Datasets:

stanfordnlp
/

imdb

	# coding=utf-8
	# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
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	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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	# limitations under the License.

	# Lint as: python3
	"""IMDB movie reviews dataset."""

	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 = "http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz"


	class IMDBReviewsConfig(datasets.BuilderConfig):
	"""BuilderConfig for IMDBReviews."""

	def __init__(self, **kwargs):
	"""BuilderConfig for IMDBReviews.

	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(IMDBReviewsConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)


	class Imdb(datasets.GeneratorBasedBuilder):
	"""IMDB movie reviews dataset."""

	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=["neg", "pos"])}
	),
	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):
	archive = dl_manager.download(_DOWNLOAD_URL)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN, gen_kwargs={"files": dl_manager.iter_archive(archive), "split": "train"}
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST, gen_kwargs={"files": dl_manager.iter_archive(archive), "split": "test"}
	),
	datasets.SplitGenerator(
	name=datasets.Split("unsupervised"),
	gen_kwargs={"files": dl_manager.iter_archive(archive), "split": "train", "labeled": False},
	),
	]

	def _generate_examples(self, files, split, labeled=True):
	"""Generate aclImdb examples."""
	# For labeled examples, extract the label from the path.
	if labeled:
	label_mapping = {"pos": 1, "neg": 0}
	for path, f in files:
	if path.startswith(f"aclImdb/{split}"):
	label = label_mapping.get(path.split("/")[2])
	if label is not None:
	yield path, {"text": f.read().decode("utf-8"), "label": label}
	else:
	for path, f in files:
	if path.startswith(f"aclImdb/{split}"):
	if path.split("/")[2] == "unsup":
	yield path, {"text": f.read().decode("utf-8"), "label": -1}