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import re |
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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 |
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_CITATION = """\ |
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@data{FK2/VTAHRH_2022, |
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author = {ARDIYANTI SURYANI, ARIE and Widyantoro, Dwi Hendratmo and Purwarianti, Ayu and Sudaryat, Yayat}, |
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publisher = {Telkom University Dataverse}, |
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title = {{PoSTagged Sundanese Monolingual Corpus}}, |
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year = {2022}, |
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version = {DRAFT VERSION}, |
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doi = {10.34820/FK2/VTAHRH}, |
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url = {https://doi.org/10.34820/FK2/VTAHRH} |
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} |
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@INPROCEEDINGS{7437678, |
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author={Suryani, Arie Ardiyanti and Widyantoro, Dwi Hendratmo and Purwarianti, Ayu and Sudaryat, Yayat}, |
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booktitle={2015 International Conference on Information Technology Systems and Innovation (ICITSI)}, |
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title={Experiment on a phrase-based statistical machine translation using PoS Tag information for Sundanese into Indonesian}, |
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year={2015}, |
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volume={}, |
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number={}, |
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pages={1-6}, |
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doi={10.1109/ICITSI.2015.7437678} |
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} |
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""" |
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_LANGUAGES = ["sun"] |
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_LOCAL = False |
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_DATASETNAME = "postag_su" |
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_DESCRIPTION = """\ |
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This dataset contains 3616 lines of Sundanese sentences taken from several online magazines (Mangle, Dewan Dakwah Jabar, and Balebat). \ |
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Annotated with PoS Labels by several undergraduates of the Sundanese Language Education Study Program (PPBS), UPI Bandung. |
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""" |
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_HOMEPAGE = "https://dataverse.telkomuniversity.ac.id/dataset.xhtml?persistentId=doi:10.34820/FK2/VTAHRH" |
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_LICENSE = 'CC0 - "Public Domain Dedication"' |
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_URLS = { |
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_DATASETNAME: "https://dataverse.telkomuniversity.ac.id/api/access/datafile/:persistentId?persistentId=doi:10.34820/FK2/VTAHRH/WQIFK8", |
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} |
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_SUPPORTED_TASKS = [Tasks.POS_TAGGING] |
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_SOURCE_VERSION = "1.1.0" |
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_SEACROWD_VERSION = "2024.06.20" |
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class PosSunMonoDataset(datasets.GeneratorBasedBuilder): |
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"""PoSTagged Sundanese Monolingual Corpus""" |
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SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) |
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SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) |
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POS_TAGS = [ |
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"", |
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"!", |
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'"', |
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"'", |
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")", |
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",", |
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"-", |
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".", |
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"...", |
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"....", |
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"/", |
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":", |
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";", |
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"?", |
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"C", |
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"CBI", |
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"CC", |
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"CDC", |
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"CDI", |
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"CDO", |
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"CDP", |
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"CDT", |
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"CP", |
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"CRB", |
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"CS", |
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"DC", |
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"DT", |
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"FE", |
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"FW", |
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"GM", |
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"IN", |
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"J", |
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"JJ", |
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"KA", |
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"KK", |
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"MD", |
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"MG", |
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"MN", |
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"N", |
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"NEG", |
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"NN", |
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"NNA", |
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"NNG", |
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"NNN", |
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"NNO", |
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"NNP", |
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"NNPP", |
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"NP", |
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"NPP", |
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"OP", |
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"PB", |
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"PCDP", |
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"PR", |
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"PRL", |
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"PRL|IN", |
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"PRN", |
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"PRP", |
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"RB", |
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"RBT", |
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"RB|RP", |
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"RN", |
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"RP", |
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"SC", |
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"SCC", |
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"SC|IN", |
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"SYM", |
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"UH", |
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"VB", |
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"VBI", |
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"VBT", |
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"VRB", |
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"W", |
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"WH", |
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"WHP", |
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"WRP", |
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"`", |
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"–", |
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"—", |
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"‘", |
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"’", |
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"“", |
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"”", |
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] |
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BUILDER_CONFIGS = [ |
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SEACrowdConfig( |
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name=f"{_DATASETNAME}_source", |
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version=SOURCE_VERSION, |
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description=f"{_DATASETNAME} source schema", |
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schema="source", |
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subset_id=f"{_DATASETNAME}", |
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), |
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SEACrowdConfig( |
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name=f"{_DATASETNAME}_seacrowd_seq_label", |
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version=SEACROWD_VERSION, |
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description=f"{_DATASETNAME} Nusantara Seq Label schema", |
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schema="seacrowd_seq_label", |
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subset_id=f"{_DATASETNAME}", |
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), |
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] |
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DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source" |
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def _info(self) -> datasets.DatasetInfo: |
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if self.config.schema == "source": |
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features = datasets.Features({"labeled_sentence": datasets.Value("string")}) |
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elif self.config.schema == "seacrowd_seq_label": |
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features = schemas.seq_label_features(self.POS_TAGS) |
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else: |
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raise NotImplementedError(f"Schema '{self.config.schema}' is not defined.") |
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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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data_path = dl_manager.download(urls) |
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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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}, |
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), |
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] |
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def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]: |
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"""Yields examples as (key, example) tuples.""" |
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def __hotfix(line): |
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if line.endswith(" taun|NN 1953.|."): |
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return line.replace(" taun|NN 1953.|.", " taun|NN 1953|CDP .|.") |
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elif line.endswith(" jeung|CC|CC sasab|RB .|."): |
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return line.replace(" jeung|CC|CC sasab|RB .|.", " jeung|CC sasab|RB .|.") |
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elif line.startswith("Kagiatan|NN éta|DT dihadiran|VBT kira|-kira "): |
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return line.replace("Kagiatan|NN éta|DT dihadiran|VBT kira|-kira ", "Kagiatan|NN éta|DT dihadiran|VBT kira-kira|DT ") |
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return line |
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with open(filepath, "r", encoding="utf8") as ipt: |
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raw = list(map(lambda l: __hotfix(l.rstrip("\n ")), ipt)) |
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pat_0 = r"(,\|,|\?\|\?|-\|-|!\|!)" |
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repl_spc = r" \1 " |
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pat_1 = r"([A-Z”])(\.\|\.)" |
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pat_2 = r"(\.\|\.)([^. ])" |
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repl_spl = r"\1 \2" |
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pat_3 = r"([^ ]+\|[^ ]+)\| " |
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repl_del = r"\1 " |
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pat_4 = r"\|\|" |
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repl_dup = r"|" |
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def __apply_regex(txt): |
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for pat, repl in [(pat_0, repl_spc), (pat_1, repl_spl), (pat_2, repl_spl), (pat_3, repl_del), (pat_4, repl_dup)]: |
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txt = re.sub(pat, repl, txt) |
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return txt |
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def __cleanse_label(token): |
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text, label = token |
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return text, re.sub(r"([A-Z]+)[.,)]", r"\1", label.upper()) |
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if self.config.schema == "source": |
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for key, example in enumerate(raw): |
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yield key, {"labeled_sentence": example} |
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elif self.config.schema == "seacrowd_seq_label": |
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spaced = list(map(__apply_regex, raw)) |
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data = list(map(lambda l: [__cleanse_label(tok.split("|", 1)) for tok in filter(None, l.split(" "))], spaced)) |
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for key, example in enumerate(data): |
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tokens, labels = zip(*example) |
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yield key, {"id": str(key), "tokens": tokens, "labels": labels} |
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else: |
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raise NotImplementedError(f"Schema '{self.config.schema}' is not defined.") |
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