| 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 = [ |
| |
| 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), |
| |
| 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), |
| |
| 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( |
| { |
| |
| "sent_id": datasets.Value("string"), |
| "text": datasets.Value("string"), |
| "text_en": datasets.Value("string"), |
| |
| "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 = { |
| |
| "sent_id": str(idx), |
| "text": txt, |
| "text_en": txt, |
| |
| "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 |
|
|
