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vigetext.py

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+from pathlib import Path
+
+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{10.1145/3628797.3628837,
+    author = {Nguyen, Duc-Vu and Nguyen, Quoc-Nam},
+    title = {Evaluating the Symbol Binding Ability of Large Language Models for Multiple-Choice Questions in Vietnamese General Education},
+    year = {2023},
+    isbn = {9798400708916},
+    publisher = {Association for Computing Machinery},
+    address = {New York, NY, USA},
+    url = {https://doi.org/10.1145/3628797.3628837},
+    doi = {10.1145/3628797.3628837},
+    booktitle = {Proceedings of the 12th International Symposium on Information and Communication Technology},
+    pages = {379–386},
+    numpages = {8},
+    keywords = {Analysis of Language Models, Multiple Choice Symbol Binding, Multiple Choice Question Answering, Language Modeling},
+    location = {<conf-loc>, <city>Ho Chi Minh</city>, <country>Vietnam</country>, </conf-loc>},
+    series = {SOICT '23}
+}
+"""
+
+_DATASETNAME = "vigetext"
+
+_DESCRIPTION = """
+The high-quality dataset with structured guidelines for typing LaTeX formulas in Mathematics, Physics, Chemistry, and
+Biology. Objective was to cover the entire scope of the Vietnamese General Education Examination spanning from 2017 to 2023.
+This comprehensive approach included the challenging examinations of the years 2017 and 2018, which have been significant
+for nearly all Vietnamese students in recent years. It is important to highlight that the exact and unquestionably correct
+answers have been exclusively obtained from the Vietnamese Ministry of Education.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/uitnlp/ViGEText_17to23"
+
+_LANGUAGES = ["vie"]
+
+_LICENSE = Licenses.UNKNOWN.value
+
+_LOCAL = False
+
+_URLS = {
+    _DATASETNAME: {
+        "train": "https://huggingface.co/datasets/uitnlp/ViGEText_17to23/resolve/main/data/train-00000-of-00001.parquet",
+        "validation": "https://huggingface.co/datasets/uitnlp/ViGEText_17to23/resolve/main/data/validation-00000-of-00001.parquet",
+        "test": "https://huggingface.co/datasets/uitnlp/ViGEText_17to23/resolve/main/data/test-00000-of-00001.parquet",
+    }
+}
+
+_SUPPORTED_TASKS = [Tasks.QUESTION_ANSWERING]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class VigetextDataset(datasets.GeneratorBasedBuilder):
+    """Vigetext is a dataset for evaluating the Symbol Binding Ability of Large Language Models for Multiple-Choice Questions in Vietnamese General Education."""
+
+    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=_DATASETNAME,
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_qa",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema="seacrowd_qa",
+            subset_id=_DATASETNAME,
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "input": datasets.Value("string"),
+                    "target": datasets.Value("string"),
+                }
+            )
+
+        else:
+            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]
+        data_dir = dl_manager.download_and_extract(urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": data_dir, "split": "train"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": data_dir, "split": "validation"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": data_dir, "split": "test"},
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> tuple[int, dict]:
+        df = pd.read_parquet(filepath[split])
+        data = df.to_dict(orient="records")
+        for i, item in enumerate(data):
+            if self.config.schema == "source":
+                yield i, {
+                    "id": item["id"],
+                    "input": item["input"],
+                    "target": item["target"],
+                }
+            else:
+                question_and_options = item["input"].split("\n")
+                answer_map = {opt[0]: opt[2:].strip() for opt in question_and_options[1:]}
+                yield i, {
+                    "id": str(i),
+                    "question_id": item["id"],
+                    "document_id": "",
+                    "question": question_and_options[0],
+                    "type": "multiple_choice",
+                    "choices": [opt[2:].strip() for opt in question_and_options[1:]],  # remove A., B., ... in the options
+                    "context": "",
+                    "answer": [answer_map[item["target"]]],
+                    "meta": {}
+                }