Datasets:
search_qa

Task Categories: question-answering
Languages: English
Multilinguality: monolingual
Size Categories: 100K<n<1M
Language Creators: found
Annotations Creators: found
Source Datasets: original
Licenses: unknown
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Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    BadZipFile
Message:      File is not a zip file
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/responses/first_rows.py", line 337, in get_first_rows_response
                  rows = get_rows(dataset, config, split, streaming=True, rows_max_number=rows_max_number, hf_token=hf_token)
                File "/src/services/worker/src/worker/utils.py", line 123, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/responses/first_rows.py", line 65, in get_rows
                  ds = load_dataset(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1739, in load_dataset
                  return builder_instance.as_streaming_dataset(split=split)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1025, in as_streaming_dataset
                  splits_generators = {sg.name: sg for sg in self._split_generators(dl_manager)}
                File "/tmp/modules-cache/datasets_modules/datasets/search_qa/31ad60d4a9f914d3ec2e41a4ce4b7cf959de758787379b96286d697799f18b59/search_qa.py", line 127, in _split_generators
                  if os.path.isdir(os.path.join(file_path, zip_folder)):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/streaming.py", line 67, in wrapper
                  return function(*args, use_auth_token=use_auth_token, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 305, in xisdir
                  fs, *_ = fsspec.get_fs_token_paths(path, storage_options=storage_options)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 606, in get_fs_token_paths
                  fs = filesystem(protocol, **inkwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/registry.py", line 268, in filesystem
                  return cls(**storage_options)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/spec.py", line 76, in __call__
                  obj = super().__call__(*args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/zip.py", line 49, in __init__
                  files = open_files(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 273, in open_files
                  fs, fs_token, paths = get_fs_token_paths(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 639, in get_fs_token_paths
                  fs = cls(**options)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/spec.py", line 76, in __call__
                  obj = super().__call__(*args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/zip.py", line 60, in __init__
                  self.zip = zipfile.ZipFile(self.fo, mode=mode)
                File "/usr/local/lib/python3.9/zipfile.py", line 1257, in __init__
                  self._RealGetContents()
                File "/usr/local/lib/python3.9/zipfile.py", line 1324, in _RealGetContents
                  raise BadZipFile("File is not a zip file")
              zipfile.BadZipFile: File is not a zip file

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Dataset Card for "search_qa"

Dataset Summary

We publicly release a new large-scale dataset, called SearchQA, for machine comprehension, or question-answering. Unlike recently released datasets, such as DeepMind CNN/DailyMail and SQuAD, the proposed SearchQA was constructed to reflect a full pipeline of general question-answering. That is, we start not from an existing article and generate a question-answer pair, but start from an existing question-answer pair, crawled from J! Archive, and augment it with text snippets retrieved by Google. Following this approach, we built SearchQA, which consists of more than 140k question-answer pairs with each pair having 49.6 snippets on average. Each question-answer-context tuple of the SearchQA comes with additional meta-data such as the snippet's URL, which we believe will be valuable resources for future research. We conduct human evaluation as well as test two baseline methods, one simple word selection and the other deep learning based, on the SearchQA. We show that there is a meaningful gap between the human and machine performances. This suggests that the proposed dataset could well serve as a benchmark for question-answering.

Supported Tasks and Leaderboards

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Languages

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Dataset Structure

Data Instances

raw_jeopardy

  • Size of downloaded dataset files: 3160.84 MB
  • Size of the generated dataset: 7410.98 MB
  • Total amount of disk used: 10571.82 MB

An example of 'train' looks as follows.


train_test_val

  • Size of downloaded dataset files: 3002.69 MB
  • Size of the generated dataset: 7162.78 MB
  • Total amount of disk used: 10165.47 MB

An example of 'validation' looks as follows.


Data Fields

The data fields are the same among all splits.

raw_jeopardy

  • category: a string feature.
  • air_date: a string feature.
  • question: a string feature.
  • value: a string feature.
  • answer: a string feature.
  • round: a string feature.
  • show_number: a int32 feature.
  • search_results: a dictionary feature containing:
    • urls: a string feature.
    • snippets: a string feature.
    • titles: a string feature.
    • related_links: a string feature.

train_test_val

  • category: a string feature.
  • air_date: a string feature.
  • question: a string feature.
  • value: a string feature.
  • answer: a string feature.
  • round: a string feature.
  • show_number: a int32 feature.
  • search_results: a dictionary feature containing:
    • urls: a string feature.
    • snippets: a string feature.
    • titles: a string feature.
    • related_links: a string feature.

Data Splits

raw_jeopardy

train
raw_jeopardy 216757

train_test_val

train validation test
train_test_val 151295 21613 43228

Dataset Creation

Curation Rationale

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Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotations

Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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Citation Information

@article{DBLP:journals/corr/DunnSHGCC17,
    author    = {Matthew Dunn and
                Levent Sagun and
                Mike Higgins and
                V. Ugur G{"{u}}ney and
                Volkan Cirik and
                Kyunghyun Cho},
    title     = {SearchQA: {A} New Q{\&}A Dataset Augmented with Context from a
                Search Engine},
    journal   = {CoRR},
    volume    = {abs/1704.05179},
    year      = {2017},
    url       = {http://arxiv.org/abs/1704.05179},
    archivePrefix = {arXiv},
    eprint    = {1704.05179},
    timestamp = {Mon, 13 Aug 2018 16:47:09 +0200},
    biburl    = {https://dblp.org/rec/journals/corr/DunnSHGCC17.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
}

Contributions

Thanks to @lewtun, @mariamabarham, @lhoestq, @thomwolf for adding this dataset.

Models trained or fine-tuned on search_qa