,280
283
NOASSERTION
2025-02-25T00:34:35.867688Z
2016-12-02T09:32:43+00:00Z
#!/usr/bin/env python # install-lldb-swift.py # # This source file is part of the Swift.org open source project # # Copyright (c) 2014 - 2016 Apple Inc. and the Swift project authors # Licensed under Apache License v2.0 with Runtime Library Exception # # See https://swift.org/LICENSE.txt for license information # See ...
apple/swift-lldb
scripts/macos-setup-codesign.sh
Shell
80
666
57
NOASSERTION
2025-02-25T00:34:37.834753Z
2018-06-14T18:04:13+00:00Z
#!/bin/bash CERT="lldb_codesign" function error() { echo error: "$@" exit 1 } function cleanup { # Remove generated files rm -f "$TMPDIR/$CERT.tmpl" "$TMPDIR/$CERT.cer" "$TMPDIR/$CERT.key" > /dev/null 2>&1 } trap cleanup EXIT # Check if the certificate is already present in the system keychain secu...
apple/swift-lldb
scripts/swig_bot.py
Python
80
714
85
NOASSERTION
2025-02-25T00:35:03.526736Z
2016-09-06T20:57:50+00:00Z
#!/usr/bin/env python """ SWIG generation top-level script. Supports both local and remote generation of SWIG bindings for multiple languages. """ # Python modules import argparse import logging import sys import traceback # LLDB modules import use_lldb_suite # swig_bot modules from swig_bot_lib import client from...
apple/swift-lldb
scripts/utilsOsType.py
Python
80
866
103
NOASSERTION
2025-02-25T00:35:13.281154Z
2018-10-04T20:34:58+00:00Z
""" Utility module to determine the OS Python running on -------------------------------------------------------------------------- File: utilsOsType.py Overview: Python module to supply functions and an enumeration to help determine the platform type, bit size and OS cur...
apple/swift-lldb
source/lldb.cpp
C++
90
972
99
NOASSERTION
2025-02-25T00:35:18.326737Z
2019-04-17T18:39:30+00:00Z
//===-- lldb.cpp ------------------------------------------------*- C++ -*-===// // // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions. // See https://llvm.org/LICENSE.txt for license information. // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception // //===---------------------------...

FineCode: A High-Quality Code Dataset

Disclaimer: No big files uploaded...yet The one upload is simply an example format and doesn't contain all the highest quality code or the final version.

Overview

FineCode is a meticulously curated dataset aimed at providing high-quality code for training and benchmarking code generation models. While many code datasets exist on Hugging Face, the quality of code varies significantly. FineCode seeks to address this by rigorously filtering and scoring code to ensure only well-structured, optimized, and readable examples are included.

Dataset Details

  • Total Size: 100B tokens
  • Languages Covered: Top 15 programming languages, with an emphasis on the top 5
  • Source: Scraped from top-ranked GitHub repositories of well-known organizations and highly rated open-source projects
  • Filtering Criteria:
    • Code is evaluated using Llama3.2-3B, which assigns a quality score (0-100) based on factors like readability, optimization, and best practices
    • Only code with a score of 75 or higher is included in the dataset
    • Additional filtering techniques are applied to remove low-quality or redundant content

Availability

The dataset will be released soon on Hugging Face, along with the code used for data collection and filtering, allowing full transparency and reproducibility.

Stay tuned for updates!

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