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The dataset generation failed
Error code: DatasetGenerationError
Exception: TypeError
Message: Couldn't cast array of type
struct<id: int64, width: int64, height: int64, file_name: string, license: int64, bboxes: list<item: struct<id: int64, bbox: list<item: double>, category_id: int64>>, reactions: list<item: struct<reactants: list<item: int64>, conditions: list<item: int64>, products: list<item: int64>>>, caption: string, pdf: struct<Page: int64, DPI: int64, Width: int64, Height: int64, CaptionBB: list<item: int64>, ImageBB: list<item: int64>>, diagram_type: string, flickr_url: string, coco_url: string, date_captured: int64>
to
{'id': Value(dtype='int64', id=None), 'width': Value(dtype='int64', id=None), 'height': Value(dtype='int64', id=None), 'file_name': Value(dtype='string', id=None), 'license': Value(dtype='int64', id=None), 'bboxes': [{'id': Value(dtype='int64', id=None), 'bbox': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'category_id': Value(dtype='int64', id=None)}], 'reactions': [{'reactants': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'conditions': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'products': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)}], 'corefs': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None), 'caption': Value(dtype='string', id=None), 'pdf': {'Page': Value(dtype='int64', id=None), 'DPI': Value(dtype='int64', id=None), 'Width': Value(dtype='int64', id=None), 'Height': Value(dtype='int64', id=None), 'CaptionBB': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ImageBB': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)}, 'diagram_type': Value(dtype='string', id=None), 'flickr_url': Value(dtype='string', id=None), 'coco_url': Value(dtype='string', id=None), 'date_captured': Value(dtype='int64', id=None)}
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 585, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2261, 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 2261, 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 1802, 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 1802, 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 2025, in cast_array_to_feature
casted_array_values = _c(array.values, feature[0])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1804, in wrapper
return func(array, *args, **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2122, 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<id: int64, width: int64, height: int64, file_name: string, license: int64, bboxes: list<item: struct<id: int64, bbox: list<item: double>, category_id: int64>>, reactions: list<item: struct<reactants: list<item: int64>, conditions: list<item: int64>, products: list<item: int64>>>, caption: string, pdf: struct<Page: int64, DPI: int64, Width: int64, Height: int64, CaptionBB: list<item: int64>, ImageBB: list<item: int64>>, diagram_type: string, flickr_url: string, coco_url: string, date_captured: int64>
to
{'id': Value(dtype='int64', id=None), 'width': Value(dtype='int64', id=None), 'height': Value(dtype='int64', id=None), 'file_name': Value(dtype='string', id=None), 'license': Value(dtype='int64', id=None), 'bboxes': [{'id': Value(dtype='int64', id=None), 'bbox': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'category_id': Value(dtype='int64', id=None)}], 'reactions': [{'reactants': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'conditions': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'products': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)}], 'corefs': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None), 'caption': Value(dtype='string', id=None), 'pdf': {'Page': Value(dtype='int64', id=None), 'DPI': Value(dtype='int64', id=None), 'Width': Value(dtype='int64', id=None), 'Height': Value(dtype='int64', id=None), 'CaptionBB': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ImageBB': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)}, 'diagram_type': Value(dtype='string', id=None), 'flickr_url': Value(dtype='string', id=None), 'coco_url': Value(dtype='string', id=None), 'date_captured': Value(dtype='int64', id=None)}
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 1529, 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 1154, 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 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.
categories list | licenses list | info dict | images list | annotations list |
|---|---|---|---|---|
[{"id":1,"name":"structure"},{"id":2,"name":"text"},{"id":3,"name":"identifier"},{"id":4,"name":"sup(...TRUNCATED) | [
{
"name": "",
"id": 0,
"url": ""
}
] | {"contributor":"Jiang Guo","date_created":"Feb. 01","description":"A dataset for chemical visual dia(...TRUNCATED) | [{"id":0,"width":1352,"height":1112,"file_name":"jacs.5b05415-Figure-c2.png","license":0,"bboxes":[{(...TRUNCATED) | [{"id":0,"image_id":0,"category_id":3,"segmentation":[],"area":2185.701,"bbox":[193.4,728.31,53.65,4(...TRUNCATED) |
[{"id":1,"name":"structure"},{"id":2,"name":"text"},{"id":3,"name":"identifier"},{"id":4,"name":"sup(...TRUNCATED) | [
{
"name": "",
"id": 0,
"url": ""
}
] | {"contributor":"Jiang Guo","date_created":"Feb. 01","description":"A dataset for chemical visual dia(...TRUNCATED) | [{"id":0,"width":1352,"height":1112,"file_name":"jacs.5b05415-Figure-c2.png","license":0,"bboxes":[{(...TRUNCATED) | [{"id":2440,"image_id":0,"category_id":1,"segmentation":[],"area":30071.618800000004,"bbox":[85.79,5(...TRUNCATED) |
MolDetect and MolCoref Data
The MolDetect and MolCoref models can be found in this github repository, as well as additional instructions for testing or running the models.
The reaction diagrams are located at images.zip.
Additionally, we use a 70-10-20 split in our experiments. The full train/dev/test split for each task is available in this repository as well.
This notebook shows how to visualize the diagram and the ground truth.
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