Fix style in openbookqa dataset (#4270)

* Fix style in openbookqa dataset

* Fix style

* Fix dataset card

Commit from https://github.com/huggingface/datasets/commit/fbc3d1419aca2fc083cc2be11aa4d12ff2ba4399

README.md

@@ -1,11 +1,28 @@
 ---
+annotations_creators:
+- crowdsourced
+- expert-generated
+language_creators:
+- expert-generated
 languages:
 - en
-paperswithcode_id: openbookqa
+licenses:
+- unknown
+multilinguality:
+- monolingual
 pretty_name: OpenBookQA
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- question-answering
+task_ids:
+- open-domain-qa
+paperswithcode_id: openbookqa
 ---
 
-# Dataset Card for "openbookqa"
+# Dataset Card for OpenBookQA
 
 ## Table of Contents
 - [Dataset Description](#dataset-description)

openbookqa.py

@@ -39,12 +39,9 @@ class OpenbookqaConfig(datasets.BuilderConfig):
         Args:
           data_dir: directory for the given dataset name
           **kwargs: keyword arguments forwarded to super.
-
         """
 
-        super(OpenbookqaConfig, self).__init__(
-            version=datasets.Version("1.0.0", ""), **kwargs
-        )
+        super().__init__(version=datasets.Version("1.0.0", ""), **kwargs)
 
         self.data_dir = data_dir
 
@@ -58,25 +55,25 @@ class Openbookqa(datasets.GeneratorBasedBuilder):
         OpenbookqaConfig(
             name="main",
             description=textwrap.dedent(
+                """\
+                It consists of 5,957 multiple-choice elementary-level science questions (4,957 train, 500 dev, 500 test),
+                which probe the understanding of a small “book” of 1,326 core science facts and the application of these facts to novel
+                situations. For training, the dataset includes a mapping from each question to the core science fact it was designed to
+                probe. Answering OpenBookQA questions requires additional broad common knowledge, not contained in the book. The questions,
+                by design, are answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm. Strong neural
+                baselines achieve around 50% on OpenBookQA, leaving a large gap to the 92% accuracy of crowd-workers.
                 """
-                                  It consists of 5,957 multiple-choice elementary-level science questions (4,957 train, 500 dev, 500 test),
-                                  which probe the understanding of a small “book” of 1,326 core science facts and the application of these facts to novel
-                                  situations. For training, the dataset includes a mapping from each question to the core science fact it was designed to
-                                  probe. Answering OpenBookQA questions requires additional broad common knowledge, not contained in the book. The questions,
-                                  by design, are answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm. Strong neural
-                                  baselines achieve around 50% on OpenBookQA, leaving a large gap to the 92% accuracy of crowd-workers.
-                                """
             ),
             data_dir="Main",
         ),
         OpenbookqaConfig(
             name="additional",
             description=textwrap.dedent(
+                """\
+                Additionally, we provide 5,167 crowd-sourced common knowledge facts, and an expanded version of the train/dev/test questions where
+                each question is associated with its originating core fact, a human accuracy score, a clarity score, and an anonymized crowd-worker
+                ID (in the 'Additional' folder).
                 """
-                                  Additionally, we provide 5,167 crowd-sourced common knowledge facts, and an expanded version of the train/dev/test questions where
-                                  each question is associated with its originating core fact, a human accuracy score, a clarity score, and an anonymized crowd-worker
-                                  ID (in the “Additional” folder).
-                                """
             ),
             data_dir="Additional",
         ),
@@ -162,12 +159,8 @@ class Openbookqa(datasets.GeneratorBasedBuilder):
                     "id": data["id"],
                     "question_stem": data["question"]["stem"],
                     "choices": {
-                        "text": [
-                            choice["text"] for choice in data["question"]["choices"]
-                        ],
-                        "label": [
-                            choice["label"] for choice in data["question"]["choices"]
-                        ],
+                        "text": [choice["text"] for choice in data["question"]["choices"]],
+                        "label": [choice["label"] for choice in data["question"]["choices"]],
                     },
                     "answerKey": data["answerKey"],
                 }