| |
| """20ng question classification dataset.""" |
|
|
|
|
| import csv |
|
|
| import datasets |
| from datasets.tasks import TextClassification |
| import sys |
| csv.field_size_limit(sys.maxsize) |
|
|
| _DESCRIPTION = """\ |
| This data collection contains all the data used in our learning question classification experiments(see [1]), which has question class definitions, the training and testing question sets, examples of preprocessing the questions, feature definition scripts and examples of semantically related word features. |
| This work has been done by Xin Li and Dan Roth and supported by [2]. |
| """ |
|
|
| _CITATION = """""" |
|
|
| _TRAIN_DOWNLOAD_URL = "https://huggingface.co/datasets/vmalperovich/20ng/resolve/main/train.csv" |
| _TEST_DOWNLOAD_URL = "https://huggingface.co/datasets/vmalperovich/20ng/resolve/main/test.csv" |
| _VALID_DOWNLOAD_URL = "https://huggingface.co/datasets/vmalperovich/20ng/raw/main/validaton.csv" |
|
|
|
|
| CATEGORY_MAPPING = {'comp.sys.mac.hardware': 0, |
| 'comp.graphics': 1, |
| 'sci.space': 2, |
| 'talk.politics.guns': 3, |
| 'sci.med': 4, |
| 'comp.sys.ibm.pc.hardware': 5, |
| 'comp.os.ms-windows.misc': 6, |
| 'rec.motorcycles': 7, |
| 'misc.forsale': 8, |
| 'alt.atheism': 9, |
| 'rec.autos': 10, |
| 'sci.electronics': 11, |
| 'comp.windows.x': 12, |
| 'rec.sport.hockey': 13, |
| 'rec.sport.baseball': 14, |
| 'talk.politics.mideast': 15, |
| 'sci.crypt': 16, |
| 'soc.religion.christian': 17, |
| 'talk.politics.misc': 18, |
| 'talk.religion.misc': 19} |
|
|
| class NG(datasets.GeneratorBasedBuilder): |
| """20ng classification dataset.""" |
|
|
| def _info(self): |
| return datasets.DatasetInfo( |
| description=_DESCRIPTION, |
| features=datasets.Features( |
| { |
| "text": datasets.Value("string"), |
| "label": datasets.features.ClassLabel(names=list(CATEGORY_MAPPING.keys())), |
| } |
| ), |
| homepage="", |
| citation=_CITATION, |
| task_templates=[TextClassification(text_column="text", label_column="label")], |
| ) |
|
|
| def _split_generators(self, dl_manager): |
| train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL) |
| test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL) |
| valid_path = dl_manager.download_and_extract(_VALID_DOWNLOAD_URL) |
| return [ |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}), |
| datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}), |
| datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": valid_path}), |
| ] |
|
|
| def _generate_examples(self, filepath): |
| """Generate examples.""" |
| with open(filepath, encoding="utf-8") as csv_file: |
| csv_reader = csv.reader( |
| csv_file, quotechar='"', delimiter=";", quoting=csv.QUOTE_ALL, skipinitialspace=True |
| ) |
| _ = next(csv_reader) |
| for id_, row in enumerate(csv_reader): |
| text, label = row |
| label = CATEGORY_MAPPING[label] |
| yield id_, {"text": text, "label": label} |
