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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.
""" NERGrit Dataset """
from pathlib import Path
from typing import List
import datasets
from nusacrowd.utils import schemas
from nusacrowd.utils.common_parser import load_conll_data
from nusacrowd.utils.configs import NusantaraConfig
from nusacrowd.utils.constants import Tasks
_CITATION = """\
@misc{Fahmi_NERGRIT_CORPUS_2019,
author = {Fahmi, Husni and Wibisono, Yudi and Kusumawati, Riyanti},
title = {{NERGRIT CORPUS}},
url = {https://github.com/grit-id/nergrit-corpus},
year = {2019}
}
"""
_LOCAL = False
_LANGUAGES = ["ind"] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
_DATASETNAME = "nergrit"
_DESCRIPTION = """\
Nergrit Corpus is a dataset collection of Indonesian Named Entity Recognition (NER), Statement Extraction,
and Sentiment Analysis developed by PT Gria Inovasi Teknologi (GRIT).
The Named Entity Recognition contains 18 entities as follow:
'CRD': Cardinal
'DAT': Date
'EVT': Event
'FAC': Facility
'GPE': Geopolitical Entity
'LAW': Law Entity (such as Undang-Undang)
'LOC': Location
'MON': Money
'NOR': Political Organization
'ORD': Ordinal
'ORG': Organization
'PER': Person
'PRC': Percent
'PRD': Product
'QTY': Quantity
'REG': Religion
'TIM': Time
'WOA': Work of Art
'LAN': Language
"""
_HOMEPAGE = "https://github.com/grit-id/nergrit-corpus"
_LICENSE = "MIT"
_URL = "https://github.com/cahya-wirawan/indonesian-language-models/raw/master/data/nergrit-corpus_20190726_corrected.tgz"
_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION]
_SOURCE_VERSION = "1.0.0"
_NUSANTARA_VERSION = "1.0.0"
class NergritDataset(datasets.GeneratorBasedBuilder):
"""Indonesian Named Entity Recognition from https://github.com/grit-id/nergrit-corpus."""
label_classes = {
"ner": [
"B-CRD",
"B-DAT",
"B-EVT",
"B-FAC",
"B-GPE",
"B-LAN",
"B-LAW",
"B-LOC",
"B-MON",
"B-NOR",
"B-ORD",
"B-ORG",
"B-PER",
"B-PRC",
"B-PRD",
"B-QTY",
"B-REG",
"B-TIM",
"B-WOA",
"I-CRD",
"I-DAT",
"I-EVT",
"I-FAC",
"I-GPE",
"I-LAN",
"I-LAW",
"I-LOC",
"I-MON",
"I-NOR",
"I-ORD",
"I-ORG",
"I-PER",
"I-PRC",
"I-PRD",
"I-QTY",
"I-REG",
"I-TIM",
"I-WOA",
"O",
],
"sentiment": ["B-POS", "B-NEG", "B-NET", "I-POS", "I-NEG", "I-NET", "O"],
"statement": ["B-BREL", "B-FREL", "B-STAT", "B-WHO", "I-BREL", "I-FREL", "I-STAT", "I-WHO", "O"],
}
BUILDER_CONFIGS = [
NusantaraConfig(
name=f"nergrit_{task}_source",
version=datasets.Version(_SOURCE_VERSION),
description="NERGrit source schema",
schema="source",
subset_id=f"nergrit_{task}",
)
for task in label_classes
]
BUILDER_CONFIGS += [
NusantaraConfig(
name=f"nergrit_{task}_nusantara_seq_label",
version=datasets.Version(_SOURCE_VERSION),
description="NERGrit Nusantara schema",
schema="nusantara_seq_label",
subset_id=f"nergrit_{task}",
)
for task in label_classes
]
DEFAULT_CONFIG_NAME = "nergrit_ner_source"
def _info(self):
features = None
task = self.config.subset_id.split("_")[-1]
if self.config.schema == "source":
features = datasets.Features({"index": datasets.Value("string"), "tokens": [datasets.Value("string")], "ner_tag": [datasets.Value("string")]})
elif self.config.schema == "nusantara_seq_label":
features = schemas.seq_label_features(self.label_classes[task])
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
task = self.config.subset_id.split("_")[-1]
archive = Path(dl_manager.download_and_extract(_URL))
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": archive / f"nergrit-corpus/{task}/data/train_corrected.txt"},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": archive / f"nergrit-corpus/{task}/data/test_corrected.txt"},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"filepath": archive / f"nergrit-corpus/{task}/data/valid_corrected.txt"},
),
]
def _generate_examples(self, filepath: Path):
conll_dataset = load_conll_data(filepath)
if self.config.schema == "source":
for index, row in enumerate(conll_dataset):
ex = {"index": str(index), "tokens": row["sentence"], "ner_tag": row["label"]}
yield index, ex
elif self.config.schema == "nusantara_seq_label":
for index, row in enumerate(conll_dataset):
ex = {"id": str(index), "tokens": row["sentence"], "labels": row["label"]}
yield index, ex
else:
raise ValueError(f"Invalid config: {self.config.name}")
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