Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
sentiment-classification
Languages:
Swedish
Size:
100K - 1M
License:
File size: 2,789 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
from __future__ import absolute_import, division, print_function
import csv
import os
import datasets
_DOWNLOAD_URL = "https://raw.githubusercontent.com/timpal0l/swedish-sentiment/main/swedish_sentiment.zip"
_TRAIN_FILE = "train.csv"
_VAL_FILE = "dev.csv"
_TEST_FILE = "test.csv"
_CITATION = ""
_DESCRIPTION = "Swedish reviews scarped from various public available websites"
class SwedishReviews(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="plain_text",
version=datasets.Version("1.0.0", ""),
description="Plain text import of the Swedish Reviews dataset",
)
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{"text": datasets.Value("string"), "label": datasets.ClassLabel(names=["negative", "positive"])}
),
supervised_keys=None,
homepage="https://github.com/timpal0l/swedish-sentiment",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
dl_dir = dl_manager.download_and_extract(_DOWNLOAD_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": os.path.join(dl_dir, _TEST_FILE)},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"filepath": os.path.join(dl_dir, _VAL_FILE)},
),
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": os.path.join(dl_dir, _TRAIN_FILE)},
),
]
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
with open(filepath, encoding="utf-8") as f:
reader = csv.DictReader(f)
for idx, row in enumerate(reader):
yield idx, {
"text": row["text"],
"label": row["sentiment"],
}
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