import os from pathlib import Path from typing import Dict, List, Tuple import datasets from seacrowd.utils.configs import SEACrowdConfig from seacrowd.utils.constants import Tasks from seacrowd.utils import schemas import pandas as pd _CITATION = """\ @inproceedings{wongso2021causal, title={Causal and masked language modeling of Javanese language using transformer-based architectures}, author={Wongso, Wilson and Setiawan, David Samuel and Suhartono, Derwin}, booktitle={2021 International Conference on Advanced Computer Science and Information Systems (ICACSIS)}, pages={1--7}, year={2021}, organization={IEEE} } """ _DATASETNAME = "imdb_jv" _DESCRIPTION = """\ Javanese Imdb Movie Reviews Dataset is a Javanese version of the IMDb Movie Reviews dataset by translating the original English dataset to Javanese. """ _HOMEPAGE = "https://huggingface.co/datasets/w11wo/imdb-javanese" _LANGUAGES = ["ind"] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data) _LOCAL = False _LICENSE = "Unknown" _URLS = { _DATASETNAME: "https://huggingface.co/datasets/w11wo/imdb-javanese/resolve/main/javanese_imdb_csv.zip", } _SUPPORTED_TASKS = [Tasks.SENTIMENT_ANALYSIS] _SOURCE_VERSION = "1.0.0" _SEACROWD_VERSION = "2024.06.20" class IMDbJv(datasets.GeneratorBasedBuilder): """Javanese Imdb Movie Reviews Dataset is a Javanese version of the IMDb Movie Reviews dataset by translating the original English dataset to Javanese.""" SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) BUILDER_CONFIGS = [ SEACrowdConfig( name="imdb_jv_source", version=datasets.Version(_SOURCE_VERSION), description="imdb_jv source schema", schema="source", subset_id="imdb_jv", ), SEACrowdConfig( name="imdb_jv_seacrowd_text", version=datasets.Version(_SEACROWD_VERSION), description="imdb_jv Nusantara schema", schema="seacrowd_text", subset_id="imdb_jv", ), ] DEFAULT_CONFIG_NAME = "imdb_jv_source" def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source": features = datasets.Features( { "id": datasets.Value("string"), "text": datasets.Value("string"), "label": datasets.Value("string") } ) elif self.config.schema == "seacrowd_text": features = schemas.text_features(['1', '0', '-1']) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: data_dir = Path(dl_manager.download_and_extract(_URLS[_DATASETNAME])) data_files = { "train": "javanese_imdb_train.csv", "unsupervised": "javanese_imdb_unsup.csv", "test": "javanese_imdb_test.csv", } return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": os.path.join(data_dir, data_files["train"]), "split": "train", }, ), datasets.SplitGenerator( name="unsupervised", gen_kwargs={ "filepath": os.path.join(data_dir, data_files["unsupervised"]), "split": "unsupervised", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "filepath": os.path.join(data_dir, data_files["test"]), "split": "test", }, ), ] def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]: if self.config.schema == "source": data = pd.read_csv(filepath) length = len(data['label']) for id in range(length): ex = { "id": str(id), "text": data['text'][id], "label": data['label'][id], } yield id, ex elif self.config.schema == "seacrowd_text": data = pd.read_csv(filepath) length = len(data['label']) for id in range(length): ex = { "id": str(id), "text": data['text'][id], "label": data['label'][id], } yield id, ex else: raise ValueError(f"Invalid config: {self.config.name}")