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import os
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 Licenses, Tasks
_CITATION = """\
@inproceedings{nguyen-etal-2018-introducing,
title = "Introducing Two {V}ietnamese Datasets for Evaluating Semantic Models of (Dis-)Similarity and Relatedness",
author = "Nguyen, Kim Anh and
Schulte im Walde, Sabine and
Vu, Ngoc Thang",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-2032",
doi = "10.18653/v1/N18-2032",
pages = "199--205"
}
"""
_DATASETNAME = "visim400"
_DESCRIPTION = """\
ViSim-400 is a Vietnamese dataset of semantic relation \
pairs for evaluation of models that reflect the \
continuum between similarity and relatedness.
We choose 'Sim2' instead of 'Sim1' for the label output of \
our SEACrowd dataloader schema because it's been normalized to [1, 10].
"""
_HOMEPAGE = "https://www.ims.uni-stuttgart.de/forschung/ressourcen/experiment-daten/vnese-sem-datasets/"
_LANGUAGES = ["vie"]
_LICENSE = Licenses.CC_BY_NC_SA_4_0.value
_LOCAL = False
_URLS = {_DATASETNAME: "https://www.ims.uni-stuttgart.de/documents/ressourcen/experiment-daten/ViData.zip"}
_SUPPORTED_TASKS = [Tasks.SEMANTIC_SIMILARITY]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class ViSim400Dataset(datasets.GeneratorBasedBuilder):
"""
ViSim-400 is a Vietnamese dataset of semantic relation \
pairs for evaluation of models that reflect the \
continuum between similarity and relatedness.
"""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
SEACROWD_SCHEMA_NAME = "pairs_score"
BUILDER_CONFIGS = [
SEACrowdConfig(
name=f"{_DATASETNAME}_source",
version=_SOURCE_VERSION,
description=f"{_DATASETNAME} source schema",
schema="source",
subset_id=f"{_DATASETNAME}",
),
SEACrowdConfig(
name=f"{_DATASETNAME}_seacrowd_{SEACROWD_SCHEMA_NAME}",
version=_SEACROWD_VERSION,
description=f"{_DATASETNAME} SEACrowd schema",
schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
subset_id=f"{_DATASETNAME}",
),
]
DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"Word1": datasets.Value("string"),
"Word2": datasets.Value("string"),
"POS": datasets.Value("string"),
"Sim1": datasets.Value("string"),
"Sim2": datasets.Value("string"),
"STD": datasets.Value("string"),
}
)
elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
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]:
"""Returns SplitGenerators."""
data_dir = dl_manager.download_and_extract(_URLS[_DATASETNAME])
return [
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": os.path.join(data_dir, "ViData/ViSim-400/Visim-400.txt"),
"split": "test",
},
)
]
def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
with open(filepath, "r", encoding="utf-8") as file:
lines = file.readlines()
data = []
for line in lines:
columns = line.strip().split("\t")
data.append(columns)
df = pd.DataFrame(data[1:], columns=data[0])
for index, row in df.iterrows():
if self.config.schema == "source":
example = row.to_dict()
elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
example = {
"id": str(index),
"text_1": str(row["Word1"]),
"text_2": str(row["Word2"]),
# I choose Sim2 instead of Sim1 because it's been normalized to [1, 10]
"label": str(row["Sim2"]),
}
yield index, example
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