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import io |
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import conllu |
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import datasets |
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from seacrowd.utils.common_parser import load_ud_data_as_seacrowd_kb |
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from seacrowd.utils.configs import SEACrowdConfig |
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from seacrowd.utils import schemas |
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from seacrowd.utils.constants import DEFAULT_SEACROWD_VIEW_NAME, DEFAULT_SOURCE_VIEW_NAME, Licenses, Tasks |
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_DATASETNAME = "stb_ext" |
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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 = ["eng"] |
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_LOCAL = False |
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_CITATION = """\ |
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@article{wang2019genesis, |
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title={From genesis to creole language: Transfer learning for singlish universal dependencies parsing and POS tagging}, |
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author={Wang, Hongmin and Yang, Jie and Zhang, Yue}, |
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journal={ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP)}, |
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volume={19}, |
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number={1}, |
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pages={1--29}, |
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year={2019}, |
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publisher={ACM New York, NY, USA} |
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} |
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""" |
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_DESCRIPTION = """\ |
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We adopt the Universal Dependencies protocol for constructing the Singlish dependency treebank, both as a new resource |
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for the low-resource languages and to facilitate knowledge transfer from English. Briefly, the STB-EXT dataset offers |
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a 3-times larger training set, while keeping the same dev and test sets from STB-ACL. We provide treebanks with both |
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gold-standard as well as automatically generated POS tags. |
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""" |
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_HOMEPAGE = "https://github.com/wanghm92/Sing_Par/tree/master/TALLIP19_dataset/treebank" |
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_LICENSE = Licenses.MIT.value |
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_PREFIX = "https://raw.githubusercontent.com/wanghm92/Sing_Par/master/TALLIP19_dataset/treebank/" |
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_URLS = { |
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"gold_pos": { |
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"train": _PREFIX + "gold_pos/train.ext.conll", |
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}, |
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"en_ud_autopos": {"train": _PREFIX + "en-ud-autopos/en-ud-train.conllu.autoupos", "validation": _PREFIX + "en-ud-autopos/en-ud-dev.conllu.ann.auto.epoch24.upos", "test": _PREFIX + "en-ud-autopos/en-ud-test.conllu.ann.auto.epoch24.upos"}, |
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"auto_pos_multiview": { |
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"train": _PREFIX + "auto_pos/multiview/train.autopos.multiview.conll", |
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"validation": _PREFIX + "auto_pos/multiview/dev.autopos.multiview.conll", |
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"test": _PREFIX + "auto_pos/multiview/test.autopos.multiview.conll", |
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}, |
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"auto_pos_stack": { |
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"train": _PREFIX + "auto_pos/stack/train.autopos.stack.conll", |
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"validation": _PREFIX + "auto_pos/stack/dev.autopos.stack.conll", |
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"test": _PREFIX + "auto_pos/stack/test.autopos.stack.conll", |
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}, |
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} |
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_POSTAGS = ["ADJ", "ADP", "ADV", "AUX", "CONJ", "DET", "INTJ", "NOUN", "NUM", "PART", "PRON", "PROPN", "PUNCT", "SCONJ", "SYM", "VERB", "X", "root"] |
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_SUPPORTED_TASKS = [Tasks.POS_TAGGING, Tasks.DEPENDENCY_PARSING] |
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_SOURCE_VERSION = "1.0.0" |
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_SEACROWD_VERSION = "2024.06.20" |
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def config_constructor(subset_id, schema, version): |
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return SEACrowdConfig(name=f"{_DATASETNAME}_{subset_id}_{schema}", |
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version=datasets.Version(version), description=_DESCRIPTION, |
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schema=schema, subset_id=subset_id) |
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class StbExtDataset(datasets.GeneratorBasedBuilder): |
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"""This is a seacrowd dataloader for the STB-EXT dataset, which offers a 3-times larger training set, while keeping |
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the same dev and test sets from STB-ACL. It provides treebanks with both gold-standard and automatically generated POS tags.""" |
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BUILDER_CONFIGS = [ |
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config_constructor(subset_id="auto_pos_stack", schema="source", version=_SOURCE_VERSION), |
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config_constructor(subset_id="auto_pos_multiview", schema="source", version=_SOURCE_VERSION), |
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config_constructor(subset_id="en_ud_autopos", schema="source", version=_SOURCE_VERSION), |
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config_constructor(subset_id="gold_pos", schema="source", version=_SOURCE_VERSION), |
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config_constructor(subset_id="auto_pos_stack", schema="seacrowd_seq_label", version=_SEACROWD_VERSION), |
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config_constructor(subset_id="auto_pos_multiview", schema="seacrowd_seq_label", version=_SEACROWD_VERSION), |
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config_constructor(subset_id="en_ud_autopos", schema="seacrowd_seq_label", version=_SEACROWD_VERSION), |
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config_constructor(subset_id="gold_pos", schema="seacrowd_seq_label", version=_SEACROWD_VERSION), |
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config_constructor(subset_id="auto_pos_stack", schema="seacrowd_kb", version=_SEACROWD_VERSION), |
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config_constructor(subset_id="auto_pos_multiview", schema="seacrowd_kb", version=_SEACROWD_VERSION), |
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config_constructor(subset_id="en_ud_autopos", schema="seacrowd_kb", version=_SEACROWD_VERSION), |
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config_constructor(subset_id="gold_pos", schema="seacrowd_kb", version=_SEACROWD_VERSION), |
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] |
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DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_gold_pos_source" |
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def _info(self): |
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if self.config.schema == "source": |
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features = datasets.Features( |
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{ |
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"sent_id": datasets.Value("string"), |
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"text": datasets.Value("string"), |
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"text_en": datasets.Value("string"), |
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"id": [datasets.Value("string")], |
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"form": [datasets.Value("string")], |
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"lemma": [datasets.Value("string")], |
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"upos": [datasets.Value("string")], |
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"xpos": [datasets.Value("string")], |
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"feats": [datasets.Value("string")], |
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"head": [datasets.Value("string")], |
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"deprel": [datasets.Value("string")], |
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"deps": [datasets.Value("string")], |
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"misc": [datasets.Value("string")], |
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} |
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) |
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elif self.config.schema == "seacrowd_seq_label": |
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features = schemas.seq_label_features(label_names=_POSTAGS) |
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elif self.config.schema == "seacrowd_kb": |
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features = schemas.kb_features |
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else: |
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raise ValueError(f"Invalid config: {self.config.schema}") |
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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): |
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""" "return splitGenerators""" |
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urls = _URLS[self.config.subset_id] |
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downloaded_files = dl_manager.download_and_extract(urls) |
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splits = [] |
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if "train" in downloaded_files: |
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splits.append(datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]})) |
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if "validation" in downloaded_files: |
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splits.append(datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["validation"]})) |
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if "test" in downloaded_files: |
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splits.append(datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]})) |
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return splits |
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def _generate_examples(self, filepath): |
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def process_buffer(TextIO): |
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BOM = "\ufeff" |
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buffer = io.StringIO() |
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for line in TextIO: |
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line = line.replace(BOM, "") if BOM in line else line |
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buffer.write(line) |
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buffer.seek(0) |
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return buffer |
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with open(filepath, "r", encoding="utf-8") as data_file: |
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tokenlist = list(conllu.parse_incr(process_buffer(data_file))) |
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data_instances = [] |
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for idx, sent in enumerate(tokenlist): |
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idx = sent.metadata["sent_id"] if "sent_id" in sent.metadata else idx |
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tokens = [token["form"] for token in sent] |
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txt = sent.metadata["text"] if "text" in sent.metadata else " ".join(tokens) |
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example = { |
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"sent_id": str(idx), |
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"text": txt, |
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"text_en": txt, |
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"id": [token["id"] for token in sent], |
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"form": [token["form"] for token in sent], |
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"lemma": [token["lemma"] for token in sent], |
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"upos": [token["upos"] for token in sent], |
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"xpos": [token["xpos"] for token in sent], |
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"feats": [str(token["feats"]) for token in sent], |
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"head": [str(token["head"]) for token in sent], |
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"deprel": [str(token["deprel"]) for token in sent], |
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"deps": [str(token["deps"]) for token in sent], |
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"misc": [str(token["misc"]) for token in sent] |
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} |
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data_instances.append(example) |
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if self.config.schema == "source": |
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pass |
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if self.config.schema == "seacrowd_seq_label": |
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data_instances = list( |
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map( |
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lambda d: { |
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"id": d["sent_id"], |
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"tokens": d["form"], |
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"labels": d["upos"], |
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}, |
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data_instances, |
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) |
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) |
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if self.config.schema == "seacrowd_kb": |
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data_instances = load_ud_data_as_seacrowd_kb(filepath, data_instances) |
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for key, exam in enumerate(data_instances): |
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yield key, exam |
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