Dataset Viewer
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:    ArrowTypeError
Message:      ("Expected bytes, got a 'int' object", 'Conversion failed for column answer with type object')
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 137, in _generate_tables
                  pa_table = paj.read_json(
                File "pyarrow/_json.pyx", line 308, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to array in row 0
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 233, 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 2998, 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 1918, 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 2093, in __iter__
                  for key, example in ex_iterable:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1576, in __iter__
                  for key_example in islice(self.ex_iterable, self.n - ex_iterable_num_taken):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 279, in __iter__
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 167, in _generate_tables
                  pa_table = pa.Table.from_pandas(df, preserve_index=False)
                File "pyarrow/table.pxi", line 3874, in pyarrow.lib.Table.from_pandas
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/pandas_compat.py", line 624, in dataframe_to_arrays
                  arrays[i] = maybe_fut.result()
                File "/usr/local/lib/python3.9/concurrent/futures/_base.py", line 439, in result
                  return self.__get_result()
                File "/usr/local/lib/python3.9/concurrent/futures/_base.py", line 391, in __get_result
                  raise self._exception
                File "/usr/local/lib/python3.9/concurrent/futures/thread.py", line 58, in run
                  result = self.fn(*self.args, **self.kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/pandas_compat.py", line 598, in convert_column
                  raise e
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/pandas_compat.py", line 592, in convert_column
                  result = pa.array(col, type=type_, from_pandas=True, safe=safe)
                File "pyarrow/array.pxi", line 339, in pyarrow.lib.array
                File "pyarrow/array.pxi", line 85, in pyarrow.lib._ndarray_to_array
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowTypeError: ("Expected bytes, got a 'int' object", 'Conversion failed for column answer with type object')

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Dataset Card for ACQAD

we propose a new approach to automatically generate a dataset for Arabic complex question answering task. The proposed approach is based on using an effective workflow with a set of templates. The generated dataset, denoted as ACQAD, contains more than 118k questions, covering both comparison and multi-hop types. Each question-answer pair is decomposed into a set of single-hop questions, allowing QA systems to reduce question complexity and explain the reasoning steps

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Arabic Wikipedia

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BibTeX:

@inproceedings{sidhoum2022acqad, title={ACQAD: a dataset for arabic complex question answering}, author={Hamouda Sidhoum, Abdellah and Mataoui, M’hamed and Sebbak, Faouzi and Sma{"\i}li, Kamel}, booktitle={International conference on cyber security, artificial inteligence and theoretical computer science}, year={2022} }

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