The dataset viewer is not available for this dataset.
Cannot get the config names for the dataset.
Error code:   ConfigNamesError
Exception:    ImportError
Message:      To be able to use yuyang/bart_newsroom, you need to install the following dependency: bs4.
Please install it using 'pip install bs4' for instance.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
                  config_names = get_dataset_config_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 347, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1914, in dataset_module_factory
                  raise e1 from None
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1880, in dataset_module_factory
                  return HubDatasetModuleFactoryWithScript(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1504, in get_module
                  local_imports = _download_additional_modules(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 354, in _download_additional_modules
                  raise ImportError(
              ImportError: To be able to use yuyang/bart_newsroom, you need to install the following dependency: bs4.
              Please install it using 'pip install bs4' for instance.

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.

Modification of the newsroom dataset in Hugging Face. The main goal is to reproduce the results on BART.

References: https://huggingface.co/datasets/newsroom/blob/main/newsroom.py

Major changes:

  1. replace the "\n\n" with one whitespace in the text
  2. remove the html related tags, remove the latin1 coded characters in the summary.
  3. remove unnessary data features.

The main motivation for doing such modifications is that with the original data, particularly the summary data, the prediction will be weird and would involve multiple lines, which then makes the number of predictions unmatched with the number of references.

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