Datasets:
Tasks:
Text Classification
Sub-tasks:
sentiment-classification
Languages:
Polish
Size:
1K<n<10K
License:
File size: 4,852 Bytes
0eb59bc 2c1a7b5 0eb59bc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 |
# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
"""PolEmo2.0 IN and OUT"""
import csv
import os
import datasets
_CITATION = """\
@inproceedings{kocon-etal-2019-multi,
title = "Multi-Level Sentiment Analysis of {P}ol{E}mo 2.0: Extended Corpus of Multi-Domain Consumer Reviews",
author = "Koco{\'n}, Jan and
Milkowski, Piotr and
Za{\'s}ko-Zieli{\'n}ska, Monika",
booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/K19-1092",
doi = "10.18653/v1/K19-1092",
pages = "980--991",
}
"""
_DESCRIPTION = """\
The PolEmo2.0 is a set of online reviews from medicine and hotels domains. The task is to predict the sentiment of a review. There are two separate test sets, to allow for in-domain (medicine and hotels) as well as out-of-domain (products and university) validation.
"""
_HOMEPAGE = "https://clarin-pl.eu/dspace/handle/11321/710"
_LICENSE = "CC BY-NC-SA 4.0"
_URLs = {
"in": "https://klejbenchmark.com/static/data/klej_polemo2.0-in.zip",
"out": "https://klejbenchmark.com/static/data/klej_polemo2.0-out.zip",
}
class Polemo2(datasets.GeneratorBasedBuilder):
"""PolEmo2.0"""
VERSION = datasets.Version("1.1.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="in",
version=VERSION,
description="The PolEmo2.0 is a set of online reviews from medicine and hotels domains. The task is to predict the sentiment of a review. There are two separate test sets, to allow for in-domain (medicine and hotels) as well as out-of-domain (products and university) validation.",
),
datasets.BuilderConfig(
name="out",
version=VERSION,
description="The PolEmo2.0 is a set of online reviews from medicine and hotels domains. The task is to predict the sentiment of a review. There are two separate test sets, to allow for in-domain (medicine and hotels) as well as out-of-domain (products and university) validation.",
),
]
DEFAULT_CONFIG_NAME = "in"
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"sentence": datasets.Value("string"),
"target": datasets.ClassLabel(
names=[
"__label__meta_amb",
"__label__meta_minus_m",
"__label__meta_plus_m",
"__label__meta_zero",
]
),
}
),
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
my_urls = _URLs[self.config.name]
data_dir = dl_manager.download_and_extract(my_urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": os.path.join(data_dir, "train.tsv"),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": os.path.join(data_dir, "test_features.tsv"), "split": "test"},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": os.path.join(data_dir, "dev.tsv"),
"split": "dev",
},
),
]
def _generate_examples(self, filepath, split):
"""Yields examples."""
with open(filepath, encoding="utf-8") as f:
reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
for id_, row in enumerate(reader):
yield id_, {
"sentence": row["sentence"],
"target": -1 if split == "test" else row["target"],
}
|