imdb_pt.py

"""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}