Datasets:
muchocine

Fine-Grained Tasks: sentiment-classification
Languages: Spanish
Multilinguality: monolingual
Size Categories: 1K<n<10K
Language Creators: found
Annotations Creators: found
Source Datasets: original
Licenses: unknown
muchocine / muchocine.py
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Update files from the datasets library (from 1.18.0)
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# 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.
import glob
import os
import re
from xml.dom.minidom import parseString
import datasets
from datasets.tasks import TextClassification
# no BibTeX citation
_CITATION = ""
_DESCRIPTION = """\
The Muchocine reviews dataset contains 3,872 longform movie reviews in Spanish language,
each with a shorter summary review, and a rating on a 1-5 scale.
"""
_LICENSE = "CC-BY-2.1"
_URLs = {"default": "http://www.lsi.us.es/~fermin/corpusCine.zip"}
class Muchocine(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.1.1")
def _info(self):
features = datasets.Features(
{
"review_body": datasets.Value("string"),
"review_summary": datasets.Value("string"),
"star_rating": datasets.ClassLabel(names=[str(i) for i in range(1, 6)]),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage="http://www.lsi.us.es/~fermin/index.php/Datasets",
license=_LICENSE,
citation=_CITATION,
task_templates=[
TextClassification(text_column="review_body", label_column="star_rating"),
TextClassification(text_column="review_summary", label_column="star_rating"),
],
)
def _split_generators(self, dl_manager):
my_urls = _URLs[self.config.name]
data_dir = dl_manager.download_and_extract(my_urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepaths": sorted(glob.glob(os.path.join(data_dir, "corpusCriticasCine", "*.xml"))),
"split": "train",
},
),
]
def _generate_examples(self, filepaths, split):
for filepath in filepaths:
with open(filepath, encoding="latin-1") as f:
id = re.search(r"\d+\.xml", filepath)[0][:-4]
txt = f.read()
txt = txt.replace("&ldquo;", '"').replace("&rdquo;", '"').replace("&hellip;", "")
txt = txt.replace("&lsquo;", '"').replace("&rsquo;", '"').replace("&prime;", "")
txt = txt.replace("&agrave;", "à").replace("&ndash;", "-").replace("&egrave;", "è")
txt = txt.replace("&ouml;", "ö").replace("&ccedil;", "ç").replace("&", "and")
try:
doc = parseString(txt)
except Exception as e:
# skip 6 malformed xml files, for example unescaped < and >
_ = e
continue
btxt = ""
review_bod = doc.getElementsByTagName("body")
if len(review_bod) > 0:
for node in review_bod[0].childNodes:
if node.nodeType == node.TEXT_NODE:
btxt += node.data + " "
rtxt = ""
review_summ = doc.getElementsByTagName("summary")
if len(review_summ) > 0:
for node in review_summ[0].childNodes:
if node.nodeType == node.TEXT_NODE:
rtxt += node.data + " "
yield id, {
"review_body": btxt,
"review_summary": rtxt,
"star_rating": doc.documentElement.attributes["rank"].value,
}