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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 (DEFAULT_SEACROWD_VIEW_NAME,
DEFAULT_SOURCE_VIEW_NAME, Tasks)
_DATASETNAME = "nerp"
_SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME
_UNIFIED_VIEW_NAME = DEFAULT_SEACROWD_VIEW_NAME
_LANGUAGES = ["ind"]
_LOCAL = False
_CITATION = """\
@inproceedings{hoesen2018investigating,
title={Investigating bi-lstm and crf with pos tag embedding for indonesian named entity tagger},
author={Hoesen, Devin and Purwarianti, Ayu},
booktitle={2018 International Conference on Asian Language Processing (IALP)},
pages={35--38},
year={2018},
organization={IEEE}
}
"""
_DESCRIPTION = """\
The NERP dataset (Hoesen and Purwarianti, 2018) contains texts collected from several Indonesian news websites with five labels
- PER (name of person)
- LOC (name of location)
- IND (name of product or brand)
- EVT (name of the event)
- FNB (name of food and beverage).
NERP makes use of the IOB chunking format, just like the TermA dataset.
"""
_HOMEPAGE = "https://github.com/IndoNLP/indonlu"
_LICENSE = "Creative Common Attribution Share-Alike 4.0 International"
_URLs = {
"train": "https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/nerp_ner-prosa/train_preprocess.txt",
"validation": "https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/nerp_ner-prosa/valid_preprocess.txt",
"test": "https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/nerp_ner-prosa/test_preprocess_masked_label.txt",
}
_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class NerpDataset(datasets.GeneratorBasedBuilder):
"""NERP is an NER tagging dataset contains about (train=6720,valid=840,test=840) sentences, with 11 classes."""
label_classes = ["B-PPL", "B-PLC", "B-EVT", "B-IND", "B-FNB", "I-PPL", "I-PLC", "I-EVT", "I-IND", "I-FNB", "O"]
BUILDER_CONFIGS = [
SEACrowdConfig(
name="nerp_source",
version=datasets.Version(_SOURCE_VERSION),
description="NERP source schema",
schema="source",
subset_id="nerp",
),
SEACrowdConfig(
name="nerp_seacrowd_seq_label",
version=datasets.Version(_SEACROWD_VERSION),
description="NERP Nusantara schema",
schema="seacrowd_seq_label",
subset_id="nerp",
),
]
DEFAULT_CONFIG_NAME = "nerp_source"
def _info(self):
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)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
train_tsv_path = Path(dl_manager.download_and_extract(_URLs["train"]))
validation_tsv_path = Path(dl_manager.download_and_extract(_URLs["validation"]))
test_tsv_path = Path(dl_manager.download_and_extract(_URLs["test"]))
data_files = {
"train": train_tsv_path,
"validation": validation_tsv_path,
"test": test_tsv_path,
}
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": data_files["train"]},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"filepath": data_files["validation"]},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": data_files["test"]},
),
]
def _generate_examples(self, filepath: Path):
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"], "ner_tag": 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
else:
raise ValueError(f"Invalid config: {self.config.name}")
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