Datasets:
Tasks:
Text Generation
Sub-tasks:
language-modeling
Multilinguality:
translation
Size Categories:
unknown
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
Tags:
License:
File size: 9,822 Bytes
8c09aff |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 |
# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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 required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""TODO: Add a description here."""
import csv
import json
import os
import pandas as pd
import datasets
# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@inproceedings{liguori-etal-2021-shellcode,
title = "{S}hellcode{\_}{IA}32: A Dataset for Automatic Shellcode Generation",
author = "Liguori, Pietro and
Al-Hossami, Erfan and
Cotroneo, Domenico and
Natella, Roberto and
Cukic, Bojan and
Shaikh, Samira",
booktitle = "Proceedings of the 1st Workshop on Natural Language Processing for Programming (NLP4Prog 2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.nlp4prog-1.7",
doi = "10.18653/v1/2021.nlp4prog-1.7",
pages = "58--64",
abstract = "We take the first step to address the task of automatically generating shellcodes, i.e., small pieces of code used as a payload in the exploitation of a software vulnerability, starting from natural language comments. We assemble and release a novel dataset (Shellcode{\_}IA32), consisting of challenging but common assembly instructions with their natural language descriptions. We experiment with standard methods in neural machine translation (NMT) to establish baseline performance levels on this task.",
}
"""
# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
Shellcode_IA32 is a dataset for shellcode generation from English intents. The shellcodes are compilable on Intel Architecture 32-bits.
"""
# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = "https://github.com/dessertlab/Shellcode_IA32"
# TODO: Add the licence for the dataset here if you can find it
_LICENSE = "GNU GENERAL PUBLIC LICENSE"
# TODO: Add link to the official dataset URLs here
# The HuggingFace dataset library don't host the datasets but only point to the original files
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLs = {
'default': "https://raw.githubusercontent.com/dessertlab/Shellcode_IA32/main/Shellcode_IA32.tsv",
}
# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
class ShellcodeIA32(datasets.GeneratorBasedBuilder):
"""Shellcode_IA32 a dataset for shellcode generation"""
VERSION = datasets.Version("1.1.0")
# This is an example of a dataset with multiple configurations.
# If you don't want/need to define several sub-sets in your dataset,
# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
# If you need to make complex sub-parts in the datasets with configurable options
# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
# BUILDER_CONFIG_CLASS = MyBuilderConfig
# You will be able to load one or the other configurations in the following list with
# data = datasets.load_dataset('my_dataset', 'first_domain')
# data = datasets.load_dataset('my_dataset', 'second_domain')
# BUILDER_CONFIGS = [
# datasets.BuilderConfig(name="default", version=VERSION, description="This part of my dataset covers the default train/test split"),
# #datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"),
# ]
DEFAULT_CONFIG_NAME = "default" # It's not mandatory to have a default configuration. Just use one if it make sense.
def _info(self):
# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
features = datasets.Features(
{
"intent": datasets.Value("string"),
"snippet": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=features, # Here we define them above because they are different between the two configurations
# If there's a common (input, target) tuple from the features,
# specify them here. They'll be used if as_supervised=True in
# builder.as_dataset.
supervised_keys=None,
# Homepage of the dataset for documentation
homepage=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
my_urls = _URLs[self.config.name]
data_dir = dl_manager.download_and_extract(my_urls)
# return [
# datasets.SplitGenerator(
# name=datasets.Split.TRAIN,
# # These kwargs will be passed to _generate_examples
# gen_kwargs={
# "filepath": os.path.join(data_dir, "Shellcode_IA32.tsv"),
# "split": "train",
# },
# ),
# datasets.SplitGenerator(
# name=datasets.Split.TEST,
# # These kwargs will be passed to _generate_examples
# gen_kwargs={
# "filepath": os.path.join(data_dir, "Shellcode_IA32.tsv"),
# "split": "test"
# },
# ),
# datasets.SplitGenerator(
# name=datasets.Split.VALIDATION,
# # These kwargs will be passed to _generate_examples
# gen_kwargs={
# "filepath": os.path.join(data_dir, "Shellcode_IA32.tsv"),
# "split": "dev",
# },
# ),
# ]
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir),
"split": "test"
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir),
"split": "dev",
},
),
]
def _generate_examples(
self, filepath, split # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
):
""" Yields examples as (key, example) tuples. """
# This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
# The `key` is here for legacy reason (tfds) and is not important in itself.
"""This function returns the examples in the raw (text) form."""
df = pd.read_csv(filepath, delimiter = '\t')
train = df.sample(frac = 0.8, random_state = 0)
test = df.drop(train.index)
dev = test.sample(frac = 0.5, random_state = 0)
test = test.drop(dev.index)
if split == 'train':
data = train
elif split == 'dev':
data = dev
elif split == 'test':
data = test
for idx, row in data.iterrows():
yield idx, {
"snippet": row["SNIPPETS"],
"intent": row["INTENTS"],
}
# with open(filepath, encoding="utf-8") as f:
# reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
# reader =
# for idx, row in enumerate(reader):
#
# yield idx, {
# "snippet": row["SNIPPETS"],
# "intent": row["INTENTS"],
#
# }
|