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"""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} |