Datasets:
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import json
import os
import datasets
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {Ember2018-malware-v2},
author=Christian Williams
},
year={2024}
}
"""
_DESCRIPTION = """\
This dataset is based on the EMBER 2018 Malware Analysis dataset that was uploaded to kaggle
"""
_HOMEPAGE = "https://www.kaggle.com/datasets/dhoogla/ember-2018-v2-features"
_LICENSE = ""
class EMBERConfig(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.1.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="text_classification",
version=VERSION,
description="This part of my dataset can be used to train LLMs for text classification",
license=""
)
]
DEFAULT_CONFIG_NAME = "text_classification"
def _info(self):
if self.config.name == "text_classification":
features = datasets.Features(
{
"input": datasets.Value("string"),
"label": datasets.Value("string"),
}
)
else:
features = datasets.Features(
{
"input": datasets.Value("string"),
"label": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
# _URLS = {}
_URLS = "https://huggingface.co/datasets/cw1521/ember2018-malware-v2/tree/main/data"
urls = _URLS[self.config.name]
data_dir = dl_manager.download_and_extract("https://huggingface.co/datasets/cw1521/ember2018-malware-v2/tree/main/data")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepaths": os.path.join(data_dir, "ember2018_train_*.jsonl"),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepaths": os.path.join(data_dir, "ember2018_test_*.jsonl"),
"split": "test"
},
)
]
def _generate_examples(self, filepaths, split):
key = 0
for id, filepath in enumerate(filepaths[split]):
key += 1
with open(filepath[id], encoding="utf-8") as f:
data_list = json.load(f)
for data in data_list:
if self.config.name == "text_classification":
data.remove
yield key, {
"input": data["input"],
"label": data["label"]
}
else:
yield key, {
"input": data["input"],
"label": data["label"]
}
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