Closes SEACrowd#275 | Create dataset loader for UIT-ViCoV19QA SEACrow…

…d#275 (SEACrowd#463)

* add SeaCrowd dataloader for uit_vicov19qa

* Merge subsets to one

* remove unused imported package

seacrowd/sea_datasets/uit_vicov19qa/uit_vicov19qa.py

@@ -0,0 +1,167 @@
+# coding=utf-8
+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{thai-etal-2022-uit,
+title = "{UIT}-{V}i{C}o{V}19{QA}: A Dataset for {COVID}-19 Community-based Question Answering on {V}ietnamese Language",
+author = "Thai, Triet and Thao-Ha, Ngan Chu and Vo, Anh  and Luu, Son",
+editor = "Dita, Shirley and Trillanes, Arlene and Lucas, Rochelle Irene",
+booktitle = "Proceedings of the 36th Pacific Asia Conference on Language, Information and Computation",
+month = oct,
+year = "2022",
+address = "Manila, Philippines",
+publisher = "Association for Computational Linguistics",
+url = "https://aclanthology.org/2022.paclic-1.88",
+pages = "801--810",
+}
+"""
+_DATASETNAME = "uit_vicov19qa"
+_DESCRIPTION = """\
+UIT-ViCoV19QA is the first Vietnamese community-based question answering dataset for developing question answering
+systems for COVID-19. The dataset comprises 4,500 question-answer pairs collected from trusted medical sources,
+with at least one answer and at most four unique paraphrased answers per question. This dataset contains 1800 questions
+that have at least two answers, 700 questions have at least three answers and half of them have a maximum of four paraphrased
+answers.
+"""
+_HOMEPAGE = "https://github.com/triet2397/UIT-ViCoV19QA"
+_LANGUAGES = ["vie"]
+_LICENSE = Licenses.UNKNOWN.value
+_PAPER_URL = "https://aclanthology.org/2022.paclic-1.88"
+_LOCAL = False
+_URLS = {
+    "train": {
+        "1_ans": "https://raw.githubusercontent.com/triet2397/UIT-ViCoV19QA/main/dataset/1_ans/UIT-ViCoV19QA_train.csv",
+        "2_ans": "https://raw.githubusercontent.com/triet2397/UIT-ViCoV19QA/main/dataset/2_ans/UIT-ViCoV19QA_train.csv",
+        "3_ans": "https://raw.githubusercontent.com/triet2397/UIT-ViCoV19QA/main/dataset/3_ans/UIT-ViCoV19QA_train.csv",
+        "4_ans": "https://raw.githubusercontent.com/triet2397/UIT-ViCoV19QA/main/dataset/4_ans/UIT-ViCoV19QA_train.csv",
+    },
+    "val": {
+        "1_ans": "https://raw.githubusercontent.com/triet2397/UIT-ViCoV19QA/main/dataset/1_ans/UIT-ViCoV19QA_val.csv",
+        "2_ans": "https://raw.githubusercontent.com/triet2397/UIT-ViCoV19QA/main/dataset/2_ans/UIT-ViCoV19QA_val.csv",
+        "3_ans": "https://raw.githubusercontent.com/triet2397/UIT-ViCoV19QA/main/dataset/3_ans/UIT-ViCoV19QA_val.csv",
+        "4_ans": "https://raw.githubusercontent.com/triet2397/UIT-ViCoV19QA/main/dataset/4_ans/UIT-ViCoV19QA_val.csv",
+    },
+    "test": {
+        "1_ans": "https://raw.githubusercontent.com/triet2397/UIT-ViCoV19QA/main/dataset/1_ans/UIT-ViCoV19QA_test.csv",
+        "2_ans": "https://raw.githubusercontent.com/triet2397/UIT-ViCoV19QA/main/dataset/2_ans/UIT-ViCoV19QA_test.csv",
+        "3_ans": "https://raw.githubusercontent.com/triet2397/UIT-ViCoV19QA/main/dataset/3_ans/UIT-ViCoV19QA_test.csv",
+        "4_ans": "https://raw.githubusercontent.com/triet2397/UIT-ViCoV19QA/main/dataset/4_ans/UIT-ViCoV19QA_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 uit_vicov19qa, The dataset comprises 4,500 question-answer pairs collected from trusted medical sources,
+    with at least one answer and at most four unique paraphrased answers per question.
+    """
+
+    subsets = ["1_ans", "2_ans", "3_ans", "4_ans"]
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description=f"{_DATASETNAME} source schema",
+            schema="source", subset_id=f"{_DATASETNAME}"),
+
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_qa",
+            version=datasets.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"),
+                    "answers": datasets.Value("string"),
+                }
+            )
+        elif self.config.schema == "seacrowd_qa":
+            features = schemas.qa_features
+        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."""
+
+        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: Dict, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        print(f"Generating examples for split {split}")
+        sample_id = -1
+        for path in filepath.values():
+            raw_examples = pd.read_csv(path, na_filter=False, delimiter="|")
+            for eid, exam in raw_examples.iterrows():
+                sample_id += 1
+                exam_id = exam[0]
+                exam_quest = exam[1]
+                exam_answers = exam[2:].values
+                if self.config.schema == "source":
+                    yield sample_id, {"id": str(exam_id),
+                                      "question": exam_quest,
+                                      "answers": exam_answers
+                                      }
+
+                elif self.config.schema == "seacrowd_qa":
+                    yield sample_id, {"id": str(sample_id),
+                                      "question_id": exam_id,
+                                      "document_id": str(sample_id),
+                                      "question": exam_quest,
+                                      "type": None,
+                                      "choices": [],
+                                      "context": None,
+                                      "answer": exam_answers,
+                                      "meta": {}
+                                      }