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import os |
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from pathlib import Path |
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from typing import Dict, List, Tuple |
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from seacrowd.utils.constants import Tasks |
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from seacrowd.utils import schemas |
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import datasets |
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import json |
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from seacrowd.utils.configs import SEACrowdConfig |
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_CITATION = """\ |
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@article{kurniawan2019, |
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title={KaWAT: A Word Analogy Task Dataset for Indonesian}, |
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url={http://arxiv.org/abs/1906.09912}, |
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journal={arXiv:1906.09912 [cs]}, |
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author={Kurniawan, Kemal}, |
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year={2019}, |
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month={Jun} |
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} |
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""" |
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_LANGUAGES = ["ind"] |
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_LOCAL = False |
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_DATASETNAME = "kawat" |
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_DESCRIPTION = """\ |
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We introduced KaWAT (Kata Word Analogy Task), a new word analogy task dataset for Indonesian. |
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We evaluated on it several existing pretrained Indonesian word embeddings and embeddings trained on Indonesian online news corpus. |
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We also tested them on two downstream tasks and found that pretrained word embeddings helped either by reducing the training epochs |
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or yielding significant performance gains. |
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""" |
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_HOMEPAGE = "https://github.com/kata-ai/kawat" |
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_LICENSE = "Creative Commons Attribution-ShareAlike 4.0" |
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_URLS = { |
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_DATASETNAME: "https://raw.githubusercontent.com/kata-ai/kawat/master/{}/{}", |
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} |
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_SUPPORTED_TASKS = [Tasks.WORD_ANALOGY] |
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_SOURCE_VERSION = "1.0.0" |
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_SEACROWD_VERSION = "2024.06.20" |
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_PATH_FILE = [ |
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{ |
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"folder": "semantic", |
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"file": [ |
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"antonyms.txt", |
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"country-capitals.txt", |
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"country-currencies.txt", |
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"gender-specific-words.txt", |
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"measure-words.txt", |
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"province-capitals.txt" |
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] |
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}, |
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{ |
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"folder": "syntax", |
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"file": [ |
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"nouns.txt", |
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"plurals.txt", |
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"reduplications.txt", |
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"verbs.txt" |
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] |
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} |
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] |
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class Kawat(datasets.GeneratorBasedBuilder): |
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SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) |
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SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) |
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BUILDER_CONFIGS = [ |
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SEACrowdConfig( |
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name="kawat_source", |
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version=SOURCE_VERSION, |
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description="Kawat source schema", |
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schema="source", |
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subset_id="kawat", |
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), |
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SEACrowdConfig( |
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name="kawat_seacrowd_t2t", |
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version=SEACROWD_VERSION, |
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description="Kawat Nusantara schema", |
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schema="seacrowd_t2t", |
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subset_id="kawat", |
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), |
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] |
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DEFAULT_CONFIG_NAME = "kawat_source" |
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def _info(self) -> datasets.DatasetInfo: |
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if self.config.schema == "source": |
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features = datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"text_1": datasets.Value("string"), |
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"text_1_name": datasets.Value("string"), |
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"text_2": datasets.Value("string"), |
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"text_2_name": datasets.Value("string"), |
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} |
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) |
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elif self.config.schema == "seacrowd_t2t": |
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features = schemas.text2text_features |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: |
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datas = [] |
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num = 0 |
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for each_path_file in _PATH_FILE: |
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for each_file in each_path_file["file"]: |
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data_dir = dl_manager.download_and_extract(_URLS[_DATASETNAME].format(each_path_file['folder'], each_file)) |
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parsed_lines = open(data_dir, "r").readlines() |
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titles = parsed_lines[0].split("\t") |
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num_columns = len(titles) |
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titles[num_columns-1] = titles[num_columns-1][:-1] |
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for i in range(1, len(parsed_lines)): |
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words = parsed_lines[i].split("\t") |
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words[num_columns-1] = words[num_columns-1][:-1] |
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for j in range(1, num_columns): |
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if words[j] != "-": |
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datas.append({ |
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"id": str(num), |
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"text_1": words[0], |
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"text_1_name": titles[0], |
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"text_2": words[j], |
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"text_2_name": titles[j], |
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}) |
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num+=1 |
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with open(data_dir, 'w') as f: |
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f.write(json.dumps(datas)) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"filepath": data_dir, |
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"split": "train", |
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}, |
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), |
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] |
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def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]: |
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data = json.load(open(filepath, "r")) |
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if self.config.schema == "source": |
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key = 0 |
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for each_data in data: |
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example = { |
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"id": each_data["id"], |
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"text_1": each_data["text_1"], |
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"text_1_name": each_data["text_1_name"], |
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"text_2": each_data["text_2"], |
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"text_2_name": each_data["text_2_name"], |
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} |
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yield key, example |
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key+=1 |
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elif self.config.schema == "seacrowd_t2t": |
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key = 0 |
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for each_data in data: |
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example = { |
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"id": each_data["id"], |
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"text_1": each_data["text_1"], |
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"text_1_name": each_data["text_1_name"], |
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"text_2": each_data["text_2"], |
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"text_2_name": each_data["text_2_name"], |
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} |
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yield key, example |
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key+=1 |
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