Datasets:
asnq

Task Categories: multiple-choice
Languages: English
Multilinguality: monolingual
Size Categories: 10M<n<100M
Language Creators: found
Annotations Creators: crowdsourced
Dataset Preview
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The dataset preview is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    FileNotFoundError
Message:      https://wqa-public.s3.amazonaws.com/tanda-aaai-2020/data/asnq.tar
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py", line 391, in _info
                  await _file_info(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py", line 772, in _file_info
                  r.raise_for_status()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/aiohttp/client_reqrep.py", line 1004, in raise_for_status
                  raise ClientResponseError(
              aiohttp.client_exceptions.ClientResponseError: 403, message='Forbidden', url=URL('https://wqa-public.s3.amazonaws.com/tanda-aaai-2020/data/asnq.tar')
              
              The above exception was the direct cause of the following exception:
              
              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 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 718, in __iter__
                  for key, example in self._iter():
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 708, in _iter
                  yield from ex_iterable
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 112, in __iter__
                  yield from self.generate_examples_fn(**self.kwargs)
                File "/tmp/modules-cache/datasets_modules/datasets/asnq/9d1e778b346e77ce06267638bbcb8745661548238317f5ebf8c821d530ac9fad/asnq.py", line 139, in _generate_examples
                  with open(filepath, encoding="utf-8") as tsvfile:
                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 453, in xopen
                  file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 441, in open
                  return 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 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/tar.py", line 41, in __init__
                  fo = self.of.open()  # keep the reference
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 135, in open
                  return self.__enter__()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 103, in __enter__
                  f = self.fs.open(self.path, mode=mode)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/spec.py", line 1034, in open
                  f = self._open(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py", line 340, in _open
                  size = size or self.info(path, **kwargs)["size"]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/asyn.py", line 111, in wrapper
                  return sync(self.loop, func, *args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/asyn.py", line 96, in sync
                  raise return_result
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/asyn.py", line 53, in _runner
                  result[0] = await coro
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py", line 404, in _info
                  raise FileNotFoundError(url) from exc
              FileNotFoundError: https://wqa-public.s3.amazonaws.com/tanda-aaai-2020/data/asnq.tar

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Dataset Card for "asnq"

Dataset Summary

ASNQ is a dataset for answer sentence selection derived from Google's Natural Questions (NQ) dataset (Kwiatkowski et al. 2019).

Each example contains a question, candidate sentence, label indicating whether or not the sentence answers the question, and two additional features -- sentence_in_long_answer and short_answer_in_sentence indicating whether ot not the candidate sentence is contained in the long_answer and if the short_answer is in the candidate sentence.

For more details please see https://arxiv.org/abs/1911.04118

and

https://research.google/pubs/pub47761/

Supported Tasks and Leaderboards

More Information Needed

Languages

More Information Needed

Dataset Structure

Data Instances

default

  • Size of downloaded dataset files: 3398.76 MB
  • Size of the generated dataset: 3647.70 MB
  • Total amount of disk used: 7046.46 MB

An example of 'validation' looks as follows.

{
    "label": 0,
    "question": "when did somewhere over the rainbow come out",
    "sentence": "In films and TV shows ( edit ) In the film Third Finger , Left Hand ( 1940 ) with Myrna Loy , Melvyn Douglas , and Raymond Walburn , the tune played throughout the film in short sequences .",
    "sentence_in_long_answer": false,
    "short_answer_in_sentence": false
}

Data Fields

The data fields are the same among all splits.

default

  • question: a string feature.
  • sentence: a string feature.
  • label: a classification label, with possible values including neg (0), pos (1).
  • sentence_in_long_answer: a bool feature.
  • short_answer_in_sentence: a bool feature.

Data Splits

name train validation
default 20377568 930062

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

More Information Needed

Annotations

Annotation process

More Information Needed

Who are the annotators?

More Information Needed

Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

More Information Needed

Other Known Limitations

More Information Needed

Additional Information

Dataset Curators

More Information Needed

Licensing Information

The data is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License: https://github.com/alexa/wqa_tanda/blob/master/LICENSE

Citation Information

@article{Garg_2020,
   title={TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection},
   volume={34},
   ISSN={2159-5399},
   url={http://dx.doi.org/10.1609/AAAI.V34I05.6282},
   DOI={10.1609/aaai.v34i05.6282},
   number={05},
   journal={Proceedings of the AAAI Conference on Artificial Intelligence},
   publisher={Association for the Advancement of Artificial Intelligence (AAAI)},
   author={Garg, Siddhant and Vu, Thuy and Moschitti, Alessandro},
   year={2020},
   month={Apr},
   pages={7780–7788}
}

Contributions

Thanks to @mkserge for adding this dataset.