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import os
from pathlib import Path
from typing import Dict, List, Tuple
import datasets
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import Tasks
from seacrowd.utils import schemas
import pandas as pd
_CITATION = """\
@inproceedings{wongso2021causal,
title={Causal and masked language modeling of Javanese language using transformer-based architectures},
author={Wongso, Wilson and Setiawan, David Samuel and Suhartono, Derwin},
booktitle={2021 International Conference on Advanced Computer Science and Information Systems (ICACSIS)},
pages={1--7},
year={2021},
organization={IEEE}
}
"""
_DATASETNAME = "imdb_jv"
_DESCRIPTION = """\
Javanese Imdb Movie Reviews Dataset is a Javanese version of the IMDb Movie Reviews dataset by translating the original English dataset to Javanese.
"""
_HOMEPAGE = "https://huggingface.co./datasets/w11wo/imdb-javanese"
_LANGUAGES = ["ind"] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
_LOCAL = False
_LICENSE = "Unknown"
_URLS = {
_DATASETNAME: "https://huggingface.co./datasets/w11wo/imdb-javanese/resolve/main/javanese_imdb_csv.zip",
}
_SUPPORTED_TASKS = [Tasks.SENTIMENT_ANALYSIS]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class IMDbJv(datasets.GeneratorBasedBuilder):
"""Javanese Imdb Movie Reviews Dataset is a Javanese version of the IMDb Movie Reviews dataset by translating the original English dataset to Javanese."""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
BUILDER_CONFIGS = [
SEACrowdConfig(
name="imdb_jv_source",
version=datasets.Version(_SOURCE_VERSION),
description="imdb_jv source schema",
schema="source",
subset_id="imdb_jv",
),
SEACrowdConfig(
name="imdb_jv_seacrowd_text",
version=datasets.Version(_SEACROWD_VERSION),
description="imdb_jv Nusantara schema",
schema="seacrowd_text",
subset_id="imdb_jv",
),
]
DEFAULT_CONFIG_NAME = "imdb_jv_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"id": datasets.Value("string"),
"text": datasets.Value("string"),
"label": datasets.Value("string")
}
)
elif self.config.schema == "seacrowd_text":
features = schemas.text_features(['1', '0', '-1'])
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
data_dir = Path(dl_manager.download_and_extract(_URLS[_DATASETNAME]))
data_files = {
"train": "javanese_imdb_train.csv",
"unsupervised": "javanese_imdb_unsup.csv",
"test": "javanese_imdb_test.csv",
}
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": os.path.join(data_dir, data_files["train"]),
"split": "train",
},
),
datasets.SplitGenerator(
name="unsupervised",
gen_kwargs={
"filepath": os.path.join(data_dir, data_files["unsupervised"]),
"split": "unsupervised",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": os.path.join(data_dir, data_files["test"]),
"split": "test",
},
),
]
def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
if self.config.schema == "source":
data = pd.read_csv(filepath)
length = len(data['label'])
for id in range(length):
ex = {
"id": str(id),
"text": data['text'][id],
"label": data['label'][id],
}
yield id, ex
elif self.config.schema == "seacrowd_text":
data = pd.read_csv(filepath)
length = len(data['label'])
for id in range(length):
ex = {
"id": str(id),
"text": data['text'][id],
"label": data['label'][id],
}
yield id, ex
else:
raise ValueError(f"Invalid config: {self.config.name}")
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