Datasets:
Languages:
Indonesian
Tags:
named-entity-recognition
| from pathlib import Path | |
| from typing import List | |
| 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 (DEFAULT_NUSANTARA_VIEW_NAME, | |
| DEFAULT_SOURCE_VIEW_NAME, Tasks) | |
| _DATASETNAME = "nerp" | |
| _SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME | |
| _UNIFIED_VIEW_NAME = DEFAULT_NUSANTARA_VIEW_NAME | |
| _LANGUAGES = ["ind"] | |
| _LOCAL = False | |
| _CITATION = """\ | |
| @inproceedings{hoesen2018investigating, | |
| title={Investigating bi-lstm and crf with pos tag embedding for indonesian named entity tagger}, | |
| author={Hoesen, Devin and Purwarianti, Ayu}, | |
| booktitle={2018 International Conference on Asian Language Processing (IALP)}, | |
| pages={35--38}, | |
| year={2018}, | |
| organization={IEEE} | |
| } | |
| """ | |
| _DESCRIPTION = """\ | |
| The NERP dataset (Hoesen and Purwarianti, 2018) contains texts collected from several Indonesian news websites with five labels | |
| - PER (name of person) | |
| - LOC (name of location) | |
| - IND (name of product or brand) | |
| - EVT (name of the event) | |
| - FNB (name of food and beverage). | |
| NERP makes use of the IOB chunking format, just like the TermA dataset. | |
| """ | |
| _HOMEPAGE = "https://github.com/IndoNLP/indonlu" | |
| _LICENSE = "Creative Common Attribution Share-Alike 4.0 International" | |
| _URLs = { | |
| "train": "https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/nerp_ner-prosa/train_preprocess.txt", | |
| "validation": "https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/nerp_ner-prosa/valid_preprocess.txt", | |
| "test": "https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/nerp_ner-prosa/test_preprocess_masked_label.txt", | |
| } | |
| _SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION] | |
| _SOURCE_VERSION = "1.0.0" | |
| _NUSANTARA_VERSION = "1.0.0" | |
| class NerpDataset(datasets.GeneratorBasedBuilder): | |
| """NERP is an NER tagging dataset contains about (train=6720,valid=840,test=840) sentences, with 11 classes.""" | |
| label_classes = ["B-PPL", "B-PLC", "B-EVT", "B-IND", "B-FNB", "I-PPL", "I-PLC", "I-EVT", "I-IND", "I-FNB", "O"] | |
| BUILDER_CONFIGS = [ | |
| NusantaraConfig( | |
| name="nerp_source", | |
| version=datasets.Version(_SOURCE_VERSION), | |
| description="NERP source schema", | |
| schema="source", | |
| subset_id="nerp", | |
| ), | |
| NusantaraConfig( | |
| name="nerp_nusantara_seq_label", | |
| version=datasets.Version(_NUSANTARA_VERSION), | |
| description="NERP Nusantara schema", | |
| schema="nusantara_seq_label", | |
| subset_id="nerp", | |
| ), | |
| ] | |
| DEFAULT_CONFIG_NAME = "nerp_source" | |
| def _info(self): | |
| if self.config.schema == "source": | |
| features = datasets.Features({"index": datasets.Value("string"), "tokens": [datasets.Value("string")], "ner_tag": [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 _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: | |
| train_tsv_path = Path(dl_manager.download_and_extract(_URLs["train"])) | |
| validation_tsv_path = Path(dl_manager.download_and_extract(_URLs["validation"])) | |
| test_tsv_path = Path(dl_manager.download_and_extract(_URLs["test"])) | |
| data_files = { | |
| "train": train_tsv_path, | |
| "validation": validation_tsv_path, | |
| "test": test_tsv_path, | |
| } | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={"filepath": data_files["train"]}, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| gen_kwargs={"filepath": data_files["validation"]}, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={"filepath": data_files["test"]}, | |
| ), | |
| ] | |
| def _generate_examples(self, filepath: Path): | |
| 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"], "ner_tag": 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 | |
| else: | |
| raise ValueError(f"Invalid config: {self.config.name}") | |