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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
"""Dutch Book Review Dataset"""
import datasets
from datasets.tasks import TextClassification
_DESCRIPTION = """\
The Dutch Book Review Dataset (DBRD) contains over 110k book reviews of which \
22k have associated binary sentiment polarity labels. It is intended as a \
benchmark for sentiment classification in Dutch and created due to a lack of \
annotated datasets in Dutch that are suitable for this task.
"""
_CITATION = """\
@article{DBLP:journals/corr/abs-1910-00896,
author = {Benjamin van der Burgh and
Suzan Verberne},
title = {The merits of Universal Language Model Fine-tuning for Small Datasets
- a case with Dutch book reviews},
journal = {CoRR},
volume = {abs/1910.00896},
year = {2019},
url = {http://arxiv.org/abs/1910.00896},
archivePrefix = {arXiv},
eprint = {1910.00896},
timestamp = {Fri, 04 Oct 2019 12:28:06 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1910-00896.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
"""
_DOWNLOAD_URL = "https://github.com/benjaminvdb/DBRD/releases/download/v3.0/DBRD_v3.tgz"
class DBRDConfig(datasets.BuilderConfig):
"""BuilderConfig for DBRD."""
def __init__(self, **kwargs):
"""BuilderConfig for DBRD.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(DBRDConfig, self).__init__(version=datasets.Version("3.0.0", ""), **kwargs)
class DBRD(datasets.GeneratorBasedBuilder):
"""Dutch Book Review Dataset."""
BUILDER_CONFIGS = [
DBRDConfig(
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="https://github.com/benjaminvdb/DBRD",
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": "unsup", "labeled": False},
),
]
def _generate_examples(self, files, split, labeled=True):
"""Generate DBRD examples."""
# For labeled examples, extract the label from the path.
if labeled:
for path, f in files:
if path.startswith(f"DBRD/{split}"):
label = {"pos": 1, "neg": 0}[path.split("/")[2]]
yield path, {"text": f.read().decode("utf-8"), "label": label}
else:
for path, f in files:
if path.startswith(f"DBRD/{split}"):
yield path, {"text": f.read().decode("utf-8"), "label": -1}
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