# 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. from itertools import chain, repeat from pathlib import Path from typing import Dict, List, Tuple import datasets from seacrowd.utils import schemas from seacrowd.utils.common_parser import load_ud_data, load_ud_data_as_seacrowd_kb from seacrowd.utils.configs import SEACrowdConfig from seacrowd.utils.constants import Tasks _CITATION = """\ @conference{2f8c7438a7f44f6b85b773586cff54e8, title = "A gold standard dependency treebank for Indonesian", author = "Ika Alfina and Arawinda Dinakaramani and Fanany, {Mohamad Ivan} and Heru Suhartanto", note = "Publisher Copyright: {\textcopyright} 2019 Proceedings of the 33rd Pacific Asia Conference on Language, Information and Computation, PACLIC 2019. All rights reserved.; \ 33rd Pacific Asia Conference on Language, Information and Computation, PACLIC 2019 ; Conference date: 13-09-2019 Through 15-09-2019", year = "2019", month = jan, day = "1", language = "English", pages = "1--9", } @article{DBLP:journals/corr/abs-2011-00677, author = {Fajri Koto and Afshin Rahimi and Jey Han Lau and Timothy Baldwin}, title = {IndoLEM and IndoBERT: {A} Benchmark Dataset and Pre-trained Language Model for Indonesian {NLP}}, journal = {CoRR}, volume = {abs/2011.00677}, year = {2020}, url = {https://arxiv.org/abs/2011.00677}, eprinttype = {arXiv}, eprint = {2011.00677}, timestamp = {Fri, 06 Nov 2020 15:32:47 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-2011-00677.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } """ _LANGUAGES = ["ind"] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data) _LOCAL = False _DATASETNAME = "indolem_ud_id_pud" _DESCRIPTION = """\ 1 of 8 sub-datasets of IndoLEM, a comprehensive dataset encompassing 7 NLP tasks (Koto et al., 2020). This dataset is part of [Parallel Universal Dependencies (PUD)](http://universaldependencies.org/conll17/) project. This is based on the first corrected version by Alfina et al. (2019), contains 1,000 sentences. """ _HOMEPAGE = "https://indolem.github.io/" _LICENSE = "Creative Commons Attribution 4.0" _FOLDS = list(range(5)) _DEFAULT_FOLD = _FOLDS[0] _URLS = { f"{_DATASETNAME}_{fold}": { "train": f"https://raw.githubusercontent.com/indolem/indolem/main/dependency_parsing/UD_Indonesian_PUD/folds/train{fold}.conllu", "validation": f"https://raw.githubusercontent.com/indolem/indolem/main/dependency_parsing/UD_Indonesian_PUD/folds/dev{fold}.conllu", "test": f"https://raw.githubusercontent.com/indolem/indolem/main/dependency_parsing/UD_Indonesian_PUD/folds/test{fold}.conllu", } for fold in _FOLDS } _SUPPORTED_TASKS = [Tasks.DEPENDENCY_PARSING] _SOURCE_VERSION = "1.0.0" _SEACROWD_VERSION = "2024.06.20" class IndolemUDIDPUDDataset(datasets.GeneratorBasedBuilder): SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) BUILDER_CONFIGS = list( chain( ( SEACrowdConfig( name=f"{_DATASETNAME}_source", version=SOURCE_VERSION, description=f"{_DATASETNAME} default fold ('{_DEFAULT_FOLD}') of source schema", schema="source", subset_id=f"{_DATASETNAME}_{_DEFAULT_FOLD}", ), SEACrowdConfig( name=f"{_DATASETNAME}_seacrowd_kb", version=SEACROWD_VERSION, description=f"{_DATASETNAME} default fold ('{_DEFAULT_FOLD}') of Nusantara KB schema", schema="seacrowd_kb", subset_id=f"{_DATASETNAME}_{_DEFAULT_FOLD}", ), ), *( ( SEACrowdConfig( name=f"{_DATASETNAME}_{fold}_source", version=ver_src, description=f"{_DATASETNAME} fold '{fold}' of source schema", schema="source", subset_id=f"{_DATASETNAME}_{fold}", ), SEACrowdConfig( name=f"{_DATASETNAME}_{fold}_seacrowd_kb", version=ver_nusa, description=f"{_DATASETNAME} fold '{fold}' of Nusantara KB schema", schema="seacrowd_kb", subset_id=f"{_DATASETNAME}_{fold}", ), ) for fold, ver_src, ver_nusa in zip(_FOLDS, repeat(SOURCE_VERSION), repeat(SEACROWD_VERSION)) ), ) ) DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source" def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source": features = datasets.Features( { # metadata "sent_id": datasets.Value("string"), "text": datasets.Value("string"), "text_en": datasets.Value("string"), # tokens "id": [datasets.Value("string")], "form": [datasets.Value("string")], "lemma": [datasets.Value("string")], "upos": [datasets.Value("string")], # Alternatively, use ClassLabel (https://huggingface.co/datasets/universal_dependencies/blob/main/universal_dependencies.py#L1211) "xpos": [datasets.Value("string")], "feats": [datasets.Value("string")], "head": [datasets.Value("string")], "deprel": [datasets.Value("string")], "deps": [datasets.Value("string")], "misc": [datasets.Value("string")], } ) elif self.config.schema == "seacrowd_kb": features = schemas.kb_features else: raise NotImplementedError(f"Schema '{self.config.schema}' is not defined.") 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.""" urls = _URLS[self.config.subset_id] data_dir = dl_manager.download(urls) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": data_dir["train"], }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "filepath": data_dir["test"], }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "filepath": data_dir["validation"], }, ), ] def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]: """Yields examples as (key, example) tuples.""" # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` try: generator_fn = { "source": load_ud_data, "seacrowd_kb": load_ud_data_as_seacrowd_kb, }[self.config.schema] except KeyError: raise NotImplementedError(f"Schema '{self.config.schema}' is not defined.") for key, example in enumerate(generator_fn(filepath)): yield key, example # if __name__ == "__main__": # datasets.load_dataset(__file__, name=f"indolem_ud_id_pud_source") # datasets.load_dataset(__file__, name=f"indolem_ud_id_pud_seacrowd_kb") # for fold in _FOLDS: # datasets.load_dataset(__file__, name=f"indolem_ud_id_pud_{fold}_source") # datasets.load_dataset(__file__, name=f"indolem_ud_id_pud_{fold}_seacrowd_kb")