Datasets:
Tasks:
Token Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
named-entity-recognition
Languages:
Romanian
Size:
10K - 100K
ArXiv:
License:
# coding=utf-8 | |
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
import json | |
import datasets | |
logger = datasets.logging.get_logger(__name__) | |
# Find for instance the citation on arxiv or on the dataset repo/website | |
_CITATION = """\ | |
@article{dumitrescu2019introducing, | |
title={Introducing RONEC--the Romanian Named Entity Corpus}, | |
author={Dumitrescu, Stefan Daniel and Avram, Andrei-Marius}, | |
journal={arXiv preprint arXiv:1909.01247}, | |
year={2019} | |
} | |
""" | |
# You can copy an official description | |
_DESCRIPTION = """\ | |
RONEC - the Romanian Named Entity Corpus, at version 2.0, holds 12330 sentences with over 0.5M tokens, annotated with 15 classes, to a total of 80.283 distinctly annotated entities. It is used for named entity recognition and represents the largest Romanian NER corpus to date. | |
""" | |
_HOMEPAGE = "https://github.com/dumitrescustefan/ronec" | |
_LICENSE = "MIT License" | |
# The HuggingFace dataset library don't host the datasets but only point to the original files | |
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) | |
_URL = "https://raw.githubusercontent.com/dumitrescustefan/ronec/master/data/" | |
_TRAINING_FILE = "train.json" | |
_DEV_FILE = "valid.json" | |
_TEST_FILE = "test.json" | |
class RONECConfig(datasets.BuilderConfig): | |
"""BuilderConfig for RONEC dataset""" | |
def __init__(self, **kwargs): | |
super(RONECConfig, self).__init__(**kwargs) | |
class RONEC(datasets.GeneratorBasedBuilder): | |
"""RONEC dataset""" | |
VERSION = datasets.Version("2.0.0") | |
BUILDER_CONFIGS = [ | |
RONECConfig(name="ronec", version=VERSION, description="RONEC dataset"), | |
] | |
def _info(self): | |
features = datasets.Features( | |
{ | |
"id": datasets.Value("int32"), | |
"tokens": datasets.Sequence(datasets.Value("string")), | |
"ner_ids": datasets.Sequence(datasets.Value("int32")), | |
"space_after": datasets.Sequence(datasets.Value("bool")), | |
"ner_tags": datasets.Sequence( | |
datasets.features.ClassLabel( | |
names=[ | |
"O", | |
"B-PERSON", | |
"I-PERSON", | |
"B-ORG", | |
"I-ORG", | |
"B-GPE", | |
"I-GPE", | |
"B-LOC", | |
"I-LOC", | |
"B-NAT_REL_POL", | |
"I-NAT_REL_POL", | |
"B-EVENT", | |
"I-EVENT", | |
"B-LANGUAGE", | |
"I-LANGUAGE", | |
"B-WORK_OF_ART", | |
"I-WORK_OF_ART", | |
"B-DATETIME", | |
"I-DATETIME", | |
"B-PERIOD", | |
"I-PERIOD", | |
"B-MONEY", | |
"I-MONEY", | |
"B-QUANTITY", | |
"I-QUANTITY", | |
"B-NUMERIC", | |
"I-NUMERIC", | |
"B-ORDINAL", | |
"I-ORDINAL", | |
"B-FACILITY", | |
"I-FACILITY", | |
] | |
) | |
), | |
} | |
) | |
return datasets.DatasetInfo( | |
# This is the description that will appear on the datasets page. | |
description=_DESCRIPTION, | |
# This defines the different columns of the dataset and their types | |
features=features, # Here we define them above because they are different between the two configurations | |
# If there's a common (input, target) tuple from the features, | |
# specify them here. They'll be used if as_supervised=True in | |
# builder.as_dataset. | |
supervised_keys=None, | |
# Homepage of the dataset for documentation | |
homepage=_HOMEPAGE, | |
# License for the dataset if available | |
license=_LICENSE, | |
# Citation for the dataset | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
"""Returns SplitGenerators.""" | |
urls_to_download = {"train": _URL + _TRAINING_FILE, "dev": _URL + _DEV_FILE, "test": _URL + _TEST_FILE} | |
downloaded_files = dl_manager.download(urls_to_download) | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={"filepath": downloaded_files["train"]}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={"filepath": downloaded_files["dev"]}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={"filepath": downloaded_files["test"]}, | |
), | |
] | |
def _generate_examples(self, filepath): | |
"""Yields examples.""" | |
logger.info("⏳ Generating examples from = %s", filepath) | |
with open(filepath, "r", encoding="utf-8") as f: | |
data = json.load(f) | |
for instance in data: | |
yield instance["id"], instance | |