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# coding=utf-8
# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from pathlib import Path
from typing import Dict, List, Tuple
import datasets
from pandas import read_excel
from seacrowd.utils import schemas
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import TASK_TO_SCHEMA, Licenses, Tasks
_CITATION = """\
@inproceedings{koto-koto-2020-towards,
title = "Towards Computational Linguistics in {M}inangkabau Language:
Studies on Sentiment Analysis and Machine Translation",
author = "Koto, Fajri and
Koto, Ikhwan",
editor = "Nguyen, Minh Le and
Luong, Mai Chi and
Song, Sanghoun",
booktitle = "Proceedings of the 34th Pacific Asia Conference on Language,
Information and Computation",
month = oct,
year = "2020",
address = "Hanoi, Vietnam",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.paclic-1.17",
pages = "138--148",
}
"""
_DATASETNAME = "minang_senti"
_DESCRIPTION = """\
We release the Minangkabau corpus for sentiment analysis by manually translating
5,000 sentences of Indonesian sentiment analysis corpora. In this work, we
conduct a binary sentiment classification on positive and negative sentences by
first manually translating the Indonesian sentiment analysis corpus to the
Minangkabau language (Agam-Tanah Datar dialect)
"""
_HOMEPAGE = "https://github.com/fajri91/minangNLP"
_LANGUAGES = ["ind", "min"]
_LICENSE = Licenses.MIT.value
_LOCAL = False
_BASE_URL = "https://github.com/fajri91/minangNLP/raw/master/sentiment/data/folds/{split}{index}.xlsx"
_SUPPORTED_TASKS = [Tasks.SENTIMENT_ANALYSIS]
_SEACROWD_SCHEMA = f"seacrowd_{TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]].lower()}" # text
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class MinangSentiDataset(datasets.GeneratorBasedBuilder):
"""Binary sentiment classification on manually translated Minangkabau corpus."""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
BUILDER_CONFIGS = []
for subset in _LANGUAGES:
BUILDER_CONFIGS += [
SEACrowdConfig(
name=f"{_DATASETNAME}_{subset}_source",
version=SOURCE_VERSION,
description=f"{_DATASETNAME} {subset} source schema",
schema="source",
subset_id=subset,
),
SEACrowdConfig(
name=f"{_DATASETNAME}_{subset}_{_SEACROWD_SCHEMA}",
version=SEACROWD_VERSION,
description=f"{_DATASETNAME} {subset} SEACrowd schema",
schema=_SEACROWD_SCHEMA,
subset_id=subset,
),
]
DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_{_LANGUAGES[0]}_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"minang": datasets.Value("string"),
"indo": datasets.Value("string"),
"sentiment": datasets.ClassLabel(names=["positive", "negative"]),
}
)
elif self.config.schema == _SEACROWD_SCHEMA:
features = schemas.text_features(label_names=["positive", "negative"])
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."""
train_urls = [_BASE_URL.format(split="train", index=i) for i in range(5)]
test_urls = [_BASE_URL.format(split="test", index=i) for i in range(5)]
dev_urls = [_BASE_URL.format(split="dev", index=i) for i in range(5)]
train_paths = [Path(dl_manager.download(url)) for url in train_urls]
test_paths = [Path(dl_manager.download(url)) for url in test_urls]
dev_paths = [Path(dl_manager.download(url)) for url in dev_urls]
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": train_paths,
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": test_paths,
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": dev_paths,
},
),
]
def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
key = 0
for file in filepath:
data = read_excel(file)
for _, row in data.iterrows():
if self.config.schema == "source":
yield key, {
"minang": row["minang"],
"indo": row["indo"],
"sentiment": row["sentiment"],
}
elif self.config.schema == _SEACROWD_SCHEMA:
yield key, {
"id": str(key),
"text": row["minang"] if self.config.subset_id == "min" else row["indo"],
"label": row["sentiment"],
}
key += 1
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