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wikiann.py

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+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")