from pathlib import Path from typing import List import datasets from datasets import NamedSplit from nusacrowd.utils import schemas from nusacrowd.utils.configs import NusantaraConfig from nusacrowd.utils.constants import (DEFAULT_NUSANTARA_VIEW_NAME, DEFAULT_SOURCE_VIEW_NAME, Tasks) _DATASETNAME = "wikiann" _SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME _UNIFIED_VIEW_NAME = DEFAULT_NUSANTARA_VIEW_NAME _LANGUAGES = ["ind", "eng", "jav", "min", "sun", "ace", "mly", "map-bms"] _LOCAL = False _CITATION = """\ @inproceedings{pan-etal-2017-cross, title = "Cross-lingual Name Tagging and Linking for 282 Languages", author = "Pan, Xiaoman and Zhang, Boliang and May, Jonathan and Nothman, Joel and Knight, Kevin and Ji, Heng", booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = jul, year = "2017", address = "Vancouver, Canada", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/P17-1178", doi = "10.18653/v1/P17-1178", pages = "1946--1958", abstract = "The ambitious goal of this work is to develop a cross-lingual name tagging and linking framework for 282 languages that exist in Wikipedia. Given a document in any of these languages, our framework is able to identify name mentions, assign a coarse-grained or fine-grained type to each mention, and link it to an English Knowledge Base (KB) if it is linkable. We achieve this goal by performing a series of new KB mining methods: generating {``}silver-standard{''} annotations by transferring annotations from English to other languages through cross-lingual links and KB properties, refining annotations through self-training and topic selection, deriving language-specific morphology features from anchor links, and mining word translation pairs from cross-lingual links. Both name tagging and linking results for 282 languages are promising on Wikipedia data and on-Wikipedia data.", } @inproceedings{rahimi-etal-2019-massively, title = "Massively Multilingual Transfer for {NER}", author = "Rahimi, Afshin and Li, Yuan and Cohn, Trevor", booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/P19-1015", pages = "151--164", } """ _DESCRIPTION = """\ The wikiann dataset contains NER tags with labels from O (0), B-PER (1), I-PER (2), B-ORG (3), I-ORG (4), B-LOC (5), I-LOC (6). The Indonesian subset is used. WikiANN (sometimes called PAN-X) is a multilingual named entity recognition dataset consisting of Wikipedia articles annotated with LOC (location), PER (person), and ORG (organisation) tags in the IOB2 format. This version corresponds to the balanced train, dev, and test splits of Rahimi et al. (2019), and uses the following subsets from the original WikiANN corpus Language WikiAnn ISO 639-3 Indonesian id ind Javanese jv jav Minangkabau min min Sundanese su sun Acehnese ace ace Malay ms mly Banyumasan map-bms map-bms """ _HOMEPAGE = "https://github.com/afshinrahimi/mmner" _LICENSE = "Apache-2.0 license" _URLs = { "wikiann": "https://s3.amazonaws.com/datasets.huggingface.co/wikiann/1.1.0/panx_dataset.zip", } _SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION] _SOURCE_VERSION = "1.1.0" _NUSANTARA_VERSION = "1.0.0" def nusantara_config_constructor(lang, schema, version): if lang == "": raise ValueError(f"Invalid lang {lang}") if schema != "source" and schema != "nusantara_seq_label": raise ValueError(f"Invalid schema: {schema}") return NusantaraConfig( name="wikiann_{lang}_{schema}".format(lang=lang, schema=schema), version=datasets.Version(version), description="wikiann with {schema} schema for {lang} language".format(lang=lang, schema=schema), schema=schema, subset_id="wikiann", ) LANGUAGES_MAP = {"eng": "english", "ind": "indonesian", "jav": "javanese", "min": "minangkabau", "sun": "sundanese", "ace": "acehnese", "mly": "malay", "map_bms": "banyumasan"} # Actual code is map-bms LANG_CODES = {"eng": "en", "ind": "id", "jav": "jv", "min": "min", "sun": "su", "ace": "ace", "mly": "ms", "map_bms": "map-bms"} class WikiAnnDataset(datasets.GeneratorBasedBuilder): """wikiann is an NER tagging dataset consisting of Wikipedia articles annotated with LOC, PER, and ORG tags for multiple Indonesian language. If the language is not specified, it loads the Indonesian subset.""" label_classes = ["B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "O"] BUILDER_CONFIGS = [nusantara_config_constructor(lang, "source", _SOURCE_VERSION) for lang in LANGUAGES_MAP] + [nusantara_config_constructor(lang, "nusantara_seq_label", _NUSANTARA_VERSION) for lang in LANGUAGES_MAP] DEFAULT_CONFIG_NAME = "wikiann_ind_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 get_lang(self, name): return name.removesuffix("_source").removesuffix("_nusantara_seq_label").removeprefix("wikiann_") def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: path = Path(dl_manager.download_and_extract(_URLs["wikiann"])) lang = LANG_CODES[self.get_lang(self.config.name)] wikiann_dl_dir = path / f"{lang}.tar.gz" return [ datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={"split": "dev", "filepath": dl_manager.iter_archive(wikiann_dl_dir)}, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={"split": "test", "filepath": dl_manager.iter_archive(wikiann_dl_dir)}, ), datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"split": "train", "filepath": dl_manager.iter_archive(wikiann_dl_dir)}, ), datasets.SplitGenerator( name=NamedSplit("extra"), gen_kwargs={"split": "extra", "filepath": dl_manager.iter_archive(wikiann_dl_dir)}, ), ] def _generate_examples(self, filepath: Path, split): """Based on https://github.com/huggingface/datasets/blob/main/datasets/wikiann/wikiann.py""" fps = filepath tokens = [] ner_tags = [] langs = [] guid_index = 0 for k, file in fps: if k == split: for line in file: line = line.decode("utf-8") if line == "" or line == "\n": if tokens: if self.config.schema == "source": yield guid_index, {"index": str(guid_index), "tokens": tokens, "ner_tag": ner_tags} elif self.config.schema == "nusantara_seq_label": yield guid_index, {"id": str(guid_index), "tokens": tokens, "labels": ner_tags} else: raise ValueError(f"Invalid config: {self.config.name}") guid_index += 1 tokens = [] ner_tags = [] langs = [] else: # wikiann data is tab separated splits = line.split("\t") # strip out en: prefix langs.append(splits[0].split(":")[0]) tokens.append(":".join(splits[0].split(":")[1:])) if len(splits) > 1: ner_tags.append(splits[-1].replace("\n", "")) else: # examples have no label in test set ner_tags.append("O")