# 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 typing import Dict, List, Tuple import datasets import pandas as pd from seacrowd.utils import schemas from seacrowd.utils.configs import SEACrowdConfig from seacrowd.utils.constants import TASK_TO_SCHEMA, Licenses, Tasks _CITATION = """\ @inproceedings{PhoNER_COVID19, title = {{COVID-19 Named Entity Recognition for Vietnamese}}, author = {Thinh Hung Truong and Mai Hoang Dao and Dat Quoc Nguyen}, booktitle = {Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies}, year = {2021} } """ _DATASETNAME = "pho_ner_covid" _DESCRIPTION = """\ A named entity recognition dataset for Vietnamese with 10 newly-defined entity types in the context of the COVID-19 pandemic. Data is extracted from news articles and manually annotated. In total, there are 34 984 entities over 10 027 sentences. """ _HOMEPAGE = "https://github.com/VinAIResearch/PhoNER_COVID19/tree/main" _LANGUAGES = ["vie"] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data) _LICENSE = Licenses.UNKNOWN.value _LOCAL = False _URLS = { _DATASETNAME: { "word_level": { "dev": "https://raw.githubusercontent.com/VinAIResearch/PhoNER_COVID19/main/data/word/dev_word.json", "train": "https://raw.githubusercontent.com/VinAIResearch/PhoNER_COVID19/main/data/word/train_word.json", "test": "https://raw.githubusercontent.com/VinAIResearch/PhoNER_COVID19/main/data/word/test_word.json", }, "syllable_level": { "dev": "https://raw.githubusercontent.com/VinAIResearch/PhoNER_COVID19/main/data/syllable/dev_syllable.json", "train": "https://raw.githubusercontent.com/VinAIResearch/PhoNER_COVID19/main/data/syllable/train_syllable.json", "test": "https://raw.githubusercontent.com/VinAIResearch/PhoNER_COVID19/main/data/syllable/test_syllable.json", }, } } _SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION] _SUPPORTED_SCHEMA_STRINGS = [f"seacrowd_{str(TASK_TO_SCHEMA[task]).lower()}" for task in _SUPPORTED_TASKS] _SUPPORTED_SCHEMA_STRING_MAP: Dict[Tasks, str] = {} for task, schema_string in zip(_SUPPORTED_TASKS, _SUPPORTED_SCHEMA_STRINGS): _SUPPORTED_SCHEMA_STRING_MAP[task] = schema_string _SUBSETS = ["word_level", "syllable_level"] _SPLITS = ["train", "dev", "test"] _TAGS = [ "O", "B-ORGANIZATION", "I-ORGANIZATION", "B-SYMPTOM_AND_DISEASE", "I-SYMPTOM_AND_DISEASE", "B-LOCATION", "B-DATE", "B-PATIENT_ID", "B-AGE", "B-NAME", "I-DATE", "B-JOB", "I-LOCATION", "B-TRANSPORTATION", "B-GENDER", "I-TRANSPORTATION", "I-JOB", "I-NAME", "I-AGE", "I-PATIENT_ID", "I-GENDER", ] _SOURCE_VERSION = "1.0.0" _SEACROWD_VERSION = "2024.06.20" class PhoNerCovidDataset(datasets.GeneratorBasedBuilder): """A named entity recognition dataset for Vietnamese with 10 newly-defined entity types in the context of the COVID-19 pandemic.""" SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) BUILDER_CONFIGS = [] for subset_id in _SUBSETS: BUILDER_CONFIGS.append( SEACrowdConfig( name=f"{subset_id}_source", version=SOURCE_VERSION, description=f"{_DATASETNAME} source schema", schema="source", subset_id=subset_id, ) ) seacrowd_schema_config: list[SEACrowdConfig] = [] for seacrowd_schema in _SUPPORTED_SCHEMA_STRINGS: seacrowd_schema_config.append( SEACrowdConfig( name=f"{subset_id}_{seacrowd_schema}", version=SEACROWD_VERSION, description=f"{_DATASETNAME} {seacrowd_schema} schema", schema=f"{seacrowd_schema}", subset_id=subset_id, ) ) BUILDER_CONFIGS.extend(seacrowd_schema_config) DEFAULT_CONFIG_NAME = f"{_SUBSETS[0]}_source" def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source": features = datasets.Features( { "words": datasets.Sequence(datasets.Value("string")), "tags": datasets.Sequence(datasets.ClassLabel(names=_TAGS)), } ) elif self.config.schema == _SUPPORTED_SCHEMA_STRING_MAP[Tasks.NAMED_ENTITY_RECOGNITION]: features = schemas.seq_label_features(label_names=_TAGS) else: raise ValueError(f"Invalid config: {self.config.name}") 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.""" split_generators = [] for split in _SPLITS: path = dl_manager.download_and_extract(_URLS[_DATASETNAME][self.config.subset_id][split]) split_generators.append( datasets.SplitGenerator( name=split, gen_kwargs={ "path": path, }, ) ) return split_generators def _generate_examples(self, path: str) -> Tuple[int, Dict]: """Yields examples as (key, example) tuples.""" idx = 0 df = pd.read_json(path, lines=True) if self.config.schema == "source": for _, row in df.iterrows(): yield idx, row.to_dict() idx += 1 elif self.config.schema == _SUPPORTED_SCHEMA_STRING_MAP[Tasks.NAMED_ENTITY_RECOGNITION]: df["id"] = df.index df = df.rename(columns={"words": "tokens", "tags": "labels"}) for _, row in df.iterrows(): yield idx, row.to_dict() idx += 1 else: raise ValueError(f"Invalid config: {self.config.name}")