# 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 from typing import Sequence 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( { "id": datasets.Value("string"), "title": datasets.Value("string"), "context": datasets.Value("string"), "question": datasets.Value("string"), "answers": { "text": datasets.Sequence(datasets.Value("string")), "answer_start": datasets.Sequence(datasets.Value("int32")) } } ) """ features = datasets.Features( { "id": datasets.Value("string"), "title": datasets.Value("string"), "context": datasets.Value("string"), "question": datasets.Value("string"), "answers": { "text": datasets.Sequence(datasets.Value("string")), "answer_start": datasets.Sequence(datasets.Value("int32")) } } ) """ 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("https://huggingface.co/datasets/Serhii/Custom_SQuAD/blob/main/Dataset.json") data_dir = "/content/drive/MyDrive/datasets/custom_squad/Dataset.json" 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.""" print(f"FILEPATH ------------------ {filepath}") df = pd.read_json(filepath, lines=True) 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, { "id": row["id"], "title": "", "context": row["context"], "question": row["question"], "answers": { "text": row["answers"]["text"], "answer_start": [0] } } """ for idx, row in data.iterrows(): yield idx, { "id": row["id"], "title": row["title"], "context": row["context"], "question": row["question"], "answers": { "text": row["answers"]["text"], "answer_start": row["answers"]["answer_start"] } } """