remove old names

Shellcode_IA32.py

@@ -1,219 +0,0 @@
-# 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"],
-            #
-            #         }