Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
Igbo
Size:
10K<n<100K
ArXiv:
License:
File size: 4,341 Bytes
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# 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.
"""Igbo Named Entity Recognition Dataset"""
from __future__ import absolute_import, division, print_function
import datasets
_CITATION = """\
@misc{ezeani2020igboenglish,
title={Igbo-English Machine Translation: An Evaluation Benchmark},
author={Ignatius Ezeani and Paul Rayson and Ikechukwu Onyenwe and Chinedu Uchechukwu and Mark Hepple},
year={2020},
eprint={2004.00648},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
"""
_DESCRIPTION = """\
Igbo Named Entity Recognition Dataset
"""
_HOMEPAGE = "https://github.com/IgnatiusEzeani/IGBONLP/tree/master/ig_ner"
_URLs = {
"ner_data": "https://raw.githubusercontent.com/IgnatiusEzeani/IGBONLP/master/ig_ner/igbo_data.txt",
"free_text": "https://raw.githubusercontent.com/IgnatiusEzeani/IGBONLP/master/ig_ner/igbo_data10000.txt",
}
class IgboNer(datasets.GeneratorBasedBuilder):
"""Dataset from the Igbo NER Project"""
VERSION = datasets.Version("1.1.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="ner_data",
version=VERSION,
description="This dataset contains the named entity and all the sentences containing that entity.",
),
datasets.BuilderConfig(
name="free_text", version=VERSION, description="This dataset contains all sentences used for NER."
),
]
DEFAULT_CONFIG_NAME = "ner_data"
def _info(self):
if self.config.name == "ner_data":
features = datasets.Features(
{
"content_n": datasets.Value("string"),
"named_entity": datasets.Value("string"),
"sentences": datasets.Sequence(datasets.Value("string")),
}
)
else:
features = datasets.Features(
{
"sentences": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
my_urls = _URLs[self.config.name]
data_dir = dl_manager.download_and_extract(my_urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": data_dir,
"split": "train",
},
),
]
def _generate_examples(self, filepath, split):
""" Yields examples. """
dictionary = {}
with open(filepath, "r", encoding="utf-8-sig") as f:
if self.config.name == "ner_data":
for id_, row in enumerate(f):
row = row.strip().split("\t")
content_n = row[0]
if content_n in dictionary.keys():
(dictionary[content_n]["sentences"]).append(row[1])
else:
dictionary[content_n] = {}
dictionary[content_n]["named_entity"] = row[1]
dictionary[content_n]["sentences"] = [row[1]]
yield id_, {
"content_n": content_n,
"named_entity": dictionary[content_n]["named_entity"],
"sentences": dictionary[content_n]["sentences"],
}
else:
for id_, row in enumerate(f):
yield id_, {
"sentences": row.strip(),
}
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