Close SEACrowd#306 | Create dataset loader for ViHealthQA (SEACrowd#319)

* Create dataset loader for ViHealthQA SEACrowd#306

* add class docstring

* Update vihealthqa.py

seacrowd/sea_datasets/vihealthqa/vihealthqa.py

@@ -0,0 +1,157 @@
+# 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 = "1.0.0"
+
+
+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,
+                    },
+                }