Convert dataset to Parquet

README.md

@@ -45,6 +45,7 @@ task_ids:
 paperswithcode_id: mkqa
 pretty_name: Multilingual Knowledge Questions and Answers
 dataset_info:
+  config_name: mkqa
   features:
   - name: example_id
     dtype: string
@@ -626,13 +627,18 @@ dataset_info:
         dtype: string
       - name: aliases
         list: string
-  config_name: mkqa
   splits:
   - name: train
-    num_bytes: 36005650
+    num_bytes: 35957889
     num_examples: 10000
-  download_size: 11903948
-  dataset_size: 36005650
+  download_size: 19871622
+  dataset_size: 35957889
+configs:
+- config_name: mkqa
+  data_files:
+  - split: train
+    path: mkqa/train-*
+  default: true
 ---
 
 # Dataset Card for MKQA: Multilingual Knowledge Questions & Answers

mkqa.py

@@ -1,149 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""MKQA: Multilingual Knowledge Questions & Answers"""
-
-
-import json
-
-import datasets
-
-
-_CITATION = """\
-@misc{mkqa,
-    title = {MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering},
-    author = {Shayne Longpre and Yi Lu and Joachim Daiber},
-    year = {2020},
-    URL = {https://arxiv.org/pdf/2007.15207.pdf}
-}
-"""
-
-_DESCRIPTION = """\
-We introduce MKQA, an open-domain question answering evaluation set comprising 10k question-answer pairs sampled from the Google Natural Questions dataset, aligned across 26 typologically diverse languages (260k question-answer pairs in total). For each query we collected new passage-independent answers. These queries and answers were then human translated into 25 Non-English languages.
-"""
-_HOMEPAGE = "https://github.com/apple/ml-mkqa"
-_LICENSE = "CC BY-SA 3.0"
-
-
-_URLS = {"train": "https://github.com/apple/ml-mkqa/raw/main/dataset/mkqa.jsonl.gz"}
-
-
-class Mkqa(datasets.GeneratorBasedBuilder):
-    """MKQA dataset"""
-
-    VERSION = datasets.Version("1.0.0")
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="mkqa",
-            version=VERSION,
-            description=_DESCRIPTION,
-        ),
-    ]
-
-    def _info(self):
-        langs = [
-            "ar",
-            "da",
-            "de",
-            "en",
-            "es",
-            "fi",
-            "fr",
-            "he",
-            "hu",
-            "it",
-            "ja",
-            "ko",
-            "km",
-            "ms",
-            "nl",
-            "no",
-            "pl",
-            "pt",
-            "ru",
-            "sv",
-            "th",
-            "tr",
-            "vi",
-            "zh_cn",
-            "zh_hk",
-            "zh_tw",
-        ]
-
-        # Preferring list type instead of datasets.Sequence
-        queries_features = {lan: datasets.Value("string") for lan in langs}
-        answer_feature = [
-            {
-                "type": datasets.ClassLabel(
-                    names=[
-                        "entity",
-                        "long_answer",
-                        "unanswerable",
-                        "date",
-                        "number",
-                        "number_with_unit",
-                        "short_phrase",
-                        "binary",
-                    ]
-                ),
-                "entity": datasets.Value("string"),
-                "text": datasets.Value("string"),
-                "aliases": [datasets.Value("string")],
-            }
-        ]
-        answer_features = {lan: answer_feature for lan in langs}
-
-        features = datasets.Features(
-            {
-                "example_id": datasets.Value("string"),
-                "queries": queries_features,
-                "query": datasets.Value("string"),
-                "answers": answer_features,
-            }
-        )
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # download and extract URLs
-        urls_to_download = _URLS
-        downloaded_files = dl_manager.download_and_extract(urls_to_download)
-
-        return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]})]
-
-    def _generate_examples(self, filepath):
-        """Yields examples."""
-        with open(filepath, encoding="utf-8") as f:
-            for row in f:
-                data = json.loads(row)
-                data["example_id"] = str(data["example_id"])
-                id_ = data["example_id"]
-                for language in data["answers"].keys():
-                    # Add default values for possible missing keys
-                    for a in data["answers"][language]:
-                        if "aliases" not in a:
-                            a["aliases"] = []
-                        if "entity" not in a:
-                            a["entity"] = ""
-
-                yield id_, data

mkqa/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aa5d4e6685777784c1dda9b89066eb051b5199a1b6ce6232f9adb24ff62703bd
+size 19871622