Datasets:
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# Loading script for the ReviewsFinder dataset.
import json
import csv
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """ """
_DESCRIPTION = """ GuiaCat is a dataset consisting of 5.750 restaurant reviews in Catalan, with 5 associated scores and a label of sentiment. The data was provided by GuiaCat and curated by the BSC. """
_HOMEPAGE = """ https://huggingface.co/datasets/projecte-aina/Parafraseja/ """
_URL = "https://huggingface.co/datasets/projecte-aina/Parafraseja/resolve/main/"
_TRAINING_FILE = "train.csv"
_DEV_FILE = "dev.csv"
_TEST_FILE = "test.csv"
class GuiaCatConfig(datasets.BuilderConfig):
""" Builder config for the reviews_finder dataset """
def __init__(self, **kwargs):
"""BuilderConfig for reviews_finder.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(GuiaCatConfig, self).__init__(**kwargs)
class GuiaCat(datasets.GeneratorBasedBuilder):
""" GuiaCat Dataset """
BUILDER_CONFIGS = [
GuiaCatConfig(
name="GuiaCat",
version=datasets.Version("1.0.0"),
description="GuiaCat dataset",
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"text": datasets.Value("string"),
"label": datasets.features.ClassLabel
(names=
[
"molt bo",
"bo",
"regular",
"dolent",
"molt dolent"
]
),
}
),
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
urls_to_download = {
"train": f"{_URL}{_TRAINING_FILE}",
"dev": f"{_URL}{_DEV_FILE}",
"test": f"{_URL}{_TEST_FILE}",
}
downloaded_files = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
]
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
logger.info("generating examples from = %s", filepath)
with open(filepath) as f:
read = csv.reader(f)
data = [item for item in read]
for id_, article in enumerate(data):
text = article[5]
label = article[6]
yield id_, {
"text": text,
"label": label,
}
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