Update wikiann.py

wikiann.py

@@ -0,0 +1,100 @@
+""" NER dataset compiled by T-NER library https://github.com/asahi417/tner/tree/master/tner """
+import json
+from itertools import chain
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+_DESCRIPTION = """[WikiAnn](https://aclanthology.org/P17-1178/)"""
+_NAME = "wikiann"
+_VERSION = "1.0.0"
+_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://aclanthology.org/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.",
+}
+"""
+
+_HOME_PAGE = "https://github.com/asahi417/tner"
+_URL = f'https://huggingface.co/datasets/tner/{_NAME}/raw/main/dataset'
+_LANGUAGE = ["ace", "bg", "da", "fur", "ilo", "lij", "mzn", "qu", "su", "vi", "af", "bh", "de", "fy", "io", "lmo", "nap",
+        "rm", "sv", "vls", "als", "bn", "diq", "ga", "is", "ln", "nds", "ro", "sw", "vo", "am", "bo", "dv", "gan", "it",
+        "lt", "ne", "ru", "szl", "wa", "an", "br", "el", "gd", "ja", "lv", "nl", "rw", "ta", "war", "ang", "bs", "eml",
+        "gl", "jbo", "map-bms", "nn", "sa", "te", "wuu", "ar", "ca", "en", "gn", "jv", "mg", "no", "sah", "tg", "xmf",
+        "arc", "cbk-zam", "eo", "gu", "ka", "mhr", "nov", "scn", "th", "yi", "arz", "cdo", "es", "hak", "kk", "mi",
+        "oc", "sco", "tk", "yo", "as", "ce", "et", "he", "km", "min", "or", "sd", "tl", "zea", "ast", "ceb", "eu", "hi",
+        "kn", "mk", "os", "sh", "tr", "zh-classical", "ay", "ckb", "ext", "hr", "ko", "ml", "pa", "si", "tt",
+        "zh-min-nan", "az", "co", "fa", "hsb", "ksh", "mn", "pdc", "simple", "ug", "zh-yue", "ba", "crh", "fi", "hu",
+        "ku", "mr", "pl", "sk", "uk", "zh", "bar", "cs", "fiu-vro", "hy", "ky", "ms", "pms", "sl", "ur", "bat-smg",
+        "csb", "fo", "ia", "la", "mt", "pnb", "so", "uz", "be-x-old", "cv", "fr", "id", "lb", "mwl", "ps", "sq", "vec",
+        "be", "cy", "frr", "ig", "li", "my", "pt", "sr", "vep"]
+_URLS = {
+    l: {
+      str(datasets.Split.TEST): [f'{_URL}/{l}/test.jsonl'],
+      str(datasets.Split.TRAIN): [f'{_URL}/{l}/train.jsonl'],
+      str(datasets.Split.VALIDATION): [f'{_URL}/{l}/dev.jsonl']
+     } for l in _LANGUAGE
+}
+
+
+class WikiAnnConfig(datasets.BuilderConfig):
+    """BuilderConfig"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig.
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(WikiAnnConfig, self).__init__(**kwargs)
+
+
+class WikiAnn(datasets.GeneratorBasedBuilder):
+    """Dataset."""
+
+    BUILDER_CONFIGS = [
+        WikiAnnConfig(name=l, version=datasets.Version(_VERSION), description=f"{_DESCRIPTION} (language: {l})") for l in _LANGUAGE
+    ]
+
+    def _split_generators(self, dl_manager):
+        downloaded_file = dl_manager.download_and_extract(_URLS[self.config.name])
+        return [datasets.SplitGenerator(name=i, gen_kwargs={"filepaths": downloaded_file[str(i)]})
+                for i in [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST]]
+
+    def _generate_examples(self, filepaths):
+        _key = 0
+        for filepath in filepaths:
+            logger.info(f"generating examples from = {filepath}")
+            with open(filepath, encoding="utf-8") as f:
+                _list = [i for i in f.read().split('\n') if len(i) > 0]
+                for i in _list:
+                    data = json.loads(i)
+                    yield _key, data
+                    _key += 1
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "tags": datasets.Sequence(datasets.Value("int32")),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOME_PAGE,
+            citation=_CITATION,
+        )