File size: 8,197 Bytes
880151b 56a1497 880151b 56a1497 880151b 56a1497 880151b 56a1497 880151b 56a1497 880151b 56a1497 880151b 56a1497 880151b 56a1497 880151b 56a1497 880151b 56a1497 880151b 56a1497 880151b |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 |
from pathlib import Path
from typing import Dict, List, Tuple
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 = """\
@INPROCEEDINGS{8275098,
author={Gultom, Yohanes and Wibowo, Wahyu Catur},
booktitle={2017 International Workshop on Big Data and Information Security (IWBIS)},
title={Automatic open domain information extraction from Indonesian text},
year={2017},
volume={},
number={},
pages={23-30},
doi={10.1109/IWBIS.2017.8275098}}
@article{DBLP:journals/corr/abs-2011-00677,
author = {Fajri Koto and
Afshin Rahimi and
Jey Han Lau and
Timothy Baldwin},
title = {IndoLEM and IndoBERT: {A} Benchmark Dataset and Pre-trained Language
Model for Indonesian {NLP}},
journal = {CoRR},
volume = {abs/2011.00677},
year = {2020},
url = {https://arxiv.org/abs/2011.00677},
eprinttype = {arXiv},
eprint = {2011.00677},
timestamp = {Fri, 06 Nov 2020 15:32:47 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2011-00677.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
"""
_LOCAL = False
_LANGUAGES = ["ind"] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
_DATASETNAME = "indolem_nerui"
_DESCRIPTION = """\
NER UI is a Named Entity Recognition dataset that contains 2,125 sentences obtained via an annotation assignment in an NLP course at the University of Indonesia in 2016.
The corpus has three named entity classes: location, organisation, and person with training/dev/test distribution: 1,530/170/42 and based on 5-fold cross validation.
"""
_HOMEPAGE = "https://indolem.github.io/"
_LICENSE = "Creative Commons Attribution 4.0"
_URLS = {
_DATASETNAME: [
{
"train": "https://raw.githubusercontent.com/indolem/indolem/main/ner/data/nerui/train.01.tsv",
"validation": "https://raw.githubusercontent.com/indolem/indolem/main/ner/data/nerui/dev.01.tsv",
"test": "https://raw.githubusercontent.com/indolem/indolem/main/ner/data/nerui/test.01.tsv",
},
{
"train": "https://raw.githubusercontent.com/indolem/indolem/main/ner/data/nerui/train.02.tsv",
"validation": "https://raw.githubusercontent.com/indolem/indolem/main/ner/data/nerui/dev.02.tsv",
"test": "https://raw.githubusercontent.com/indolem/indolem/main/ner/data/nerui/test.02.tsv",
},
{
"train": "https://raw.githubusercontent.com/indolem/indolem/main/ner/data/nerui/train.03.tsv",
"validation": "https://raw.githubusercontent.com/indolem/indolem/main/ner/data/nerui/dev.03.tsv",
"test": "https://raw.githubusercontent.com/indolem/indolem/main/ner/data/nerui/test.03.tsv",
},
{
"train": "https://raw.githubusercontent.com/indolem/indolem/main/ner/data/nerui/train.04.tsv",
"validation": "https://raw.githubusercontent.com/indolem/indolem/main/ner/data/nerui/dev.04.tsv",
"test": "https://raw.githubusercontent.com/indolem/indolem/main/ner/data/nerui/test.04.tsv",
},
{
"train": "https://raw.githubusercontent.com/indolem/indolem/main/ner/data/nerui/train.05.tsv",
"validation": "https://raw.githubusercontent.com/indolem/indolem/main/ner/data/nerui/dev.05.tsv",
"test": "https://raw.githubusercontent.com/indolem/indolem/main/ner/data/nerui/test.05.tsv",
},
]
}
_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class IndolemNERUIDataset(datasets.GeneratorBasedBuilder):
"""NER UI contains 2,125 sentences obtained via an annotation assignment in an NLP course at the University of Indonesia. The corpus has three named entity classes: location, organisation, and person; and based on 5-fold cross validation."""
label_classes = [
"O",
"B-LOCATION",
"B-ORGANIZATION",
"B-PERSON",
"I-LOCATION",
"I-ORGANIZATION",
"I-PERSON",
]
BUILDER_CONFIGS = [
SEACrowdConfig(
name=f"indolem_nerui_source",
version=datasets.Version(_SOURCE_VERSION),
description="Indolem NER UI source schema",
schema="source",
subset_id=f"indolem_nerui",
),
SEACrowdConfig(
name=f"indolem_nerui_seacrowd_seq_label",
version=datasets.Version(_SEACROWD_VERSION),
description="Indolem NER UI Nusantara schema",
schema="seacrowd_seq_label",
subset_id=f"indolem_nerui",
)
] + [
SEACrowdConfig(
name=f"indolem_nerui_fold{i}_source",
version=datasets.Version(_SOURCE_VERSION),
description="Indolem NER UI source schema",
schema="source",
subset_id=f"indolem_nerui_fold{i}",
)
for i in range(5)
] + [
SEACrowdConfig(
name=f"indolem_nerui_fold{i}_seacrowd_seq_label",
version=datasets.Version(_SEACROWD_VERSION),
description="Indolem NER UI Nusantara schema",
schema="seacrowd_seq_label",
subset_id=f"indolem_nerui_fold{i}",
)
for i in range(5)
]
DEFAULT_CONFIG_NAME = "indolem_nerui_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"index": datasets.Value("string"),
"tokens": [datasets.Value("string")],
"tags": [datasets.Value("string")],
}
)
elif self.config.schema == "seacrowd_seq_label":
features = schemas.seq_label_features(self.label_classes)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
idx = self._get_fold_index()
urls = _URLS[_DATASETNAME][idx]
data_dir = dl_manager.download_and_extract(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": data_dir["train"],
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": data_dir["test"],
"split": "test",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": data_dir["validation"],
"split": "dev",
},
),
]
def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
conll_dataset = load_conll_data(filepath)
if self.config.schema == "source":
for i, row in enumerate(conll_dataset):
ex = {
"index": str(i),
"tokens": row["sentence"],
"tags": row["label"],
}
yield i, ex
elif self.config.schema == "seacrowd_seq_label":
for i, row in enumerate(conll_dataset):
ex = {
"id": str(i),
"tokens": row["sentence"],
"labels": row["label"],
}
yield i, ex
def _get_fold_index(self):
try:
subset_id = self.config.subset_id
idx_fold = subset_id.index("_fold")
file_id = subset_id[(idx_fold + 5):]
return int(file_id)
except:
# get default: fold0 (index 0)
return 0
|