Datasets:

Salama1429
/

tarteel-ai-QuranQA

Error code:   NormalRowsError
Exception:    DatasetGenerationError
Message:      An error occurred while generating the dataset
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 126, in get_rows_or_raise
                  return get_rows(
                File "/src/services/worker/src/worker/utils.py", line 64, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 103, 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 1384, in __iter__
                  for key, example in ex_iterable:
                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/parquet/parquet.py", line 94, in _generate_tables
                  yield f"{file_idx}_{batch_idx}", self._cast_table(pa_table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py", line 74, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2322, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2281, in cast_table_to_schema
                  arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2281, in <listcomp>
                  arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1797, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1797, in <listcomp>
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2111, in cast_array_to_feature
                  raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}")
              TypeError: Couldn't cast array of type
              struct<text: list<item: string>, answer_start: list<item: int32>>
              to
              {'text': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'start_char': Sequence(feature=Value(dtype='int32', id=None), length=-1, id=None)}
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1973, in _prepare_split_single
                  for _, table in generator:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py", line 94, in _generate_tables
                  yield f"{file_idx}_{batch_idx}", self._cast_table(pa_table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py", line 74, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2322, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2281, in cast_table_to_schema
                  arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2281, in <listcomp>
                  arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1797, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1797, in <listcomp>
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2111, in cast_array_to_feature
                  raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}")
              TypeError: Couldn't cast array of type
              struct<text: list<item: string>, answer_start: list<item: int32>>
              to
              {'text': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'start_char': Sequence(feature=Value(dtype='int32', id=None), length=-1, id=None)}
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 158, in get_rows_or_raise
                  return get_rows(
                File "/src/services/worker/src/worker/utils.py", line 64, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 87, in get_rows
                  ds = load_dataset(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 2549, in load_dataset
                  builder_instance.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1005, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1100, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1860, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2016, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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Dataset Card for the Qur'anic Reading Comprehension Dataset (QRCD)

Dataset Summary

The QRCD (Qur'anic Reading Comprehension Dataset) is composed of 1,093 tuples of question-passage pairs that are coupled with their extracted answers to constitute 1,337 question-passage-answer triplets.

Supported Tasks and Leaderboards

This task is evaluated as a ranking task. To give credit to a QA system that may retrieve an answer (not necessarily at the first rank) that does not fully match one of the gold answers but partially matches it, we use partial Reciprocal Rank (pRR) measure. It is a variant of the traditional Reciprocal Rank evaluation metric that considers partial matching. pRR is the official evaluation measure of this shared task. We will also report Exact Match (EM) and F1@1, which are evaluation metrics applied only on the top predicted answer. The EM metric is a binary measure that rewards a system only if the top predicted answer exactly matches one of the gold answers. Whereas, the F1@1 metric measures the token overlap between the top predicted answer and the best matching gold answer. To get an overall evaluation score, each of the above measures is averaged over all questions.

Languages

Qur'anic Arabic

Dataset Structure

Data Instances

To simplify the structure of the dataset, each tuple contains one passage, one question and a list that may contain one or more answers to that question, as shown below:

{
  "pq_id": "38:41-44_105",
  "passage": "واذكر عبدنا أيوب إذ نادى ربه أني مسني الشيطان بنصب وعذاب. اركض برجلك هذا مغتسل بارد وشراب. ووهبنا له أهله ومثلهم معهم رحمة منا وذكرى لأولي الألباب. وخذ بيدك ضغثا فاضرب به ولا تحنث إنا وجدناه صابرا نعم العبد إنه أواب.",
  "surah": 38,
  "verses": "41-44",
  "question": "من هو النبي المعروف بالصبر؟",
  "answers": [
    {
      "text": "أيوب",
      "start_char": 12
    }
  ]
}

Each Qur’anic passage in QRCD may have more than one occurrence; and each passage occurrence is paired with a different question. Likewise, each question in QRCD may have more than one occurrence; and each question occurrence is paired with a different Qur’anic passage. The source of the Qur'anic text in QRCD is the Tanzil project download page, which provides verified versions of the Holy Qur'an in several scripting styles. We have chosen the simple-clean text style of Tanzil version 1.0.2.

Data Fields

pq_id: Sample ID
passage: Context text
surah: Surah number
verses: Verse range
question: Question text
answers: List of answers and their start character

Data Splits

Dataset	%	# Question-Passage Pairs	# Question-Passage-Answer Triplets
Training	65%	710	861
Development	10%	109	128
Test	25%	274	348
All	100%	1,093	1,337

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 QRCD v1.1 dataset is distributed under the CC-BY-ND 4.0 License https://creativecommons.org/licenses/by-nd/4.0/legalcode For a human-readable summary of (and not a substitute for) the above CC-BY-ND 4.0 License, please refer to https://creativecommons.org/licenses/by-nd/4.0/

Citation Information

@article{malhas2020ayatec,
  author = {Malhas, Rana and Elsayed, Tamer},
  title = {AyaTEC: Building a Reusable Verse-Based Test Collection for Arabic Question Answering on the Holy Qur’an},
  year = {2020},
  issue_date = {November 2020},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  volume = {19},
  number = {6},
  issn = {2375-4699},
  url = {https://doi.org/10.1145/3400396},
  doi = {10.1145/3400396},
  journal = {ACM Trans. Asian Low-Resour. Lang. Inf. Process.},
  month = {oct},
  articleno = {78},
  numpages = {21},
  keywords = {evaluation, Classical Arabic}
}