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""" |
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https://github.com/ir-nlp-csui/indoler/tree/main |
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The dataset contains 993 annotated court decission document. |
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The document was taken from Decision of the Supreme Court of Indonesia. |
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The documents have also been tokenized and cleaned |
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""" |
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import os |
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import json |
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from pathlib import Path |
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from typing import Dict, List, Tuple |
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import datasets |
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from seacrowd.utils import schemas |
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from seacrowd.utils.configs import SEACrowdConfig |
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from seacrowd.utils.constants import Tasks, Licenses |
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_CITATION = """\ |
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@INPROCEEDINGS{9263157, |
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author={Nuranti, Eka Qadri and Yulianti, Evi}, |
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booktitle={2020 International Conference on Advanced Computer Science and Information Systems (ICACSIS)}, |
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title={Legal Entity Recognition in Indonesian Court Decision Documents Using Bi-LSTM and CRF Approaches}, |
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year={2020}, |
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volume={}, |
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number={}, |
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pages={429-434}, |
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keywords={Xenon;6G mobile communication;legal processing;legal entity recognition;legal document;name entity recognition;ner;bi-lstm;lstm;crf}, |
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doi={10.1109/ICACSIS51025.2020.9263157}} |
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""" |
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_DATASETNAME = "indoler" |
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_DESCRIPTION = """\ |
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https://github.com/ir-nlp-csui/indoler/tree/main |
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The data can be used for NER Task in legal documents. |
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The dataset contains 993 annotated court decission document. |
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The document was taken from Decision of the Supreme Court of Indonesia. |
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The documents have also been tokenized and cleaned |
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""" |
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_HOMEPAGE = "https://github.com/ir-nlp-csui/indoler/tree/main" |
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_LANGUAGES = ['ind'] |
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_LICENSE = Licenses.UNKNOWN.value |
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_LOCAL = False |
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_URLS = { |
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_DATASETNAME: { |
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"test_idx": "https://raw.githubusercontent.com/ir-nlp-csui/indoler/main/test.ids.csv", |
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"train_idx": "https://raw.githubusercontent.com/ir-nlp-csui/indoler/main/train.ids.csv", |
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"valid_idx": "https://raw.githubusercontent.com/ir-nlp-csui/indoler/main/val.ids.csv", |
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"full_data": "https://raw.githubusercontent.com/ir-nlp-csui/indoler/main/data.json" |
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}, |
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} |
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_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION] |
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_SOURCE_VERSION = "2.0.0" |
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_SEACROWD_VERSION = "2024.06.20" |
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class IndoLer(datasets.GeneratorBasedBuilder): |
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"""https://github.com/ir-nlp-csui/indoler/tree/main |
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The data can be used for NER Task in legal documents |
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The dataset contains 993 annotated court decission document. |
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The document was taken from Decision of the Supreme Court of Indonesia. |
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The documents have also been tokenized and cleaned""" |
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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="indoler_source", |
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version=SOURCE_VERSION, |
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description="indoler source schema", |
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schema="source", |
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subset_id="indoler", |
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), |
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SEACrowdConfig( |
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name="indoler_seacrowd_seq_label", |
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version=SEACROWD_VERSION, |
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description="indoler SEACrowd schema", |
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schema="seacrowd_seq_label", |
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subset_id="indoler", |
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), |
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] |
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DEFAULT_CONFIG_NAME = "indoler_source" |
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def _info(self) -> datasets.DatasetInfo: |
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NAMED_ENTITIES = ['O', 'B-Jenis Amar', 'B-Jenis Dakwaan', 'B-Jenis Perkara', 'B-Melanggar UU (Dakwaan)', |
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'B-Melanggar UU (Pertimbangan Hukum)', 'B-Melanggar UU (Tuntutan)', 'B-Nama Hakim Anggota', 'B-Nama Hakim Ketua', |
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'B-Nama Jaksa', 'B-Nama Panitera', 'B-Nama Pengacara', 'B-Nama Pengadilan', |
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'B-Nama Saksi', 'B-Nama Terdakwa', 'B-Nomor Putusan', 'B-Putusan Hukuman', |
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'B-Tanggal Kejadian', 'B-Tanggal Putusan', 'B-Tingkat Kasus', 'B-Tuntutan Hukuman', |
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'I-Jenis Amar', 'I-Jenis Dakwaan', 'I-Jenis Perkara', 'I-Melanggar UU (Dakwaan)', |
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'I-Melanggar UU (Pertimbangan Hukum)', 'I-Melanggar UU (Tuntutan)', 'I-Nama Hakim Anggota', 'I-Nama Hakim Ketua', |
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'I-Nama Jaksa', 'I-Nama Panitera', 'I-Nama Pengacara', 'I-Nama Pengadilan', |
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'I-Nama Saksi', 'I-Nama Terdakwa', 'I-Nomor Putusan', 'I-Putusan Hukuman', |
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'I-Tanggal Kejadian', 'I-Tanggal Putusan', 'I-Tingkat Kasus', 'I-Tuntutan Hukuman'] |
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if self.config.schema == "source": |
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features = datasets.Features({ |
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"id": datasets.Value("string"), |
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"owner": datasets.Value("string"), |
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"lawyer": datasets.ClassLabel(names=[False, True]), |
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"verdict": datasets.ClassLabel(names=["guilty", "bebas", "lepas"]), |
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"indictment": datasets.ClassLabel(names=["NA", "tunggal", "subsider", "komul", "alternatif", "kombinasi", "gabungan"]), |
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"text-tags": datasets.Sequence(datasets.ClassLabel(names=NAMED_ENTITIES)), |
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"text": datasets.Sequence(datasets.Value("string")), |
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}) |
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elif self.config.schema == "seacrowd_seq_label": |
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features = schemas.seq_label.features(NAMED_ENTITIES) |
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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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"""Returns SplitGenerators.""" |
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urls = _URLS[_DATASETNAME] |
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test_path = dl_manager.download_and_extract(urls['test_idx']) |
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train_path = dl_manager.download_and_extract(urls['train_idx']) |
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valid_path = dl_manager.download_and_extract(urls['valid_idx']) |
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data_path = dl_manager.download_and_extract(urls['full_data']) |
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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_path, |
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"idx_path": train_path, |
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"split": "train", |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"filepath": data_path, |
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"idx_path": test_path, |
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"split": "test", |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"filepath": data_path, |
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"idx_path": valid_path, |
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"split": "validation", |
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}, |
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), |
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] |
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def _generate_examples(self, filepath: Path, idx_path: Path, split: str) -> Tuple[int, Dict]: |
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"""Yields examples as (key, example) tuples.""" |
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split_idxs = [] |
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with open(idx_path, 'r', encoding="utf-8") as indexes: |
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for index in indexes.readlines(): |
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split_idxs.append(int(index)) |
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with open(filepath, 'r', encoding="utf-8") as file: |
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contents = json.load(file) |
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counter = 0 |
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for content in contents: |
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if int(content['id']) in split_idxs: |
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if self.config.schema == "source": |
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if content['indictment'] not in ["NA", "tunggal", "subsider", "komul", "alternatif", "kombinasi", "gabungan"]: |
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content['indictment'] = "NA" |
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yield( |
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counter, |
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{ |
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"id" : content['id'], |
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"owner" : content['owner'], |
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"lawyer" : content['lawyer'], |
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"verdict" : content['verdict'], |
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"indictment": content['indictment'], |
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"text-tags" : content['text-tags'], |
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"text" : content['text'], |
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} |
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) |
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counter += 1 |
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elif self.config.schema == "seacrowd_seq_label": |
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yield( |
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counter, |
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{ |
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"id": content['id'], |
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"tokens": content['text'], |
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"labels": content['text-tags'], |
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} |
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) |
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counter += 1 |
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