Datasets:
Tasks:
Text Classification
Modalities:
Text
Sub-tasks:
sentiment-classification
Size:
10K - 100K
License:
File size: 4,446 Bytes
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# 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
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
"""IMDB movie reviews dataset."""
import gzip
import json
import datasets
from datasets.tasks import TextClassification
_DESCRIPTION = """\
Large Movie Review Dataset translated to Dutch.
This is a dataset for binary sentiment classification containing substantially \
more data than previous benchmark datasets. We provide a set of 24,992 highly \
polar movie reviews for training, and 24,992 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/yhavinga/imdb_dutch/resolve/main/dataset/{split}.jsonl.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"),
"text_en": 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):
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"files": dl_manager.download([_DOWNLOAD_URL.format(split="train")]),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"files": dl_manager.download([_DOWNLOAD_URL.format(split="test")]),
"split": "test",
},
),
datasets.SplitGenerator(
name=datasets.Split("unsupervised"),
gen_kwargs={
"files": dl_manager.download(
[_DOWNLOAD_URL.format(split="unsupervised")]
),
"split": "unsupervised",
"labeled": False,
},
),
]
def _generate_examples(self, files, split, labeled=True):
"""Generate aclImdb examples."""
for filepath in files:
with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8") as f:
for _, line in enumerate(f):
example = json.loads(line)
yield _, example
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