Datasets:
Tasks:
Text Classification
Languages:
Catalan
Multilinguality:
monolingual
Language Creators:
found
Annotations Creators:
found
Tags:
License:
| # 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] | |
| text = "" | |
| label = "" | |
| for id_, article in enumerate(data): | |
| text = article[5] | |
| label = article[6] | |
| yield id_, { | |
| "text": text, | |
| "label": label, | |
| } | |