Datasets:
Tasks:
Text Classification
Sub-tasks:
topic-classification
Languages:
Romanian
Size:
10K<n<100K
ArXiv:
License:
File size: 6,145 Bytes
f49ef35 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 |
# coding=utf-8
# Copyright 2021 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.
"""MOROCO: The Moldavian and Romanian Dialectal Corpus"""
import datasets
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@inproceedings{ Butnaru-ACL-2019,
author = {Andrei M. Butnaru and Radu Tudor Ionescu},
title = "{MOROCO: The Moldavian and Romanian Dialectal Corpus}",
booktitle = {Proceedings of ACL},
year = {2019},
pages={688--698},
}
"""
# You can copy an official description
_DESCRIPTION = """\
The MOROCO (Moldavian and Romanian Dialectal Corpus) dataset contains 33564 samples of text collected from the news domain.
The samples belong to one of the following six topics:
- culture
- finance
- politics
- science
- sports
- tech
"""
_HOMEPAGE = "https://github.com/butnaruandrei/MOROCO"
_LICENSE = "CC BY-SA 4.0 License"
# The HuggingFace dataset library don't host the datasets but only point to the original files
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URL = "https://raw.githubusercontent.com/butnaruandrei/MOROCO/master/MOROCO/preprocessed/all/"
_TRAIN_SAMPLES_FILE = "train_samples.txt"
_TRAIN_LABELS_FILE = "train_category_labels.txt"
_VAL_SAMPLES_FILE = "validation_samples.txt"
_VAL_LABELS_FILE = "validation_category_labels.txt"
_TEST_SAMPLES_FILE = "test_samples.txt"
_TEST_LABELS_FILE = "test_category_labels.txt"
class MOROCOConfig(datasets.BuilderConfig):
"""BuilderConfig for the MOROCO dataset"""
def __init__(self, **kwargs):
super(MOROCOConfig, self).__init__(**kwargs)
class MOROCO(datasets.GeneratorBasedBuilder):
"""MOROCO dataset"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
MOROCOConfig(name="moroco", version=VERSION, description="MOROCO dataset"),
]
def _info(self):
features = datasets.Features(
{
"id": datasets.Value("string"),
"category": datasets.features.ClassLabel(
names=[
"culture",
"finance",
"politics",
"science",
"sports",
"tech",
]
),
"sample": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=features, # Here we define them above because they are different between the two configurations
# If there's a common (input, target) tuple from the features,
# specify them here. They'll be used if as_supervised=True in
# builder.as_dataset.
supervised_keys=None,
# Homepage of the dataset for documentation
homepage=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
urls_to_download = {
"train_samples": _URL + _TRAIN_SAMPLES_FILE,
"train_labels": _URL + _TRAIN_LABELS_FILE,
"val_samples": _URL + _VAL_SAMPLES_FILE,
"val_labels": _URL + _VAL_LABELS_FILE,
"test_samples": _URL + _TEST_SAMPLES_FILE,
"test_labels": _URL + _TEST_LABELS_FILE,
}
downloaded_files = dl_manager.download(urls_to_download)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"samples_filepath": downloaded_files["train_samples"],
"labels_filepath": downloaded_files["train_labels"],
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"samples_filepath": downloaded_files["test_samples"],
"labels_filepath": downloaded_files["test_labels"],
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"samples_filepath": downloaded_files["val_samples"],
"labels_filepath": downloaded_files["val_labels"],
},
),
]
def _generate_examples(self, samples_filepath, labels_filepath):
"""This function returns the examples in the raw (text) form."""
with open(samples_filepath, "r", encoding="utf-8") as fsamples:
sample_rows = fsamples.read().splitlines()
with open(labels_filepath, "r", encoding="utf-8") as flabels:
label_rows = flabels.readlines()
for i, row in enumerate(sample_rows):
samp_id = row.split("\t")[0]
sample = "".join(row.split("\t")[1:])
label = int(label_rows[i].split("\t")[1])
yield i, {
"id": samp_id,
"category": label - 1,
"sample": sample,
}
|