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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"""
from __future__ import absolute_import, division, print_function
import os
import datasets
_DESCRIPTION = """\
Dutch Book Review Dataset
The DBRD (pronounced dee-bird) dataset contains over 110k book reviews along \
with associated binary sentiment polarity labels and is intended as a \
benchmark for sentiment classification in Dutch.
"""
_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://drive.google.com/uc?export=download&id=1k5UMoqoB3RT4kK9FI5Xyl7RmWWyBSwux"
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,
)
def _vocab_text_gen(self, archive):
for _, ex in self._generate_examples(archive, os.path.join("DBRD", "train")):
yield ex["text"]
def _split_generators(self, dl_manager):
arch_path = dl_manager.download_and_extract(_DOWNLOAD_URL)
data_dir = os.path.join(arch_path, "DBRD")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"directory": os.path.join(data_dir, "train")}
),
datasets.SplitGenerator(
name=datasets.Split.TEST, gen_kwargs={"directory": os.path.join(data_dir, "test")}
),
datasets.SplitGenerator(
name=datasets.Split("unsupervised"),
gen_kwargs={"directory": os.path.join(data_dir, "unsup"), "labeled": False},
),
]
def _generate_examples(self, directory, labeled=True):
"""Generate DBRD examples."""
# For labeled examples, extract the label from the path.
if labeled:
files = {
"pos": sorted(os.listdir(os.path.join(directory, "pos"))),
"neg": sorted(os.listdir(os.path.join(directory, "neg"))),
}
for key in files:
for id_, file in enumerate(files[key]):
filepath = os.path.join(directory, key, file)
with open(filepath, encoding="UTF-8") as f:
yield key + "_" + str(id_), {"text": f.read(), "label": key}
else:
unsup_files = sorted(os.listdir(directory))
for id_, file in enumerate(unsup_files):
filepath = os.path.join(directory, file)
with open(filepath, encoding="UTF-8") as f:
yield id_, {"text": f.read(), "label": -1}
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