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Error code: DatasetGenerationError
Exception: TypeError
Message: Couldn't cast array of type
struct<file_name: string, origin_query: string, query_type: string, reference_page: list<item: int64>, source_type: string>
to
{'file_name': Value('string'), 'query_type': Value('string'), 'reference_page': List(Value('int64')), 'source_type': Value('string')}
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1831, in _prepare_split_single
writer.write_table(table)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 714, in write_table
pa_table = table_cast(pa_table, self._schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2224, in cast_table_to_schema
cast_array_to_feature(
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1795, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2002, in cast_array_to_feature
_c(array.field(name) if name in array_fields else null_array, subfeature)
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1797, in wrapper
return func(array, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2092, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
struct<file_name: string, origin_query: string, query_type: string, reference_page: list<item: int64>, source_type: string>
to
{'file_name': Value('string'), 'query_type': Value('string'), 'reference_page': List(Value('int64')), 'source_type': Value('string')}
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 1339, 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 972, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 894, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 970, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1702, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1858, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
examples dict |
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🚀Overview
This is the Repo for ViDoSeek, a benchmark specifically designed for visually rich document retrieval-reason-answer, fully suited for evaluation of RAG within large document corpus.
- The paper is available at https://arxiv.org/abs/2502.18017.
- ViDoRAG Project: https://github.com/Alibaba-NLP/ViDoRAG
ViDoSeek sets itself apart with its heightened difficulty level, attributed to the multi-document context and the intricate nature of its content types, particularly the Layout category. The dataset contains both single-hop and multi-hop queries, presenting a diverse set of challenges. We have also released the SlideVQA-Refined dataset which is refined through our pipeline. This dataset is suitable for evaluating retrieval-augmented generation tasks as well.
🔍Dataset Format
The annotation is in the form of a JSON file.
{
"uid": "04d8bb0db929110f204723c56e5386c1d8d21587_2",
// Unique identifier to distinguish different queries
"query": "What is the temperature of Steam explosion of Pretreatment for Switchgrass and Sugarcane bagasse preparation?",
// Query content
"reference_answer": "195-205 Centigrade",
// Reference answer to the query
"meta_info": {
"file_name": "Pretreatment_of_Switchgrass.pdf",
// Original file name, typically a PDF file
"reference_page": [10, 11],
// Reference page numbers represented as an array
"source_type": "Text",
// Type of data source, 2d_layout\Text\Table\Chart
"query_type": "Multi-Hop"
// Query type, Multi-Hop or Single-Hop
}
}
📚 Download and Pre-Process
To use ViDoSeek, you need to download the document files vidoseek_pdf_document.zip and query annotations vidoseek.json.
Optionally, you can use the code we provide to process the dataset and perform inference. The process code is available at https://github.com/Alibaba-NLP/ViDoRAG/tree/main/scripts.
📝 Citation
If you find this dataset useful, please consider citing our paper:
@article{wang2025vidorag,
title={ViDoRAG: Visual Document Retrieval-Augmented Generation via Dynamic Iterative Reasoning Agents},
author={Wang, Qiuchen and Ding, Ruixue and Chen, Zehui and Wu, Weiqi and Wang, Shihang and Xie, Pengjun and Zhao, Feng},
journal={arXiv preprint arXiv:2502.18017},
year={2025}
}
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