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from pathlib import Path
from typing import Dict, List, Tuple
import datasets
import pandas as pd
from seacrowd.utils import schemas
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import Tasks
_CITATION = """
"""
_DATASETNAME = "id_sts"
_DESCRIPTION = """\
SemEval is a series of international natural language processing (NLP) research workshops whose mission is
to advance the current state of the art in semantic analysis and to help create high-quality annotated datasets in a
range of increasingly challenging problems in natural language semantics. This is a translated version of SemEval Dataset
from 2012-2016 for Semantic Textual Similarity Task to Indonesian language.
"""
_HOMEPAGE = "https://github.com/ahmadizzan/sts-indo"
_LANGUAGES = ["ind"] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
_LOCAL = False
_LICENSE = "Unknown"
_URLS = {
_DATASETNAME: {
"train": "https://raw.githubusercontent.com/ahmadizzan/sts-indo/master/data/final-data/train.tsv",
"test": "https://raw.githubusercontent.com/ahmadizzan/sts-indo/master/data/final-data/test.tsv",
}
}
_SUPPORTED_TASKS = [Tasks.SEMANTIC_SIMILARITY]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class IdSts(datasets.GeneratorBasedBuilder):
"""id_sts, translated version of SemEval Dataset
from 2012-2016 for Semantic Textual Similarity Task to Indonesian language"""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
BUILDER_CONFIGS = [
SEACrowdConfig(
name="id_sts_source",
version=SOURCE_VERSION,
description="ID_STS source schema",
schema="source",
subset_id="id_sts",
),
SEACrowdConfig(
name="id_sts_seacrowd_pairs_score",
version=SEACROWD_VERSION,
description="ID_STS Nusantara schema",
schema="seacrowd_pairs_score",
subset_id="id_sts",
),
]
DEFAULT_CONFIG_NAME = "id_sts_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"text_1": datasets.Value("string"),
"text_2": datasets.Value("string"),
"label": datasets.Value("float64"),
}
)
elif self.config.schema == "seacrowd_pairs_score":
features = schemas.pairs_features_score()
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
urls = _URLS[_DATASETNAME]
train_data_path = Path(dl_manager.download(urls["train"]))
test_data_path = Path(dl_manager.download(urls["test"]))
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": train_data_path, "split": "train"},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": test_data_path, "split": "test"},
),
]
def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
# Dataset does not have id, using row index as id
df = pd.read_csv(filepath, delimiter="\t").reset_index()
df.columns = ["id", "score", "original_text_1", "original_text_2", "source", "text_1", "text_2"]
if self.config.schema == "source":
for row in df.itertuples():
ex = {"text_1": row.text_1, "text_2": row.text_2, "label": row.score}
yield row.id, ex
elif self.config.schema == "seacrowd_pairs_score":
for row in df.itertuples():
ex = {"id": str(row.id), "text_1": row.text_1, "text_2": row.text_2, "label": row.score}
yield row.id, ex
else:
raise ValueError(f"Invalid config: {self.config.name}")
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