diff --git a/seacrowd/sea_datasets/indonglish/__init__.py b/seacrowd/sea_datasets/indonglish/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/seacrowd/sea_datasets/indonglish/indonglish.py b/seacrowd/sea_datasets/indonglish/indonglish.py new file mode 100644 index 000000000..a6707be07 --- /dev/null +++ b/seacrowd/sea_datasets/indonglish/indonglish.py @@ -0,0 +1,216 @@ +# coding=utf-8 +# Copyright 2022 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 csv +from pathlib import Path +from typing import Dict, List, Tuple + +import datasets + +from seacrowd.utils import schemas +from seacrowd.utils.configs import SEACrowdConfig +from seacrowd.utils.constants import Licenses, Tasks + +_CITATION = """\ +@article{Astuti2023, +title = {Code-Mixed Sentiment Analysis using Transformer for Twitter Social Media Data}, +journal = {International Journal of Advanced Computer Science and Applications}, +doi = {10.14569/IJACSA.2023.0141053}, +url = {http://dx.doi.org/10.14569/IJACSA.2023.0141053}, +year = {2023}, +publisher = {The Science and Information Organization}, +volume = {14}, +number = {10}, +author = {Laksmita Widya Astuti and Yunita Sari and Suprapto} +} +""" + +_DATASETNAME = "indonglish" +_DESCRIPTION = """\ +Indonglish-dataset was constructed based on keywords derived from the +sociolinguistic phenomenon observed among teenagers in South Jakarta. The +dataset was designed to tackle the semantic task of sentiment analysis, +incorporating three distinct label categories: positive, negative, and +neutral. The annotation of the dataset was carried out by a panel of five +annotators, each possessing expertise language and data science. +""" + +_HOMEPAGE = "https://github.com/laksmitawidya/indonglish-dataset" +_LANGUAGES = ["ind"] +_LICENSE = Licenses.UNKNOWN.value +_LOCAL = False + +_URLS = { + "skenario-orig": { + "train": "https://raw.githubusercontent.com/laksmitawidya/indonglish-dataset/master/skenario-ori/train.csv", + "validation": "https://raw.githubusercontent.com/laksmitawidya/indonglish-dataset/master/skenario-ori/validation.csv", + "test": "https://raw.githubusercontent.com/laksmitawidya/indonglish-dataset/master/skenario-ori/test.csv", + }, + "skenario1": { + "train": "https://raw.githubusercontent.com/laksmitawidya/indonglish-dataset/master/skenario1/training.csv", + "validation": "https://raw.githubusercontent.com/laksmitawidya/indonglish-dataset/master/skenario1/validation.csv", + "test": "https://raw.githubusercontent.com/laksmitawidya/indonglish-dataset/master/skenario1/test.csv", + }, + "skenario2": { + "train": "https://raw.githubusercontent.com/laksmitawidya/indonglish-dataset/master/skenario2/training.csv", + "validation": "https://raw.githubusercontent.com/laksmitawidya/indonglish-dataset/master/skenario2/validation.csv", + "test": "https://raw.githubusercontent.com/laksmitawidya/indonglish-dataset/master/skenario2/test.csv", + }, + "skenario3": { + "train": "https://raw.githubusercontent.com/laksmitawidya/indonglish-dataset/master/skenario3/training.csv", + "validation": "https://raw.githubusercontent.com/laksmitawidya/indonglish-dataset/master/skenario3/validation.csv", + "test": "https://raw.githubusercontent.com/laksmitawidya/indonglish-dataset/master/skenario3/test.csv", + }, +} + +_SUPPORTED_TASKS = [Tasks.SENTIMENT_ANALYSIS] + +_SOURCE_VERSION = "1.0.0" +_SEACROWD_VERSION = "1.0.0" + + +class Indonglish(datasets.GeneratorBasedBuilder): + """Indonglish dataset for sentiment analysis from https://github.com/laksmitawidya/indonglish-dataset.""" + + SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) + SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) + + SEACROWD_SCHEMA_NAME = "text" + _LABELS = ["Positif", "Negatif", "Netral"] + + BUILDER_CONFIGS = [ + SEACrowdConfig( + name=f"{_DATASETNAME}_source", + version=SOURCE_VERSION, + description=f"{_DATASETNAME} source schema", + schema="source", + subset_id=_DATASETNAME, + ), + SEACrowdConfig( + name=f"{_DATASETNAME}_seacrowd_{SEACROWD_SCHEMA_NAME}", + version=SEACROWD_VERSION, + description=f"{_DATASETNAME} SEACrowd schema", + schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}", + subset_id=_DATASETNAME, + ), + ] + for i in range(1, 4): + BUILDER_CONFIGS += [ + SEACrowdConfig( + name=f"{_DATASETNAME}_skenario{i}_source", + version=SOURCE_VERSION, + description=f"{_DATASETNAME} source schema", + schema="source", + subset_id=f"{_DATASETNAME}_skenario{i}", + ), + SEACrowdConfig( + name=f"{_DATASETNAME}_skenario{i}_seacrowd_{SEACROWD_SCHEMA_NAME}", + version=SEACROWD_VERSION, + description=f"{_DATASETNAME} SEACrowd schema", + schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}", + subset_id=f"{_DATASETNAME}_skenario{i}", + ), + ] + + DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source" + + def _info(self) -> datasets.DatasetInfo: + + if self.config.schema == "source": + features = datasets.Features( + { + "id": datasets.Value("string"), + "tweet": datasets.Value("string"), + "label": datasets.ClassLabel(names=self._LABELS), + } + ) + + elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}": + features = schemas.text_features(self._LABELS) + + return datasets.DatasetInfo( + description=_DESCRIPTION, + features=features, + homepage=_HOMEPAGE, + license=_LICENSE, + citation=_CITATION, + ) + + def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: + """Returns SplitGenerators.""" + + if "skenario" in self.config.name: + setting = self.config.name.split("_")[1] + else: + setting = "skenario-orig" + + data_paths = { + setting: { + "train": Path(dl_manager.download_and_extract(_URLS[setting]["train"])), + "validation": Path(dl_manager.download_and_extract(_URLS[setting]["validation"])), + "test": Path(dl_manager.download_and_extract(_URLS[setting]["test"])), + } + } + + return [ + datasets.SplitGenerator( + name=datasets.Split.TRAIN, + gen_kwargs={ + "filepath": data_paths[setting]["train"], + "split": "train", + }, + ), + datasets.SplitGenerator( + name=datasets.Split.TEST, + gen_kwargs={ + "filepath": data_paths[setting]["test"], + "split": "test", + }, + ), + datasets.SplitGenerator( + name=datasets.Split.VALIDATION, + gen_kwargs={ + "filepath": data_paths[setting]["validation"], + "split": "dev", + }, + ), + ] + + def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]: + """Yields examples as (key, example) tuples.""" + + # read csv file + with open(filepath, "r", encoding="utf-8") as csv_file: + csv_reader = csv.reader(csv_file) + csv_data = [row for row in csv_reader] + csv_data = csv_data[1:] # remove header + + num_sample = len(csv_data) + + for i in range(num_sample): + if self.config.schema == "source": + example = { + "id": str(i), + "tweet": csv_data[i][0], + "label": csv_data[i][1], + } + elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}": + example = { + "id": str(i), + "text": csv_data[i][0], + "label": csv_data[i][1], + } + + yield i, example