Datasets:
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""" 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 = """[wikineural](https://aclanthology.org/2021.findings-emnlp.215/)"""
_NAME = "wikineural"
_VERSION = "1.0.0"
_CITATION = """
@inproceedings{tedeschi-etal-2021-wikineural-combined,
title = "{W}iki{NE}u{R}al: {C}ombined Neural and Knowledge-based Silver Data Creation for Multilingual {NER}",
author = "Tedeschi, Simone and
Maiorca, Valentino and
Campolungo, Niccol{\`o} and
Cecconi, Francesco and
Navigli, Roberto",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.215",
doi = "10.18653/v1/2021.findings-emnlp.215",
pages = "2521--2533",
abstract = "Multilingual Named Entity Recognition (NER) is a key intermediate task which is needed in many areas of NLP. In this paper, we address the well-known issue of data scarcity in NER, especially relevant when moving to a multilingual scenario, and go beyond current approaches to the creation of multilingual silver data for the task. We exploit the texts of Wikipedia and introduce a new methodology based on the effective combination of knowledge-based approaches and neural models, together with a novel domain adaptation technique, to produce high-quality training corpora for NER. We evaluate our datasets extensively on standard benchmarks for NER, yielding substantial improvements up to 6 span-based F1-score points over previous state-of-the-art systems for data creation.",
}
"""
_HOME_PAGE = "https://github.com/asahi417/tner"
_URL = f'https://huggingface.co/datasets/tner/{_NAME}/resolve/main/dataset'
_LANGUAGE = ['de', 'en', 'es', 'fr', 'it', 'nl', 'pl', 'pt', 'ru']
_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 WikiNeuralConfig(datasets.BuilderConfig):
"""BuilderConfig"""
def __init__(self, **kwargs):
"""BuilderConfig.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(WikiNeuralConfig, self).__init__(**kwargs)
class WikiNeural(datasets.GeneratorBasedBuilder):
"""Dataset."""
BUILDER_CONFIGS = [
WikiNeuralConfig(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,
) |