| """News headlines and categories dataset.""" | |
| from __future__ import absolute_import, division, print_function | |
| import datasets | |
| _DESCRIPTION = """\ | |
| Copy of [Kaggle dataset](https://www.kaggle.com/abhinavmoudgil95/short-jokes), adding to Huggingface for ease of use. | |
| Description from Kaggle: | |
| Context | |
| Generating humor is a complex task in the domain of machine learning, and it requires the models to understand the deep semantic meaning of a joke in order to generate new ones. Such problems, however, are difficult to solve due to a number of reasons, one of which is the lack of a database that gives an elaborate list of jokes. Thus, a large corpus of over 0.2 million jokes has been collected by scraping several websites containing funny and short jokes. | |
| Visit my Github repository for more information regarding collection of data and the scripts used. | |
| Content | |
| This dataset is in the form of a csv file containing 231,657 jokes. Length of jokes ranges from 10 to 200 characters. Each line in the file contains a unique ID and joke. | |
| Disclaimer | |
| It has been attempted to keep the jokes as clean as possible. Since the data has been collected by scraping websites, it is possible that there may be a few jokes that are inappropriate or offensive to some people. | |
| """ | |
| _CITATION = None | |
| _TRAIN_DOWNLOAD_URL = "https://raw.githubusercontent.com/Fraser-Greenlee/my-huggingface-datasets/master/data/short-jokes/train.txt" | |
| class ShortJokes(datasets.GeneratorBasedBuilder): | |
| """Short jokes dataset.""" | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| 'text': datasets.Value("string"), | |
| } | |
| ), | |
| homepage="https://github.com/Fraser-Greenlee/my-huggingface-datasets", | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL) | |
| return [ | |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}), | |
| ] | |
| def _generate_examples(self, filepath): | |
| """Generate examples.""" | |
| with open(filepath, encoding="utf-8") as txt_lines_file: | |
| data = [] | |
| for line in txt_lines_file: | |
| data.append({'text': line}) | |
| for id_, row in enumerate(data): | |
| yield id_, row | |