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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 = """\
@misc{
research,
title={Jakartaresearch/google-play-review · datasets at hugging face},
url={https://huggingface.co/datasets/jakartaresearch/google-play-review},
author={Research, Jakarta AI}
}
"""
_LANGUAGES = ["ind"] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
_LOCAL = False
_DATASETNAME = "id_google_play_review"
_DESCRIPTION = """\
Indonesian Google Play Review, dataset scrapped from e-commerce app on Google Play for sentiment analysis.
Total number of data: 10041 (train: 7028, validation: 3012). Provided by Jakarta AI Research.
"""
_HOMEPAGE = "https://github.com/jakartaresearch/hf-datasets/tree/main/google-play-review/google-play-review"
_LICENSE = "CC-BY 4.0"
_URLS = {
_DATASETNAME: {
"train": "https://media.githubusercontent.com/media/jakartaresearch/hf-datasets/main/google-play-review/google-play-review/train.csv",
"valid": "https://media.githubusercontent.com/media/jakartaresearch/hf-datasets/main/google-play-review/google-play-review/validation.csv",
}
}
_SUPPORTED_TASKS = [Tasks.SENTIMENT_ANALYSIS]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class IDGooglePlayReview(datasets.GeneratorBasedBuilder):
"""
Indonesian Google Play Review is a dataset containing reviews from Google Play Indonesia, used for sentiment
analysis.
The language content is mainly Indonesian, however beware of context-switching (some sentences are partly or
entirely in English).
The available labels:
label: ['pos', 'neg'] for source and seacrowd_text scheme
stars: [1, 2, 3, 4, 5] for source
"""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
BUILDER_CONFIGS = [
SEACrowdConfig(
name="id_google_play_review_source",
version=SOURCE_VERSION,
description="id_google_play_review source schema",
schema="source",
subset_id="id_google_play_review",
),
SEACrowdConfig(
name="id_google_play_review_posneg_source",
version=SOURCE_VERSION,
description="id_google_play_review source schema",
schema="source",
subset_id="id_google_play_review_posneg",
),
SEACrowdConfig(
name="id_google_play_review_seacrowd_text",
version=SEACROWD_VERSION,
description="id_google_play_review Nusantara schema, 1-5 stars rating only (for pos/neg labels, please use the subset_id \"id_google_play_review_posneg\")",
schema="seacrowd_text",
subset_id="id_google_play_review",
),
SEACrowdConfig(
name="id_google_play_review_posneg_seacrowd_text",
version=SEACROWD_VERSION,
description="id_google_play_review Nusantara schema, pos/neg label only",
schema="seacrowd_text",
subset_id="id_google_play_review_posneg",
),
]
DEFAULT_CONFIG_NAME = "id_google_play_review_source"
def _info(self) -> datasets.DatasetInfo:
# Create the source schema; this schema will keep all keys/information/labels as close to the original dataset
# as possible.
# You can arbitrarily nest lists and dictionaries.
# For iterables, use lists over tuples or `datasets.Sequence`
if self.config.schema == "source":
features = datasets.Features({
"text": datasets.Value("string"),
"label": datasets.Value("string"),
"stars": datasets.Value("int8")
})
elif self.config.schema == "seacrowd_text":
if self.config.subset_id == "id_google_play_review_posneg":
features = schemas.text_features(["pos", "neg"])
elif self.config.subset_id == "id_google_play_review":
features = schemas.text_features(["1", "2", "3", "4", "5"])
else:
raise ValueError(f"Invalid config: {self.config.name}")
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."""
urls = _URLS[_DATASETNAME]
train_data_path = Path(dl_manager.download(urls["train"]))
valid_data_path = Path(dl_manager.download(urls["valid"]))
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": train_data_path, "split": "train"},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"filepath": valid_data_path, "split": "valid"},
),
]
def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
df = pd.read_csv(filepath, sep=",").reset_index()
for row in df.itertuples(index=False):
if self.config.schema == "source":
example = {"text": row.text, "label": row.label, "stars": row.stars}
yield row.index, example
elif self.config.schema == "seacrowd_text":
if self.config.subset_id == "id_google_play_review_posneg":
example = {"id": row.index, "text": row.text, "label": row.label}
elif self.config.subset_id == "id_google_play_review":
example = {"id": row.index, "text": row.text, "label": str(row.stars)}
else:
raise ValueError(f"Invalid config: {self.config.name}")
yield row.index, example
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