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