albertvillanova HF staff commited on
Commit
ffefa2b
1 Parent(s): 4ebd011

Fix style in openbookqa dataset (#4270)

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* Fix style in openbookqa dataset

* Fix style

* Fix dataset card

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

Files changed (2) hide show
  1. README.md +19 -2
  2. openbookqa.py +14 -21
README.md CHANGED
@@ -1,11 +1,28 @@
1
  ---
 
 
 
 
 
2
  languages:
3
  - en
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- paperswithcode_id: openbookqa
 
 
 
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  pretty_name: OpenBookQA
 
 
 
 
 
 
 
 
 
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  ---
7
 
8
- # Dataset Card for "openbookqa"
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10
  ## Table of Contents
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  - [Dataset Description](#dataset-description)
 
1
  ---
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+ annotations_creators:
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+ - crowdsourced
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+ - expert-generated
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+ language_creators:
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+ - expert-generated
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  languages:
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  - en
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+ licenses:
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+ - unknown
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+ multilinguality:
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+ - monolingual
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  pretty_name: OpenBookQA
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+ size_categories:
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+ - 1K<n<10K
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+ source_datasets:
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+ - original
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+ task_categories:
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+ - question-answering
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+ task_ids:
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+ - open-domain-qa
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+ paperswithcode_id: openbookqa
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  ---
24
 
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+ # Dataset Card for OpenBookQA
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27
  ## Table of Contents
28
  - [Dataset Description](#dataset-description)
openbookqa.py CHANGED
@@ -39,12 +39,9 @@ class OpenbookqaConfig(datasets.BuilderConfig):
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  Args:
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  data_dir: directory for the given dataset name
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  **kwargs: keyword arguments forwarded to super.
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-
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  """
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45
- super(OpenbookqaConfig, self).__init__(
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- version=datasets.Version("1.0.0", ""), **kwargs
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- )
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  self.data_dir = data_dir
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@@ -58,25 +55,25 @@ class Openbookqa(datasets.GeneratorBasedBuilder):
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  OpenbookqaConfig(
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  name="main",
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  description=textwrap.dedent(
 
 
 
 
 
 
 
61
  """
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- It consists of 5,957 multiple-choice elementary-level science questions (4,957 train, 500 dev, 500 test),
63
- which probe the understanding of a small “book” of 1,326 core science facts and the application of these facts to novel
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- situations. For training, the dataset includes a mapping from each question to the core science fact it was designed to
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- probe. Answering OpenBookQA questions requires additional broad common knowledge, not contained in the book. The questions,
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- by design, are answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm. Strong neural
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- baselines achieve around 50% on OpenBookQA, leaving a large gap to the 92% accuracy of crowd-workers.
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- """
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  ),
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  data_dir="Main",
71
  ),
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  OpenbookqaConfig(
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  name="additional",
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  description=textwrap.dedent(
 
 
 
 
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  """
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- Additionally, we provide 5,167 crowd-sourced common knowledge facts, and an expanded version of the train/dev/test questions where
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- each question is associated with its originating core fact, a human accuracy score, a clarity score, and an anonymized crowd-worker
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- ID (in the “Additional” folder).
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- """
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  ),
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  data_dir="Additional",
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  ),
@@ -162,12 +159,8 @@ class Openbookqa(datasets.GeneratorBasedBuilder):
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  "id": data["id"],
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  "question_stem": data["question"]["stem"],
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  "choices": {
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- "text": [
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- choice["text"] for choice in data["question"]["choices"]
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- ],
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- "label": [
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- choice["label"] for choice in data["question"]["choices"]
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- ],
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  },
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  "answerKey": data["answerKey"],
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  }
 
39
  Args:
40
  data_dir: directory for the given dataset name
41
  **kwargs: keyword arguments forwarded to super.
 
42
  """
43
 
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+ super().__init__(version=datasets.Version("1.0.0", ""), **kwargs)
 
 
45
 
46
  self.data_dir = data_dir
47
 
 
55
  OpenbookqaConfig(
56
  name="main",
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  description=textwrap.dedent(
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+ """\
59
+ It consists of 5,957 multiple-choice elementary-level science questions (4,957 train, 500 dev, 500 test),
60
+ which probe the understanding of a small “book” of 1,326 core science facts and the application of these facts to novel
61
+ situations. For training, the dataset includes a mapping from each question to the core science fact it was designed to
62
+ probe. Answering OpenBookQA questions requires additional broad common knowledge, not contained in the book. The questions,
63
+ by design, are answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm. Strong neural
64
+ baselines achieve around 50% on OpenBookQA, leaving a large gap to the 92% accuracy of crowd-workers.
65
  """
 
 
 
 
 
 
 
66
  ),
67
  data_dir="Main",
68
  ),
69
  OpenbookqaConfig(
70
  name="additional",
71
  description=textwrap.dedent(
72
+ """\
73
+ Additionally, we provide 5,167 crowd-sourced common knowledge facts, and an expanded version of the train/dev/test questions where
74
+ each question is associated with its originating core fact, a human accuracy score, a clarity score, and an anonymized crowd-worker
75
+ ID (in the 'Additional' folder).
76
  """
 
 
 
 
77
  ),
78
  data_dir="Additional",
79
  ),
 
159
  "id": data["id"],
160
  "question_stem": data["question"]["stem"],
161
  "choices": {
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+ "text": [choice["text"] for choice in data["question"]["choices"]],
163
+ "label": [choice["label"] for choice in data["question"]["choices"]],
 
 
 
 
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  },
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  "answerKey": data["answerKey"],
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  }