spam-text-messages-dataset / spam-text-messages-dataset.py
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import datasets
import pandas as pd
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {spam-text-messages-dataset},
author = {TrainingDataPro},
year = {2023}
}
"""
_DESCRIPTION = """\
The SMS spam dataset contains a collection of text messages. The dataset
includes a diverse range of spam messages, including promotional offers,
fraudulent schemes, phishing attempts, and other forms of unsolicited
communication.
Each SMS message is represented as a string of text, and each entry in the
dataset also has a link to the corresponding screenshot. The dataset's content
represents real-life examples of spam messages that users encounter in their
everyday communication.
"""
_NAME = 'spam-text-messages-dataset'
_HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"
_LICENSE = ""
_DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"
class SpamTextMessagesDataset(datasets.GeneratorBasedBuilder):
"""Small sample of image-text pairs"""
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features({
'image': datasets.Image(),
'text': datasets.Value('string')
}),
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
images = dl_manager.download(f"{_DATA}images.tar.gz")
annotations = dl_manager.download(f"{_DATA}{_NAME}.csv")
images = dl_manager.iter_archive(images)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN,
gen_kwargs={
"images": images,
'annotations': annotations
}),
]
def _generate_examples(self, images, annotations):
annotations_df = pd.read_csv(annotations, sep=';')
for idx, (image_path, image) in enumerate(images):
yield idx, {
"image": {
"path": image_path,
"bytes": image.read()
},
'text':
annotations_df.loc[annotations_df['image'].str.lower() ==
image_path.lower()]['text'].values[0]
}