File size: 6,327 Bytes
a54b736 d84bba0 a54b736 d84bba0 a54b736 d84bba0 a54b736 d84bba0 a54b736 d84bba0 a54b736 d84bba0 a54b736 d84bba0 a54b736 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 |
# coding=utf-8
# Copyright 2022 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.
""" NERGrit Dataset """
from pathlib import Path
from typing import List
import datasets
from seacrowd.utils import schemas
from seacrowd.utils.common_parser import load_conll_data
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import Tasks
_CITATION = """\
@misc{Fahmi_NERGRIT_CORPUS_2019,
author = {Fahmi, Husni and Wibisono, Yudi and Kusumawati, Riyanti},
title = {{NERGRIT CORPUS}},
url = {https://github.com/grit-id/nergrit-corpus},
year = {2019}
}
"""
_LOCAL = False
_LANGUAGES = ["ind"] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
_DATASETNAME = "nergrit"
_DESCRIPTION = """\
Nergrit Corpus is a dataset collection of Indonesian Named Entity Recognition (NER), Statement Extraction,
and Sentiment Analysis developed by PT Gria Inovasi Teknologi (GRIT).
The Named Entity Recognition contains 18 entities as follow:
'CRD': Cardinal
'DAT': Date
'EVT': Event
'FAC': Facility
'GPE': Geopolitical Entity
'LAW': Law Entity (such as Undang-Undang)
'LOC': Location
'MON': Money
'NOR': Political Organization
'ORD': Ordinal
'ORG': Organization
'PER': Person
'PRC': Percent
'PRD': Product
'QTY': Quantity
'REG': Religion
'TIM': Time
'WOA': Work of Art
'LAN': Language
"""
_HOMEPAGE = "https://github.com/grit-id/nergrit-corpus"
_LICENSE = "MIT"
_URL = "https://github.com/cahya-wirawan/indonesian-language-models/raw/master/data/nergrit-corpus_20190726_corrected.tgz"
_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class NergritDataset(datasets.GeneratorBasedBuilder):
"""Indonesian Named Entity Recognition from https://github.com/grit-id/nergrit-corpus."""
label_classes = {
"ner": [
"B-CRD",
"B-DAT",
"B-EVT",
"B-FAC",
"B-GPE",
"B-LAN",
"B-LAW",
"B-LOC",
"B-MON",
"B-NOR",
"B-ORD",
"B-ORG",
"B-PER",
"B-PRC",
"B-PRD",
"B-QTY",
"B-REG",
"B-TIM",
"B-WOA",
"I-CRD",
"I-DAT",
"I-EVT",
"I-FAC",
"I-GPE",
"I-LAN",
"I-LAW",
"I-LOC",
"I-MON",
"I-NOR",
"I-ORD",
"I-ORG",
"I-PER",
"I-PRC",
"I-PRD",
"I-QTY",
"I-REG",
"I-TIM",
"I-WOA",
"O",
],
"sentiment": ["B-POS", "B-NEG", "B-NET", "I-POS", "I-NEG", "I-NET", "O"],
"statement": ["B-BREL", "B-FREL", "B-STAT", "B-WHO", "I-BREL", "I-FREL", "I-STAT", "I-WHO", "O"],
}
BUILDER_CONFIGS = [
SEACrowdConfig(
name=f"nergrit_{task}_source",
version=datasets.Version(_SOURCE_VERSION),
description="NERGrit source schema",
schema="source",
subset_id=f"nergrit_{task}",
)
for task in label_classes
]
BUILDER_CONFIGS += [
SEACrowdConfig(
name=f"nergrit_{task}_seacrowd_seq_label",
version=datasets.Version(_SOURCE_VERSION),
description="NERGrit Nusantara schema",
schema="seacrowd_seq_label",
subset_id=f"nergrit_{task}",
)
for task in label_classes
]
DEFAULT_CONFIG_NAME = "nergrit_ner_source"
def _info(self):
features = None
task = self.config.subset_id.split("_")[-1]
if self.config.schema == "source":
features = datasets.Features({"index": datasets.Value("string"), "tokens": [datasets.Value("string")], "ner_tag": [datasets.Value("string")]})
elif self.config.schema == "seacrowd_seq_label":
features = schemas.seq_label_features(self.label_classes[task])
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
task = self.config.subset_id.split("_")[-1]
archive = Path(dl_manager.download_and_extract(_URL))
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": archive / f"nergrit-corpus/{task}/data/train_corrected.txt"},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": archive / f"nergrit-corpus/{task}/data/test_corrected.txt"},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"filepath": archive / f"nergrit-corpus/{task}/data/valid_corrected.txt"},
),
]
def _generate_examples(self, filepath: Path):
conll_dataset = load_conll_data(filepath)
if self.config.schema == "source":
for index, row in enumerate(conll_dataset):
ex = {"index": str(index), "tokens": row["sentence"], "ner_tag": row["label"]}
yield index, ex
elif self.config.schema == "seacrowd_seq_label":
for index, row in enumerate(conll_dataset):
ex = {"id": str(index), "tokens": row["sentence"], "labels": row["label"]}
yield index, ex
else:
raise ValueError(f"Invalid config: {self.config.name}")
|