Dataset Viewer
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Cannot get the split names for the config 'default' of the dataset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 299, in get_dataset_config_info
for split_generator in builder._split_generators(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 81, in _split_generators
first_examples = list(islice(pipeline, self.NUM_EXAMPLES_FOR_FEATURES_INFERENCE))
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 55, in _get_pipeline_from_tar
current_example[field_name] = cls.DECODERS[data_extension](current_example[field_name])
ValueError: invalid literal for int() with base 10: b'1048576 1048582 1048589 786449 19 20 524307 786455 524312 786459 786460 262174 786462 32 262177 262178 786465 262183 1048617 524330 44 262189 1048624 786480 786481 51 786487 262200 786488 1048634 10
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/split_names.py", line 65, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 353, in get_dataset_split_names
info = get_dataset_config_info(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 304, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Dataset for paper MAPLE: Enhancing Review Generation with Multi-Aspect Prompt LEarning in Explainable Recommendation
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