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