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import datasets
from datasets.tasks import TextClassification
_DESCRIPTION = """
Movie Review Dataset.
This is a dataset containing 4,265 positive and 4,265 negative processed
sentences from Rotten Tomatoes movie reviews.
"""
_CITATION = """
@InProceedings{Pang+Lee:05a,
author = {Bo Pang and Lillian Lee},
title = {Seeing stars: Exploiting class relationships for sentiment
categorization with respect to rating scales},
booktitle = {Proceedings of the ACL},
year = 2005
}
"""
_DOWNLOAD_URL = "https://testerstories.com/files/ai_learn/rt-polaritydata.tar.gz"
class RottenTomatoesReviews(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"text": datasets.Value("string"),
"label": datasets.features.ClassLabel(names=["neg", "pos"]),
}
),
supervised_keys=[""],
homepage="http://www.cs.cornell.edu/people/pabo/movie-review-data/",
citation=_CITATION,
task_templates=[
TextClassification(text_column="text", label_column="label")
],
)
def _split_generators(self, dl_manager):
archive = dl_manager.download(_DOWNLOAD_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"split_key": "train",
"files": dl_manager.iter_archive(archive),
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"split_key": "validation",
"files": dl_manager.iter_archive(archive),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"split_key": "test",
"files": dl_manager.iter_archive(archive),
},
),
]
def _get_examples_from_split(self, split_key, files):
data_dir = "rt-polaritydata/"
pos_samples, neg_samples = None, None
for path, f in files:
if path == data_dir + "rt-polarity.pos":
pos_samples = [line.decode("latin-1").strip() for line in f]
elif path == data_dir + "rt-polarity.neg":
neg_samples = [line.decode("latin-1").strip() for line in f]
if pos_samples is not None and neg_samples is not None:
break
i1 = int(len(pos_samples) * 0.8 + 0.5)
i2 = int(len(pos_samples) * 0.9 + 0.5)
train_samples = pos_samples[:i1] + neg_samples[:i1]
train_labels = (["pos"] * i1) + (["neg"] * i1)
validation_samples = pos_samples[i1:i2] + neg_samples[i1:i2]
validation_labels = (["pos"] * (i2 - i1)) + (["neg"] * (i2 - i1))
test_samples = pos_samples[i2:] + neg_samples[i2:]
test_labels = (["pos"] * (len(pos_samples) - i2)) + (
["neg"] * (len(pos_samples) - i2)
)
if split_key == "train":
return (train_samples, train_labels)
if split_key == "validation":
return (validation_samples, validation_labels)
if split_key == "test":
return (test_samples, test_labels)
else:
raise ValueError(f"Invalid split key {split_key}")
def _generate_examples(self, split_key, files):
split_text, split_labels = self._get_examples_from_split(split_key, files)
for text, label in zip(split_text, split_labels):
data_key = split_key + "_" + text
feature_dict = {"text": text, "label": label}
yield data_key, feature_dict
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