| """IMDB movie reviews dataset translated to Portuguese.""" | |
| import csv | |
| import datasets | |
| from datasets.tasks import TextClassification | |
| _DESCRIPTION = """\ | |
| Large Movie Review Dataset. | |
| This is a dataset for binary sentiment classification containing substantially \ | |
| more data than previous benchmark datasets. We provide a set of 25,000 highly \ | |
| polar movie reviews for training, and 25,000 for testing. There is additional \ | |
| unlabeled data for use as well.\ | |
| """ | |
| _CITATION = """\ | |
| @InProceedings{maas-EtAl:2011:ACL-HLT2011, | |
| author = {Maas, Andrew L. and Daly, Raymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y. and Potts, Christopher}, | |
| title = {Learning Word Vectors for Sentiment Analysis}, | |
| booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies}, | |
| month = {June}, | |
| year = {2011}, | |
| address = {Portland, Oregon, USA}, | |
| publisher = {Association for Computational Linguistics}, | |
| pages = {142--150}, | |
| url = {http://www.aclweb.org/anthology/P11-1015} | |
| } | |
| """ | |
| _DOWNLOAD_URL = "https://huggingface.co/datasets/maritaca-ai/imdb_pt/resolve/main" | |
| class IMDBReviewsConfig(datasets.BuilderConfig): | |
| """BuilderConfig for IMDBReviews.""" | |
| def __init__(self, **kwargs): | |
| """BuilderConfig for IMDBReviews. | |
| Args: | |
| **kwargs: keyword arguments forwarded to super. | |
| """ | |
| super().__init__(version=datasets.Version("1.0.0", ""), **kwargs) | |
| class Imdb(datasets.GeneratorBasedBuilder): | |
| """IMDB movie reviews dataset translated to Portuguese.""" | |
| BUILDER_CONFIGS = [ | |
| IMDBReviewsConfig( | |
| name="plain_text", | |
| description="Plain text", | |
| ) | |
| ] | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| {"text": datasets.Value("string"), "label": datasets.features.ClassLabel(names=["negativo", "positivo"])} | |
| ), | |
| supervised_keys=None, | |
| homepage="http://ai.stanford.edu/~amaas/data/sentiment/", | |
| citation=_CITATION, | |
| task_templates=[TextClassification(text_column="text", label_column="label")], | |
| ) | |
| def _split_generators(self, dl_manager): | |
| train_path = dl_manager.download_and_extract(f"{_DOWNLOAD_URL}/train.csv") | |
| test_path = dl_manager.download_and_extract(f"{_DOWNLOAD_URL}/test.csv") | |
| test_all_path = dl_manager.download_and_extract(f"{_DOWNLOAD_URL}/test-all.csv") | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path, "split": "train"} | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, gen_kwargs={"filepath": test_path, "split": "test"} | |
| ), | |
| datasets.SplitGenerator( | |
| name="test_all", gen_kwargs={"filepath": test_all_path, "split": "test_all"} | |
| ), | |
| ] | |
| def _generate_examples(self, filepath, split): | |
| """Generate aclImdb examples.""" | |
| with open(filepath, encoding="utf-8") as csv_file: | |
| csv_reader = csv.reader( | |
| csv_file, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True | |
| ) | |
| for id_, row in enumerate(csv_reader): | |
| if id_ == 0: | |
| continue | |
| text, label = row | |
| yield id_, {"text": text, "label": label} |