Custom_SQuAD.py

# 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"]
                    }
            }
        """