Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
Polish
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
other
Annotations Creators:
expert-generated
Source Datasets:
original
Tags:
License:
File size: 3,551 Bytes
24e1824 a5983a1 24e1824 a5983a1 24e1824 b956e56 24e1824 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 |
# 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.
"""NKJP-NER"""
import csv
import os
import datasets
from datasets.tasks import TextClassification
_CITATION = """\
@book{przepiorkowski2012narodowy,
title={Narodowy korpus jezyka polskiego},
author={Przepi{\'o}rkowski, Adam},
year={2012},
publisher={Naukowe PWN}
}
"""
_DESCRIPTION = """\
The NKJP-NER is based on a human-annotated part of National Corpus of Polish (NKJP). We extracted sentences with named entities of exactly one type. The task is to predict the type of the named entity.
"""
_HOMEPAGE = "https://klejbenchmark.com/tasks/"
_LICENSE = "GNU GPL v.3"
_URLs = "https://klejbenchmark.com/static/data/klej_nkjp-ner.zip"
class NkjpNer(datasets.GeneratorBasedBuilder):
"""NKJP-NER"""
VERSION = datasets.Version("1.1.0")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"sentence": datasets.Value("string"),
"target": datasets.ClassLabel(
names=[
"geogName",
"noEntity",
"orgName",
"persName",
"placeName",
"time",
]
),
}
),
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
task_templates=[TextClassification(text_column="sentence", label_column="target")],
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
data_dir = dl_manager.download_and_extract(_URLs)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": os.path.join(data_dir, "train.tsv"),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": os.path.join(data_dir, "test_features.tsv"), "split": "test"},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": os.path.join(data_dir, "dev.tsv"),
"split": "dev",
},
),
]
def _generate_examples(self, filepath, split):
"""Yields examples."""
with open(filepath, encoding="utf-8") as f:
reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
for id_, row in enumerate(reader):
yield id_, {
"sentence": row["sentence"],
"target": -1 if split == "test" else row["target"],
}
|