Datasets:
Tasks:
Text Classification
Language Creators:
found
Annotations Creators:
found
Source Datasets:
original
License:
| import datasets | |
| import csv | |
| _LICENSE = """ | |
| TO DO: Licencia | |
| """ | |
| with open("README.md", "r") as f: | |
| lines = iter(f.readlines()) | |
| for line in lines: | |
| if "### Dataset Summary" in line: | |
| break | |
| next(lines) | |
| _DESCRIPTION = next(lines) | |
| _CITATION = """ | |
| TO DO: Cita | |
| """ | |
| _LANGUAGES = { | |
| "es": "Spanish", | |
| "pt": "Portuguese" | |
| } | |
| _ALL_LANGUAGES = "all_languages" | |
| _VERSION = "1.0.0" | |
| _HOMEPAGE_URL = "https://github.com/lpsc-fiuba/MeLiSA" | |
| _DOWNLOAD_URL = "./{lang}/{split}.csv" | |
| class MeLiSAConfig(datasets.BuilderConfig): | |
| """BuilderConfig for MeLiSA.""" | |
| def __init__(self, languages=None, **kwargs): | |
| """Constructs a MeLiSAConfig. | |
| Args: | |
| **kwargs: keyword arguments forwarded to super. | |
| """ | |
| super(MeLiSAConfig, self).__init__(version=datasets.Version(_VERSION, ""), **kwargs) | |
| self.languages = languages | |
| class MeLiSA(datasets.GeneratorBasedBuilder): | |
| """MeLiSA dataset.""" | |
| BUILDER_CONFIGS = [ | |
| MeLiSAConfig( | |
| name=_ALL_LANGUAGES, | |
| languages=_LANGUAGES, | |
| description="A collection of Mercado Libre reviews specifically designed to aid research in spanish and portuguese sentiment classification.", | |
| ) | |
| ] + [ | |
| MeLiSAConfig( | |
| name=lang, | |
| languages=[lang], | |
| description=f"{_LANGUAGES[lang]} examples from a collection of Mercado Libre reviews specifically designed to aid research in sentiment classification", | |
| ) | |
| for lang in _LANGUAGES | |
| ] | |
| BUILDER_CONFIG_CLASS = MeLiSAConfig | |
| DEFAULT_CONFIG_NAME = _ALL_LANGUAGES | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "country": datasets.Value("string"), | |
| "category": datasets.Value("string"), | |
| "review_content": datasets.Value("string"), | |
| "review_title": datasets.Value("string"), | |
| "review_rate": datasets.Value("int32") | |
| } | |
| ), | |
| supervised_keys=None, | |
| license=_LICENSE, | |
| homepage=_HOMEPAGE_URL, | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| train_urls = [_DOWNLOAD_URL.format(split="train", lang=lang) for lang in self.config.languages] | |
| dev_urls = [_DOWNLOAD_URL.format(split="validation", lang=lang) for lang in self.config.languages] | |
| test_urls = [_DOWNLOAD_URL.format(split="test", lang=lang) for lang in self.config.languages] | |
| train_paths = dl_manager.download_and_extract(train_urls) | |
| dev_paths = dl_manager.download_and_extract(dev_urls) | |
| test_paths = dl_manager.download_and_extract(test_urls) | |
| return [ | |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"file_paths": train_paths}), | |
| datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"file_paths": dev_paths}), | |
| datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"file_paths": test_paths}), | |
| ] | |
| def _generate_examples(self, file_paths): | |
| """Generate features given the directory path. | |
| Args: | |
| file_path: path where the tsv file is stored | |
| Yields: | |
| The features. | |
| """ | |
| for file_path in file_paths: | |
| with open(file_path, "r", encoding="utf-8") as csvfile: | |
| reader = csv.DictReader(csvfile) | |
| for i, row in enumerate(reader): | |
| yield i, row |