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from pathlib import Path
from typing import Dict, List, Tuple
import datasets
from nusacrowd.utils import schemas
from nusacrowd.utils.common_parser import load_conll_data
from nusacrowd.utils.configs import NusantaraConfig
from nusacrowd.utils.constants import Tasks
_CITATION = """\
@inproceedings{koto-etal-2020-indolem,
title = "{I}ndo{LEM} and {I}ndo{BERT}: A Benchmark Dataset and Pre-trained Language Model for {I}ndonesian {NLP}",
author = "Koto, Fajri and
Rahimi, Afshin and
Lau, Jey Han and
Baldwin, Timothy",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.66",
doi = "10.18653/v1/2020.coling-main.66",
pages = "757--770"
}
@phdthesis{fachri2014pengenalan,
title = {Pengenalan Entitas Bernama Pada Teks Bahasa Indonesia Menggunakan Hidden Markov Model},
author = {FACHRI, MUHAMMAD},
year = {2014},
school = {Universitas Gadjah Mada}
}
"""
_LOCAL = False
_LANGUAGES = ["ind"] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
_DATASETNAME = "indolem_ner_ugm"
_DESCRIPTION = """\
NER UGM is a Named Entity Recognition dataset that comprises 2,343 sentences from news articles, and was constructed at the University of Gajah Mada based on five named entity classes: person, organization, location, time, and quantity.
"""
_HOMEPAGE = "https://indolem.github.io/"
_LICENSE = "Creative Commons Attribution 4.0"
_URLS = {
_DATASETNAME: {
"train": "https://raw.githubusercontent.com/indolem/indolem/main/ner/data/nerugm/train.0{fold_number}.tsv",
"validation": "https://raw.githubusercontent.com/indolem/indolem/main/ner/data/nerugm/dev.0{fold_number}.tsv",
"test": "https://raw.githubusercontent.com/indolem/indolem/main/ner/data/nerugm/test.0{fold_number}.tsv"
}
}
_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION]
_SOURCE_VERSION = "1.0.0"
_NUSANTARA_VERSION = "1.0.0"
class IndolemNERUGM(datasets.GeneratorBasedBuilder):
"""NER UGM comprises 2,343 sentences from news articles, and was constructed at the University of Gajah Mada based on five named entity classes: person, organization, location, time, and quantity; and based on 5-fold cross validation"""
label_classes = ["B-PERSON", "B-LOCATION", "B-ORGANIZATION", "B-TIME", "B-QUANTITY", "I-PERSON", "I-LOCATION", "I-ORGANIZATION", "I-TIME", "I-QUANTITY", "O"]
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
NUSANTARA_VERSION = datasets.Version(_NUSANTARA_VERSION)
BUILDER_CONFIGS = (
[
NusantaraConfig(
name="indolem_ner_ugm_fold{fold_number}_source".format(fold_number=i),
version=_SOURCE_VERSION,
description="indolem_ner_ugm source schema",
schema="source",
subset_id="indolem_ner_ugm_fold{fold_number}".format(fold_number=i),
) for i in range(5)
]
+ [
NusantaraConfig(
name="indolem_ner_ugm_fold{fold_number}_nusantara_seq_label".format(fold_number=i),
version=_NUSANTARA_VERSION,
description="indolem_ner_ugm Nusantara schema",
schema="nusantara_seq_label",
subset_id="indolem_ner_ugm_fold{fold_number}".format(fold_number=i),
) for i in range(5)
]
)
DEFAULT_CONFIG_NAME = "indolem_ner_ugm_fold0_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 == "nusantara_seq_label":
features = schemas.seq_label_features(self.label_classes)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
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:
return 0
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
idx = self._get_fold_index()
urls = _URLS[_DATASETNAME]
for key in urls:
urls[key] = urls[key].format(fold_number=idx+1)
data_dir = dl_manager.download_and_extract(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# Whatever you put in gen_kwargs will be passed to _generate_examples
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 == "nusantara_seq_label":
for i, row in enumerate(conll_dataset):
ex = {
"id": str(i),
"tokens": row["sentence"],
"labels": row["label"]
}
yield i, ex
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