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import ast
from pathlib import Path
from typing import Dict, List, Tuple
import datasets
import pandas as pd
from seacrowd.sea_datasets.facqa.utils.facqa_utils import (getAnswerString, listToString)
from seacrowd.utils import schemas
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import Tasks
_CITATION = """
@inproceedings{purwarianti2007machine,
title={A Machine Learning Approach for Indonesian Question Answering System},
author={Ayu Purwarianti, Masatoshi Tsuchiya, and Seiichi Nakagawa},
booktitle={Proceedings of Artificial Intelligence and Applications },
pages={573--578},
year={2007}
}
"""
_LANGUAGES = ["ind"] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
_LOCAL = False
_DATASETNAME = "facqa"
_DESCRIPTION = """
FacQA: The goal of the FacQA dataset is to find the answer to a question from a provided short passage from a news article.
Each row in the FacQA dataset consists of a question, a short passage, and a label phrase, which can be found inside the
corresponding short passage. There are six categories of questions: date, location, name,
organization, person, and quantitative.
"""
_HOMEPAGE = "https://github.com/IndoNLP/indonlu"
_LICENSE = "CC-BY-SA 4.0"
_URLS = {
_DATASETNAME: {
"test": "https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/facqa_qa-factoid-itb/test_preprocess.csv",
"train": "https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/facqa_qa-factoid-itb/train_preprocess.csv",
"validation": "https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/facqa_qa-factoid-itb/valid_preprocess.csv",
}
}
_SUPPORTED_TASKS = [Tasks.QUESTION_ANSWERING]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class FacqaDataset(datasets.GeneratorBasedBuilder):
"""FacQA dataset is a labeled dataset for indonesian question answering task"""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
BUILDER_CONFIGS = [
SEACrowdConfig(
name="facqa_source",
version=SOURCE_VERSION,
description="FacQA source schema",
schema="source",
subset_id="facqa",
),
SEACrowdConfig(
name="facqa_seacrowd_qa",
version=SEACROWD_VERSION,
description="FacQA Nusantara schema",
schema="seacrowd_qa",
subset_id="facqa",
),
]
DEFAULT_CONFIG_NAME = "facqa_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"index": datasets.Value("int64"),
"question": [datasets.Value("string")],
"passage": [datasets.Value("string")],
"seq_label": [datasets.Value("string")],
}
)
elif self.config.schema == "seacrowd_qa":
features = schemas.qa_features
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_csv_path = Path(dl_manager.download_and_extract(urls["train"]))
validation_csv_path = Path(dl_manager.download_and_extract(urls["validation"]))
test_csv_path = Path(dl_manager.download_and_extract(urls["test"]))
data_files = {
"train": train_csv_path,
"validation": validation_csv_path,
"test": test_csv_path,
}
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": data_files["train"],
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": data_files["test"],
"split": "test",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": data_files["validation"],
"split": "dev",
},
),
]
def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
df = pd.read_csv(filepath, sep=",", header="infer").reset_index()
if self.config.schema == "source":
for row in df.itertuples():
entry = {"index": row.index, "question": ast.literal_eval(row.question), "passage": ast.literal_eval(row.passage), "seq_label": ast.literal_eval(row.seq_label)}
yield row.index, entry
elif self.config.schema == "seacrowd_qa":
for row in df.itertuples():
entry = {
"id": str(row.index),
"question_id": str(row.index),
"document_id": str(row.index),
"question": listToString(ast.literal_eval(row.question)),
"type": "extractive",
"choices": [],
"context": listToString(ast.literal_eval(row.passage)),
"answer": [getAnswerString(ast.literal_eval(row.passage), ast.literal_eval(row.seq_label))],
"meta": {}
}
yield row.index, entry
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