File size: 5,592 Bytes
9ec4ec7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 |
# coding=utf-8
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{nguyen2022viheathqa,
author="Nguyen, Nhung Thi-Hong
and Ha, Phuong Phan-Dieu
and Nguyen, Luan Thanh
and Van Nguyen, Kiet
and Nguyen, Ngan Luu-Thuy",
title="SPBERTQA: A Two-Stage Question Answering System Based on Sentence Transformers for Medical Texts",
booktitle="Knowledge Science, Engineering and Management",
year="2022",
publisher="Springer International Publishing",
address="Cham",
pages="371--382",
isbn="978-3-031-10986-7"
}
"""
_DATASETNAME = "vihealthqa"
_DESCRIPTION = """\
Vietnamese Visual Question Answering (ViVQA) consist of 10328 images and 15000 question-answer
pairs in Vietnamese for evaluating Vietnamese VQA models. This dataset is built based on 10328 randomly
selected images from MS COCO dataset. The question-answer pairs were based on the COCO-QA dataset that
was automatically translated from English to Vietnamese.
"""
_HOMEPAGE = "https://huggingface.co/datasets/tarudesu/ViHealthQA"
_LANGUAGES = ["vie"]
_LICENSE = Licenses.UNKNOWN.value
_PAPER_URL = "https://link.springer.com/chapter/10.1007/978-3-031-10986-7_30"
_LOCAL = False
_URLS = {
"vihealthqa": {
"train": "https://huggingface.co/datasets/tarudesu/ViHealthQA/raw/main/train.csv",
"val": "https://huggingface.co/datasets/tarudesu/ViHealthQA/raw/main/val.csv",
"test": "https://huggingface.co/datasets/tarudesu/ViHealthQA/raw/main/test.csv",
}
}
_SUPPORTED_TASKS = [Tasks.QUESTION_ANSWERING]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class ViHealthQADataset(datasets.GeneratorBasedBuilder):
'''
This is a SeaCrowed dataloader for dataset Vietnamese Visual Question Answering (ViVQA), which consists of 10328 images and 15000 question-answer
pairs in Vietnamese for evaluating Vietnamese VQA models.
'''
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
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_qa",
version=SEACROWD_VERSION,
description=f"{_DATASETNAME} SEACrowd schema",
schema="seacrowd_qa",
subset_id=f"{_DATASETNAME}",
),
]
DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"id": datasets.Value("string"),
"question": datasets.Value("string"),
"answer": datasets.Value("string"),
"link": datasets.Value("string")
}
)
elif self.config.schema == "seacrowd_qa":
features = schemas.qa_features
features["meta"] = {"link": datasets.Value("string")}
else:
raise ValueError(f"No schema matched for {self.config.schema}")
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["vihealthqa"]
data_dir = dl_manager.download_and_extract(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": data_dir["train"],
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": data_dir["val"],
"split": "val",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": data_dir["test"],
"split": "test",
},
),
]
def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
raw_examples = pd.read_csv(filepath)
for eid, exam in raw_examples.iterrows():
assert len(exam) == 4
exam_id, exam_quest, exam_answer, exam_link = exam
if self.config.schema == "source":
yield eid, {"id": str(exam_id), "question": exam_quest, "answer": exam_answer, "link": exam_link}
elif self.config.schema == "seacrowd_qa":
yield eid, {
"id": str(eid),
"question_id": exam_id,
"document_id": str(eid),
"question": exam_quest,
"type": None,
"choices": [],
"context": exam_link,
"answer": [exam_answer],
"meta": {
"link": exam_link,
},
}
|