from pathlib import Path from typing import Dict, List, Tuple import datasets from nusacrowd.utils import schemas from nusacrowd.utils.common_parser import load_conll_data from nusacrowd.utils.configs import NusantaraConfig from nusacrowd.utils.constants import Tasks _CITATION = """\ @inproceedings{koto-etal-2020-indolem, title = "{I}ndo{LEM} and {I}ndo{BERT}: A Benchmark Dataset and Pre-trained Language Model for {I}ndonesian {NLP}", author = "Koto, Fajri and Rahimi, Afshin and Lau, Jey Han and Baldwin, Timothy", booktitle = "Proceedings of the 28th International Conference on Computational Linguistics", month = dec, year = "2020", address = "Barcelona, Spain (Online)", publisher = "International Committee on Computational Linguistics", url = "https://aclanthology.org/2020.coling-main.66", doi = "10.18653/v1/2020.coling-main.66", pages = "757--770" } @phdthesis{fachri2014pengenalan, title = {Pengenalan Entitas Bernama Pada Teks Bahasa Indonesia Menggunakan Hidden Markov Model}, author = {FACHRI, MUHAMMAD}, year = {2014}, school = {Universitas Gadjah Mada} } """ _LOCAL = False _LANGUAGES = ["ind"] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data) _DATASETNAME = "indolem_ner_ugm" _DESCRIPTION = """\ NER UGM is a Named Entity Recognition dataset that comprises 2,343 sentences from news articles, and was constructed at the University of Gajah Mada based on five named entity classes: person, organization, location, time, and quantity. """ _HOMEPAGE = "https://indolem.github.io/" _LICENSE = "Creative Commons Attribution 4.0" _URLS = { _DATASETNAME: { "train": "https://raw.githubusercontent.com/indolem/indolem/main/ner/data/nerugm/train.0{fold_number}.tsv", "validation": "https://raw.githubusercontent.com/indolem/indolem/main/ner/data/nerugm/dev.0{fold_number}.tsv", "test": "https://raw.githubusercontent.com/indolem/indolem/main/ner/data/nerugm/test.0{fold_number}.tsv" } } _SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION] _SOURCE_VERSION = "1.0.0" _NUSANTARA_VERSION = "1.0.0" class IndolemNERUGM(datasets.GeneratorBasedBuilder): """NER UGM comprises 2,343 sentences from news articles, and was constructed at the University of Gajah Mada based on five named entity classes: person, organization, location, time, and quantity; and based on 5-fold cross validation""" label_classes = ["B-PERSON", "B-LOCATION", "B-ORGANIZATION", "B-TIME", "B-QUANTITY", "I-PERSON", "I-LOCATION", "I-ORGANIZATION", "I-TIME", "I-QUANTITY", "O"] SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) NUSANTARA_VERSION = datasets.Version(_NUSANTARA_VERSION) BUILDER_CONFIGS = ( [ NusantaraConfig( name="indolem_ner_ugm_fold{fold_number}_source".format(fold_number=i), version=_SOURCE_VERSION, description="indolem_ner_ugm source schema", schema="source", subset_id="indolem_ner_ugm_fold{fold_number}".format(fold_number=i), ) for i in range(5) ] + [ NusantaraConfig( name="indolem_ner_ugm_fold{fold_number}_nusantara_seq_label".format(fold_number=i), version=_NUSANTARA_VERSION, description="indolem_ner_ugm Nusantara schema", schema="nusantara_seq_label", subset_id="indolem_ner_ugm_fold{fold_number}".format(fold_number=i), ) for i in range(5) ] ) DEFAULT_CONFIG_NAME = "indolem_ner_ugm_fold0_source" def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source": features = datasets.Features( { "index": datasets.Value("string"), "tokens": [datasets.Value("string")], "tags": [datasets.Value("string")] } ) elif self.config.schema == "nusantara_seq_label": features = schemas.seq_label_features(self.label_classes) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _get_fold_index(self): try: subset_id = self.config.subset_id idx_fold = subset_id.index("_fold") file_id = subset_id[(idx_fold + 5):] return int(file_id) except: return 0 def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: idx = self._get_fold_index() urls = _URLS[_DATASETNAME] for key in urls: urls[key] = urls[key].format(fold_number=idx+1) data_dir = dl_manager.download_and_extract(urls) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # Whatever you put in gen_kwargs will be passed to _generate_examples gen_kwargs={ "filepath": data_dir["train"], "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "filepath": data_dir["test"], "split": "test", }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "filepath": data_dir["validation"], "split": "dev", }, ), ] def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]: conll_dataset = load_conll_data(filepath) if self.config.schema == "source": for i, row in enumerate(conll_dataset): ex = { "index": str(i), "tokens": row["sentence"], "tags": row["label"] } yield i, ex elif self.config.schema == "nusantara_seq_label": for i, row in enumerate(conll_dataset): ex = { "id": str(i), "tokens": row["sentence"], "labels": row["label"] } yield i, ex