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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    DatasetGenerationError
Message:      An error occurred while generating the dataset
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 583, in write_table
                  self._build_writer(inferred_schema=pa_table.schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 404, in _build_writer
                  self.pa_writer = self._WRITER_CLASS(self.stream, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 1016, in __init__
                  self.writer = _parquet.ParquetWriter(
                File "pyarrow/_parquet.pyx", line 1869, in pyarrow._parquet.ParquetWriter.__cinit__
                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.ArrowNotImplementedError: Cannot write struct type '_format_kwargs' with no child field to Parquet. Consider adding a dummy child field.
              
              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 2027, in _prepare_split_single
                  num_examples, num_bytes = writer.finalize()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 602, in finalize
                  self._build_writer(self.schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 404, in _build_writer
                  self.pa_writer = self._WRITER_CLASS(self.stream, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 1016, in __init__
                  self.writer = _parquet.ParquetWriter(
                File "pyarrow/_parquet.pyx", line 1869, in pyarrow._parquet.ParquetWriter.__cinit__
                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.ArrowNotImplementedError: Cannot write struct type '_format_kwargs' with no child field to Parquet. Consider adding a dummy child field.
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1321, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 935, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, 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 1882, 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 2038, 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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_data_files
list
_fingerprint
string
_format_columns
sequence
_format_kwargs
dict
_format_type
null
_indexes
dict
_indices_data_files
null
_output_all_columns
bool
_split
null
[ { "filename": "dataset.arrow" } ]
8b159b4b6558fd1c
[ "target", "text" ]
{}
null
{}
null
false
null
YAML Metadata Warning: The task_categories "conditional-text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, other

AutoNLP Dataset for project: Scientific_Title_Generator

Table of content

Dataset Descritpion

This dataset has been automatically processed by AutoNLP for project Scientific_Title_Generator.

Languages

The BCP-47 code for the dataset's language is unk.

Dataset Structure

Data Instances

A sample from this dataset looks as follows:

[
  {
    "target": "Unification of Fusion Theories, Rules, Filters, Image Fusion and Target\n  Tracking Methods (UFT)",
    "text": "  The author has pledged in various papers, conference or seminar\npresentations, and scientific grant applications (between 2004-2015) for the\nunification of fusion theories, combinations of fusion rules, image fusion\nprocedures, filter algorithms, and target tracking methods for more accurate\napplications to our real world problems - since neither fusion theory nor\nfusion rule fully satisfy all needed applications. For each particular\napplication, one selects the most appropriate fusion space and fusion model,\nthen the fusion rules, and the algorithms of implementation. He has worked in\nthe Unification of the Fusion Theories (UFT), which looks like a cooking\nrecipe, better one could say like a logical chart for a computer programmer,\nbut one does not see another method to comprise/unify all things. The\nunification scenario presented herein, which is now in an incipient form,\nshould periodically be updated incorporating new discoveries from the fusion\nand engineering research.\n"
  },
  {
    "target": "Investigation of Variances in Belief Networks",
    "text": "  The belief network is a well-known graphical structure for representing\nindependences in a joint probability distribution. The methods, which perform\nprobabilistic inference in belief networks, often treat the conditional\nprobabilities which are stored in the network as certain values. However, if\none takes either a subjectivistic or a limiting frequency approach to\nprobability, one can never be certain of probability values. An algorithm\nshould not only be capable of reporting the probabilities of the alternatives\nof remaining nodes when other nodes are instantiated; it should also be capable\nof reporting the uncertainty in these probabilities relative to the uncertainty\nin the probabilities which are stored in the network. In this paper a method\nfor determining the variances in inferred probabilities is obtained under the\nassumption that a posterior distribution on the uncertainty variables can be\napproximated by the prior distribution. It is shown that this assumption is\nplausible if their is a reasonable amount of confidence in the probabilities\nwhich are stored in the network. Furthermore in this paper, a surprising upper\nbound for the prior variances in the probabilities of the alternatives of all\nnodes is obtained in the case where the probability distributions of the\nprobabilities of the alternatives are beta distributions. It is shown that the\nprior variance in the probability at an alternative of a node is bounded above\nby the largest variance in an element of the conditional probability\ndistribution for that node.\n"
  }
]

Dataset Fields

The dataset has the following fields (also called "features"):

{
  "target": "Value(dtype='string', id=None)",
  "text": "Value(dtype='string', id=None)"
}

Dataset Splits

This dataset is split into a train and validation split. The split sizes are as follow:

Split name Num samples
train 5784
valid 1446
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Models trained or fine-tuned on AryanLala/autonlp-data-Scientific_Title_Generator