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The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ValueError
Message:      Not able to read records in the JSON file at hf://datasets/CZLC/SQAD_3.2@8080865c21d9054db4241c518ac0784a2d14d0ba/data_json/train.jsonl. You should probably indicate the field of the JSON file containing your records. This JSON file contain the following fields: ['version', 'data']. Select the correct one and provide it as `field='XXX'` to the dataset loading method. 
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 240, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2216, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1239, in _head
                  return _examples_to_batch(list(self.take(n)))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1389, in __iter__
                  for key, example in ex_iterable:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1044, in __iter__
                  yield from islice(self.ex_iterable, self.n)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 282, in __iter__
                  for key, pa_table in self.generate_tables_fn(**self.kwargs):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 170, in _generate_tables
                  raise ValueError(
              ValueError: Not able to read records in the JSON file at hf://datasets/CZLC/SQAD_3.2@8080865c21d9054db4241c518ac0784a2d14d0ba/data_json/train.jsonl. You should probably indicate the field of the JSON file containing your records. This JSON file contain the following fields: ['version', 'data']. Select the correct one and provide it as `field='XXX'` to the dataset loading method.

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YAML Metadata Warning: empty or missing yaml metadata in repo card (https://huggingface.co/docs/hub/datasets-cards)

Simple Question Answering Database (SQAD)

SQAD is a Czech database for question answering developed in NLP laboratory at Masaryk University. Database have been harvested from Czech Wikipedia articles by students and annotated with appropriate question, answer sentence, exact answer, question type and answer type. Part of speech tagging and lemmatization by Majka pipeline.

Each record consist of several files:

  • 01question.vert: contains a question in vertical format
  • 03text.vert: contains full text of Wikipedia article in vertical format. Or can be represented by symbolic link to another record that also uses the same text. This feature is designed for saving the disk space.
  • 04url.txt: stores original URL address
  • 05metadata.txt: consists of several XML like lines that encodes important metadata
    • <q_type>: question type
    • <a_type>: answer type
    • <user>: name of the user that creates the record
  • 06answer.selection.vert: contains an sentence from the document that can answer the input question in vertical format
  • 09answer_extraction.vert: includes a sub-string from answer selection sentence in vertical format. This sub-string is informative enough to answer the input question.
  • 10title.vert: contains the title of the article itself in vertical format.

SQAD have been developed as a part of dissertation and is used in Automatic Question Answering (AQA) system.

SQAD to databse submodule cloning:

git submodule init
git submodule update

Please cite:

  • HORÁK, Aleš a Marek MEDVEĎ. SQAD: Simple Question Answering Database. In Eighth Workshop on Recent Advances in Slavonic Natural Language Processing. Brno: Tribun EU, 2014. s. 121-128. ISSN 2336-4289.
@inproceedings{1210707,
   author = {Horák, Aleš and Medveď, Marek},
   address = {Brno},
   booktitle = {Eighth Workshop on Recent Advances in Slavonic Natural Language Processing},
   keywords = {question answering; Simple Question Answering Database; SQAD; syntax-based question answering; SBQA},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Brno},
   pages = {121-128},
   publisher = {Tribun EU},
   title = {SQAD: Simple Question Answering Database},
   year = {2014}
}
  • MEDVEĎ, Marek a Aleš HORÁK. AQA: Automatic Question Answering System for Czech. In Sojka Petr, Horák Aleš, Kopeček Ivan, Pala Karel. Text, Speech, and Dialogue 19th International Conference, TSD 2016 Brno, Czech Republic, September 12–16, 2016 Proceedings. Switzerland: Springer International Publishing, 2016. s. 270-278. ISBN 978-3-319-45510-5. doi:10.1007/978-3-319-45510-5_31.
@inproceedings{1353405,
   author = {Medveď, Marek and Horák, Aleš},
   address = {Switzerland},
   booktitle = {Text, Speech, and Dialogue 19th International Conference, TSD 2016 Brno, Czech Republic, September 12–16, 2016 Proceedings},
   doi = {http://dx.doi.org/10.1007/978-3-319-45510-5_31},
   editor = {Sojka Petr, Horák Aleš, Kopeček Ivan, Pala Karel},
   keywords = {Question Answering; AQA; Simple Question Answering Database; SQAD; Named entity recognition},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Switzerland},
   isbn = {978-3-319-45510-5},
   pages = {270-278},
   publisher = {Springer International Publishing},
   title = {AQA: Automatic Question Answering System for Czech},
   url = {http://dx.doi.org/10.1007/978-3-319-45510-5_31},
   year = {2016}
}
  • Marek Medveď, Radoslav Sabol, and Aleš Horák. Czech Question Answering with Extended SQAD v3.0 Benchmark Dataset. In Horák, Aleš and Rychlý, Pavel and Rambousek, Adam. Proceedings of the Thirteenth Workshop on Recent Advances in Slavonic Natural Languages Processing, RASLAN 2019. Brno: Tribun EU, 2019. p. 99-108. ISBN 978-80-263-1530-8.
@inproceedings{1591218,
   author = {Sabol, Radoslav and Medveď, Marek and Horák, Aleš},
   address = {Brno},
   booktitle = {Proceedings of the Thirteenth Workshop on Recent Advances in Slavonic Natural Languages Processing, RASLAN 2019},
   editor = {Horák, Aleš and Rychlý, Pavel and Rambousek, Adam},
   keywords = {question answering; QA benchmark dataset; SQAD; Czech},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Brno},
   isbn = {978-80-263-1530-8},
   pages = {99-108},
   publisher = {Tribun EU},
   title = {Czech Question Answering with Extended SQAD v3.0 Benchmark Dataset},
   year = {2019}
}
  • MEDVEĎ, Marek, Aleš HORÁK a Radoslav SABOL. Improving RNN-based Answer Selection for Morphologically Rich Languages. In Ana Rocha, Luc Steels, Jaap van den Herik. Proceedings of the 12th International Conference on Agents and Artificial Intelligence. Portugal: SCITEPRESS, 2020. s. 644-651. ISBN 978-989-758-395-7. doi:10.5220/0008979206440651.
@inproceedings{1643878,
   author = {Medveď, Marek and Horák, Aleš and Sabol, Radoslav},
   address = {Portugal},
   booktitle = {Proceedings of the 12th International Conference on Agents and Artificial Intelligence},
   doi = {http://dx.doi.org/10.5220/0008979206440651},
   editor = {Ana Rocha, Luc Steels, Jaap van den Herik},
   keywords = {Question Answering; Question Classification; Answer Classification; Czech; Simple Question Answering Database; SQAD},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Portugal},
   isbn = {978-989-758-395-7},
   pages = {644-651},
   publisher = {SCITEPRESS},
   title = {Improving RNN-based Answer Selection for Morphologically Rich Languages},
   year = {2020}
}
  • MEDVEĎ, Marek, Aleš HORÁK a Radoslav SABOL. Comparing RNN and Transformer Context Representations in the Czech Answer Selection Task. In Ana Paula Rocha, Luc Steels, Jaap van den Herik. Proceedings of the 14th International Conference on Agents and Artificial Intelligence (ICAART). Portugal: SCITEPRESS, 2022. s. 388-394. ISBN 978-989-758-547-0. doi:10.5220/0000155600003116.
@inproceedings{1810359,
   author = {Medveď, Marek and Horák, Aleš and Sabol, Radoslav},
   address = {Portugal},
   booktitle = {Proceedings of the 14th International Conference on Agents and Artificial Intelligence (ICAART)},
   doi = {http://dx.doi.org/10.5220/0000155600003116},
   editor = {Ana Paula Rocha, Luc Steels, Jaap van den Herik},
   keywords = {Question Answering; Answer Context; Answer Selection; Czech; Sentece Embeddings; RNN; BERT},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Portugal},
   isbn = {978-989-758-547-0},
   note = {accepted for publication},
   pages = {388-394},
   publisher = {SCITEPRESS},
   title = {Comparing RNN and Transformer Context Representations in the Czech Answer Selection Task},
   year = {2022}
}

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