Convert dataset to Parquet (#1)

- Convert dataset to Parquet (cb2ac75ec037a0d7f98c43f4d6563036f62476c6)
- Delete loading script (1fe2cf657425bcf60689b245e30ddd6704f870d1)

README.md

@@ -1,3 +1,22 @@
 ---
 license: apache-2.0
+dataset_info:
+  features:
+  - name: question_id
+    dtype: int64
+  - name: category
+    dtype: string
+  - name: turns
+    list: string
+  splits:
+  - name: test
+    num_bytes: 14357
+    num_examples: 80
+  download_size: 10129
+  dataset_size: 14357
+configs:
+- config_name: default
+  data_files:
+  - split: test
+    path: data/test-*
 ---

data/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1d842be894749e2068abc6f6650c494bcb48540a888f5d9ac106d6282dd7ecfb
+size 10129

ja-vicuna-qa-benchmark.py

@@ -1,67 +0,0 @@
-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