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 = """[MultiNERD](https://aclanthology.org/2022.findings-naacl.60/)"""
_NAME = "multinerd"
_VERSION = "1.0.0"
_CITATION = """
@inproceedings{tedeschi-navigli-2022-multinerd,
title = "{M}ulti{NERD}: A Multilingual, Multi-Genre and Fine-Grained Dataset for Named Entity Recognition (and Disambiguation)",
author = "Tedeschi, Simone and
Navigli, Roberto",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-naacl.60",
doi = "10.18653/v1/2022.findings-naacl.60",
pages = "801--812",
abstract = "Named Entity Recognition (NER) is the task of identifying named entities in texts and classifying them through specific semantic categories, a process which is crucial for a wide range of NLP applications. Current datasets for NER focus mainly on coarse-grained entity types, tend to consider a single textual genre and to cover a narrow set of languages, thus limiting the general applicability of NER systems.In this work, we design a new methodology for automatically producing NER annotations, and address the aforementioned limitations by introducing a novel dataset that covers 10 languages, 15 NER categories and 2 textual genres.We also introduce a manually-annotated test set, and extensively evaluate the quality of our novel dataset on both this new test set and standard benchmarks for NER.In addition, in our dataset, we include: i) disambiguation information to enable the development of multilingual entity linking systems, and ii) image URLs to encourage the creation of multimodal systems.We release our dataset at https://github.com/Babelscape/multinerd.",
}
"""
_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}.jsonl'],
} for l in _LANGUAGE
}
class MultiNERDConfig(datasets.BuilderConfig):
"""BuilderConfig"""
def __init__(self, **kwargs):
"""BuilderConfig.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(MultiNERDConfig, self).__init__(**kwargs)
class MultiNERD(datasets.GeneratorBasedBuilder):
"""Dataset."""
BUILDER_CONFIGS = [
MultiNERDConfig(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.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,
) |