add the source code for dataset

ja-vicuna-qa-benchmark.py

@@ -0,0 +1,67 @@
+from __future__ import annotations
+
+import json
+from typing import Any, Generator
+
+import datasets
+
+_CITATION = ""
+_DESCRIPTION = "These are datasets including the benchmark 'ja-vicuna-qa-benchmark.'"
+_HOMEPAGE = "https://raw.githubusercontent.com/ku-nlp/ja-vicuna-qa-benchmark/main/data/jp_bench/question.jsonl"
+_LICENSE = "This work is license under Apache-2.0 license"
+_URL = "https://raw.githubusercontent.com/ku-nlp/ja-vicuna-qa-benchmark/main/data/jp_bench/question.jsonl"
+_VERSION = "1.1.0"
+
+
+class JaVicunaQaBenchmarkConfig(datasets.BuilderConfig):
+    def __init__(
+        self,
+        name: str = "default",
+        version: datasets.Version | str | None = datasets.Version(_VERSION),
+        data_dir: str | None = None,
+        data_files: datasets.data_files.DataFilesDict | None = None,
+        description: str | None = _DESCRIPTION,
+    ) -> None:
+        super().__init__(
+            name=name,
+            version=version,
+            data_dir=data_dir,
+            data_files=data_files,
+            description=description,
+        )
+
+
+class JaVicunaQaBenchmark(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIG_CLASS = JaVicunaQaBenchmarkConfig
+
+    def _info(self) -> datasets.DatasetInfo:
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            citation=_CITATION,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            features=datasets.Features(
+                {
+                    "question_id": datasets.Value("int64"),
+                    "category": datasets.Value("string"),
+                    "turns": [datasets.Value("string")],
+                }
+            ),
+        )
+
+    def _split_generators(
+        self, dl_manager: datasets.DownloadManager
+    ) -> list[datasets.SplitGenerator]:
+        dataset_file = dl_manager.download_and_extract(_URL)
+        with open(dataset_file, "r", encoding="utf-8") as f:
+            data = [json.loads(line) for line in f]
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST, gen_kwargs={"data": data}
+            ),
+        ]
+
+    def _generate_examples(self, data: list[dict[str, Any]]) -> Generator:
+        for i, d in enumerate(data):
+            yield i, d