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Upload indqner.py with huggingface_hub
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indqner.py
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# coding=utf-8
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# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" IndQNER Dataset """
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from pathlib import Path
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from typing import List
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import datasets
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from nusacrowd.utils import schemas
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from nusacrowd.utils.common_parser import load_conll_data
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from nusacrowd.utils.configs import NusantaraConfig
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from nusacrowd.utils.constants import Tasks
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_CITATION = """\
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@misc{,
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author = {Ria Hari Gusmita, Asep Fajar Firmansyah, Khodijah Khuliyah},
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title = {{IndQNER: a NER Benchmark Dataset on Indonesian Translation of Quran}},
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url = {https://github.com/dice-group/IndQNER},
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year = {2022}
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}
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"""
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_LOCAL = False
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_LANGUAGES = ["ind"]
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_DATASETNAME = "IndQNER"
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_DESCRIPTION = """\
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IndQNER is a NER dataset created by manually annotating the Indonesian translation of Quran text.
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The dataset contains 18 named entity categories as follow:
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"Allah": Allah (including synonim of Allah such as Yang maha mengetahui lagi mahabijaksana)
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"Throne": Throne of Allah (such as 'Arasy)
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"Artifact": Artifact (such as Ka'bah, Baitullah)
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"AstronomicalBody": Astronomical body (such as bumi, matahari)
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"Event": Event (such as hari akhir, kiamat)
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"HolyBook": Holy book (such as AlQur'an)
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"Language": Language (such as bahasa Arab
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"Angel": Angel (such as Jibril, Mikail)
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"Person": Person (such as Bani Israil, Fir'aun)
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"Messenger": Messenger (such as Isa, Muhammad, Musa)
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"Prophet": Prophet (such as Adam, Sulaiman)
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"AfterlifeLocation": Afterlife location (such as Jahanam, Jahim, Padang Mahsyar)
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"GeographicalLocation": Geographical location (such as Sinai, negeru Babilonia)
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"Color": Color (such as kuning tua)
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"Religion": Religion (such as Islam, Yahudi, Nasrani)
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"Food": Food (such as manna, salwa)
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"""
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_HOMEPAGE = "https://github.com/dice-group/IndQNER"
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_LICENSE = "Unknown"
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_URLs = {
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"train": "https://raw.githubusercontent.com/dice-group/IndQNER/master/datasets/train.txt",
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"validation": "https://raw.githubusercontent.com/dice-group/IndQNER/master/datasets/dev.txt",
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"test": "https://raw.githubusercontent.com/dice-group/IndQNER/master/datasets/test.txt",
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}
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_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION]
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_SOURCE_VERSION = "1.0.0"
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_NUSANTARA_VERSION = "1.0.0"
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class IndqnerDataset(datasets.GeneratorBasedBuilder):
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"""IndQNER is an Named Entity Recognition benchmark dataset on a niche domain i.e. Indonesian Translation of Quran."""
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label_classes = [
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"B-Allah",
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"B-Throne",
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"B-Artifact",
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"B-AstronomicalBody",
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"B-Event",
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"B-HolyBook",
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"B-Language",
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"B-Angel",
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"B-Person",
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"B-Messenger",
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"B-Prophet",
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"B-AfterlifeLocation",
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"B-GeographicalLocation",
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"B-Color",
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"B-Religion",
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"B-Food",
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"I-Allah",
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"I-Throne",
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"I-Artifact",
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"I-AstronomicalBody",
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"I-Event",
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"I-HolyBook",
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"I-Language",
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"I-Angel",
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"I-Person",
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"I-Messenger",
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"I-Prophet",
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"I-AfterlifeLocation",
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"I-GeographicalLocation",
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"I-Color",
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"I-Religion",
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"I-Food",
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"O",
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]
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BUILDER_CONFIGS = [
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NusantaraConfig(
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name="indqner_source",
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version=datasets.Version(_SOURCE_VERSION),
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description="NER dataset from Indonesian translation Quran source schema",
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schema="source",
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subset_id="indqner",
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),
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NusantaraConfig(
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name="indqner_nusantara_seq_label",
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version=datasets.Version(_SOURCE_VERSION),
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description="NER dataset from Indonesian translation Quran Nusantara schema",
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schema="nusantara_seq_label",
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subset_id="indqner",
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),
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]
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DEFAULT_CONFIG_NAME = "indqner_source"
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def _info(self):
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if self.config.schema == "source":
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features = datasets.Features({"index": datasets.Value("string"), "tokens": [datasets.Value("string")], "ner_tag": [datasets.Value("string")]})
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elif self.config.schema == "nusantara_seq_label":
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features = schemas.seq_label_features(self.label_classes)
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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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train_tsv_path = Path(dl_manager.download_and_extract(_URLs["train"]))
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validation_tsv_path = Path(dl_manager.download_and_extract(_URLs["validation"]))
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test_tsv_path = Path(dl_manager.download_and_extract(_URLs["test"]))
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data_files = {
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"train": train_tsv_path,
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"validation": validation_tsv_path,
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"test": test_tsv_path,
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}
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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={"filepath": data_files["train"]},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={"filepath": data_files["validation"]},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={"filepath": data_files["test"]},
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),
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]
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def _generate_examples(self, filepath: Path):
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conll_dataset = load_conll_data(filepath)
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if self.config.schema == "source":
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for index, row in enumerate(conll_dataset):
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ex = {"index": str(index), "tokens": row["sentence"], "ner_tag": row["label"]}
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yield index, ex
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elif self.config.schema == "nusantara_seq_label":
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for index, row in enumerate(conll_dataset):
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ex = {"id": str(index), "tokens": row["sentence"], "labels": row["label"]}
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yield index, ex
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else:
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raise ValueError(f"Invalid config: {self.config.name}")
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