# coding=utf-8 # Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from pathlib import Path from typing import Dict, List, Tuple import conllu import datasets from seacrowd.sea_datasets.vndt.utils import parse_token_and_impute_metadata from seacrowd.utils import schemas from seacrowd.utils.common_parser import (load_ud_data, load_ud_data_as_seacrowd_kb) from seacrowd.utils.configs import SEACrowdConfig from seacrowd.utils.constants import Licenses, Tasks _CITATION = """\ @InProceedings{Nguyen2014NLDB, author = {Nguyen, Dat Quoc and Nguyen, Dai Quoc and Pham, Son Bao and Nguyen, Phuong-Thai and Nguyen, Minh Le}, title = {{From Treebank Conversion to Automatic Dependency Parsing for Vietnamese}}, booktitle = {{Proceedings of 19th International Conference on Application of Natural Language to Information Systems}}, year = {2014}, pages = {196-207}, url = {https://github.com/datquocnguyen/VnDT}, } """ _DATASETNAME = "vndt" _DESCRIPTION = """\ VnDT is a Vietnamese dependency treebank, consisting of 10K+ sentences (219k words). The VnDT Treebank is automatically converted from the input Vietnamese Treebank. """ _HOMEPAGE = "https://github.com/datquocnguyen/VnDT" _LANGUAGES = {"vie": "vi"} _LICENSE = Licenses.UNKNOWN.value _LOCAL = False _URLS = { "gold-dev": "https://raw.githubusercontent.com/datquocnguyen/VnDT/master/VnDTv1.1-gold-POS-tags-dev.conll", "gold-test": "https://raw.githubusercontent.com/datquocnguyen/VnDT/master/VnDTv1.1-gold-POS-tags-test.conll", "gold-train": "https://raw.githubusercontent.com/datquocnguyen/VnDT/master/VnDTv1.1-gold-POS-tags-train.conll", "predicted-dev": "https://raw.githubusercontent.com/datquocnguyen/VnDT/master/VnDTv1.1-predicted-POS-tags-dev.conll", "predicted-test": "https://raw.githubusercontent.com/datquocnguyen/VnDT/master/VnDTv1.1-predicted-POS-tags-test.conll", "predicted-train": "https://raw.githubusercontent.com/datquocnguyen/VnDT/master/VnDTv1.1-predicted-POS-tags-train.conll", } _SUPPORTED_TASKS = [Tasks.DEPENDENCY_PARSING] _SOURCE_VERSION = "1.0.0" _SEACROWD_VERSION = "2024.06.20" class VnDTDataset(datasets.GeneratorBasedBuilder): """ VnDT is a Vietnamese dependency treebank from https://github.com/datquocnguyen/VnDT. """ # Override conllu.parse_token_and_metadata via monkey patching conllu.parse_token_and_metadata = parse_token_and_impute_metadata SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) BUILDER_CONFIGS = [ SEACrowdConfig( name=f"{_DATASETNAME}_gold_source", version=datasets.Version(_SOURCE_VERSION), description=f"{_DATASETNAME} gold standard source schema", schema="source", subset_id="gold", ), SEACrowdConfig( name=f"{_DATASETNAME}_gold_seacrowd_kb", version=datasets.Version(_SEACROWD_VERSION), description=f"{_DATASETNAME} gold standard SEACrowd schema", schema="seacrowd_kb", subset_id="gold", ), SEACrowdConfig( name=f"{_DATASETNAME}_predicted_source", version=datasets.Version(_SOURCE_VERSION), description=f"{_DATASETNAME} predicted source schema", schema="source", subset_id="predicted", ), SEACrowdConfig( name=f"{_DATASETNAME}_predicted_seacrowd_kb", version=datasets.Version(_SEACROWD_VERSION), description=f"{_DATASETNAME} predicted SEACrowd schema", schema="seacrowd_kb", subset_id="predicted", ), ] def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source": features = datasets.Features( { "id": datasets.Sequence(datasets.Value("int8")), "form": datasets.Sequence(datasets.Value("string")), "lemma": datasets.Sequence(datasets.Value("string")), "upos": datasets.Sequence(datasets.Value("string")), "xpos": datasets.Sequence(datasets.Value("string")), "feats": datasets.Sequence(datasets.Value("string")), "head": datasets.Sequence(datasets.Value("int8")), "deprel": datasets.Sequence(datasets.Value("string")), "deps": datasets.Sequence(datasets.Value("string")), "misc": datasets.Sequence(datasets.Value("string")), } ) elif self.config.schema == "seacrowd_kb": features = schemas.kb_features else: raise ValueError(f"Invalid schema: '{self.config.schema}'") return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: """ Returns SplitGenerators. """ paths = {key: dl_manager.download_and_extract(value) for key, value in _URLS.items()} if self.config.subset_id == "gold": filtered_paths = {key: value for key, value in paths.items() if "gold" in key} elif self.config.subset_id == "predicted": filtered_paths = {key: value for key, value in paths.items() if "predicted" in key} else: raise NotImplementedError(f"Invalid subset: '{self.config.subset_id}'.") return [ datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "filepaths": [value for key, value in filtered_paths.items() if "dev" in key], "split": "validation", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "filepaths": [value for key, value in filtered_paths.items() if "test" in key], "split": "test", }, ), datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepaths": [value for key, value in filtered_paths.items() if "train" in key], "split": "train", }, ), ] def _generate_examples(self, filepaths: Path, split: str) -> Tuple[int, Dict]: """ Yields examples as (key, example) tuples. """ dataset = None for file in filepaths: if self.config.schema == "source": dataset = list(load_ud_data(file)) elif self.config.schema == "seacrowd_kb": dataset = list(load_ud_data_as_seacrowd_kb(file, dataset)) else: raise ValueError(f"Invalid config: '{self.config.name}'") for idx, example in enumerate(dataset): if self.config.schema == "source": example.pop('sent_id', None) example.pop('text', None) yield idx, example