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# 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")
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