| 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] | |
| } | |