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from pathlib import Path |
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from typing import List |
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import re |
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import datasets |
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import pandas as pd |
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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, DEFAULT_SOURCE_VIEW_NAME, DEFAULT_SEACROWD_VIEW_NAME |
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_DATASETNAME = "korpus_nusantara" |
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_SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME |
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_UNIFIED_VIEW_NAME = DEFAULT_SEACROWD_VIEW_NAME |
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_LANGUAGES = ["ind", "jav", "xdy", "bug", "sun", "mad", "bjn", "bbc", "khek", "msa", "min", "tiociu"] |
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_LOCAL = False |
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_CITATION = """\ |
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@article{sujaini2020improving, |
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title={Improving the role of language model in statistical machine translation (Indonesian-Javanese)}, |
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author={Sujaini, Herry}, |
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journal={International Journal of Electrical and Computer Engineering}, |
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volume={10}, |
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number={2}, |
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pages={2102}, |
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year={2020}, |
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publisher={IAES Institute of Advanced Engineering and Science} |
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} |
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""" |
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_DESCRIPTION = """\ |
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This parallel corpus was collected from several studies, assignments, and thesis of |
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students of the Informatics Study Program, Tanjungpura University. Some of the corpus |
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are used in the translation machine from Indonesian to local languages http://nustor.untan.ac.id/cammane/. |
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This corpus can be used freely for research purposes by citing the paper |
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https://ijece.iaescore.com/index.php/IJECE/article/download/20046/13738. |
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The dataset is a combination of multiple machine translation works from the author, |
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Herry Sujaini, covering Indonesian to 25 local dialects in Indonesia. Since not all |
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dialects have ISO639-3 standard coding, as agreed with Pak Herry , we decided to |
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group the dataset into the closest language family, i.e.: Javanese, Dayak, Buginese, |
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Sundanese, Madurese, Banjar, Batak Toba, Khek, Malay, Minangkabau, and Tiociu. |
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""" |
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_HOMEPAGE = "https://github.com/herrysujaini/korpusnusantara" |
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_LICENSE = "Unknown" |
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_URLS = { |
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_DATASETNAME: "https://github.com/herrysujaini/korpusnusantara/raw/main/korpus nusantara.xlsx", |
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} |
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_SUPPORTED_TASKS = [Tasks.MACHINE_TRANSLATION] |
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_SOURCE_VERSION = "1.0.0" |
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_SEACROWD_VERSION = "2024.06.20" |
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""" |
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A collection of all the dialects are: javanese, javanese kromo, javanese ngoko, dayak ahe, |
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dayak iban, dayak pesaguan, dayak taman, buginese kelolau, buginese wajo, sundanese, |
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madurese, banjar, batak toba, khek pontianak, kapuas hulu, melayu kembayan, melayu ketapang, |
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melayu melawi, melayu pontianak, melayu putussibau, melayu sambas, melayu sintang, padang, |
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tiociu pontianak. |
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In this project, we group the dialects into several subsets: |
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Javanese (jav) : javanese, javanese kromo, javanese ngoko |
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Dayak (day) : dayak ahe, dayak iban, dayak pesaguan, dayak taman |
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Buginese (bug) : buginese kelolau, buginese wajo |
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Sundanese (sun) : sundanese |
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Madurese (mad) : madurese |
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Banjar (bjn) : banjar |
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Batak Toba (bbc) : batak toba |
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Khek (khek) : khek pontianak, kapuas hulu |
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Malay (msa) : melayu kembayan, melayu ketapang, melayu melawi, melayu pontianak, melayu putussibau, melayu sambas, melayu sintang |
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Minangkabau (min): padang |
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Tiociu (tiociu) : tiociu pontianak |
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""" |
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Domain2Subsets = { |
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"jav": ['jawa', 'jawa kromo', 'jawa ngoko'], |
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"xdy": ['dayak ahe', 'dayak iban', 'dayak pesaguan', 'dayak taman'], |
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"bug": ['bugis kelolao', 'bugis wajo'], |
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"sun": ['sunda'], |
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"mad": ['madura'], |
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"bjn": ['banjar'], |
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"bbc": ['Batak'], |
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"khek": ['kapuas hulu', 'Khek Pontianak'], |
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"msa": ['melayu kembayan', 'melayu ketapang', 'melayu melawi', 'melayu pontianak', 'melayu putussibau', 'melayu sambas', 'melayu sintang'], |
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"min": ['padang'], |
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"tiociu": ['Tiociu Pontianak'], |
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} |
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class KorpusNusantara(datasets.GeneratorBasedBuilder): |
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"""Bible En-Id is a machine translation dataset containing Indonesian-English parallel sentences collected from the bible..""" |
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BUILDER_CONFIGS = [ |
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SEACrowdConfig( |
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name=f"korpus_nusantara_ind_{subset}_source", |
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version=datasets.Version(_SOURCE_VERSION), |
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description=f"Korpus_Nusantara ind2{subset} source schema", |
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schema="source", |
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subset_id=f"korpus_nusantara", |
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) |
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for subset in _LANGUAGES[1:] |
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] + \ |
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[ |
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SEACrowdConfig( |
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name=f"korpus_nusantara_ind_{subset}_seacrowd_t2t", |
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version=datasets.Version(_SEACROWD_VERSION), |
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description=f"Korpus_Nusantara ind2{subset} Nusantara schema", |
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schema="seacrowd_t2t", |
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subset_id=f"korpus_nusantara", |
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) |
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for subset in _LANGUAGES[1:] |
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] + \ |
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[ |
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SEACrowdConfig( |
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name=f"korpus_nusantara_{subset}_ind_source", |
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version=datasets.Version(_SOURCE_VERSION), |
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description=f"Korpus_Nusantara {subset}2ind source schema", |
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schema="source", |
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subset_id=f"korpus_nusantara", |
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) |
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for subset in _LANGUAGES[1:] |
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] + \ |
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[ |
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SEACrowdConfig( |
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name=f"korpus_nusantara_{subset}_ind_seacrowd_t2t", |
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version=datasets.Version(_SEACROWD_VERSION), |
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description=f"Korpus_Nusantara {subset}2ind Nusantara schema", |
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schema="seacrowd_t2t", |
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subset_id=f"korpus_nusantara", |
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) |
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for subset in _LANGUAGES[1:] |
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] |
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DEFAULT_CONFIG_NAME = "korpus_nusantara_jav_ind_source" |
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def _info(self): |
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if self.config.schema == "source": |
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features = datasets.Features({"id": datasets.Value("string"), "text": datasets.Value("string"), "label": datasets.Value("string")}) |
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elif self.config.schema == "seacrowd_t2t": |
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features = schemas.text2text_features |
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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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base_dir = Path(dl_manager.download(urls)) |
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data_files = {"train": base_dir} |
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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_files["train"], |
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}, |
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), |
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] |
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def _merge_subsets(self, dfs, subsets, revert=False): |
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if not subsets: return None |
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df = None |
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for subset in subsets: |
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sub_df = dfs[subset] |
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orig_columns = sub_df.columns.tolist() |
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sub_df.columns = ["label", "text"]+orig_columns[2:] if revert else ["text", "label"]+orig_columns[2:] |
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if df is None: |
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df = sub_df |
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else: |
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df = pd.concat([df, sub_df], axis=0, sort=False) |
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return df |
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def get_domain_data(self, dfs): |
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domain = self.config.name |
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matched_domain = re.findall(r"korpus_nusantara_.*?_.*?_", domain) |
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assert len(matched_domain) == 1 |
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domain = matched_domain[0][:-1].replace("korpus_nusantara_", "").split("_") |
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src_lang, tgt_lang = domain[0], domain[1] |
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subsets = Domain2Subsets.get(src_lang if src_lang != "ind" else tgt_lang, None) |
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return src_lang, tgt_lang, self._merge_subsets(dfs, subsets, revert=(src_lang != "ind")) |
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def _generate_examples(self, filepath: Path): |
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"""Yields examples as (key, example) tuples.""" |
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dfs = pd.read_excel(filepath, sheet_name=None, header=None) |
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src_lang, tgt_lang, df = self.get_domain_data((dfs)) |
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if self.config.schema == "source": |
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for idx, row in enumerate(df.itertuples()): |
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ex = { |
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"id": str(idx), |
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"text": row.text, |
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"label": row.label, |
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} |
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yield idx, ex |
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elif self.config.schema == "seacrowd_t2t": |
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for idx, row in enumerate(df.itertuples()): |
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ex = { |
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"id": str(idx), |
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"text_1": row.text, |
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"text_2": row.label, |
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"text_1_name": src_lang, |
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"text_2_name": tgt_lang, |
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} |
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yield idx, ex |
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else: |
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raise ValueError(f"Invalid config: {self.config.name}") |
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