The dataset viewer is not available for this dataset.
Cannot get the config names for the dataset.
Error code:   ConfigNamesError
Exception:    ValueError
Message:      Couldn't infer the same data file format for all splits. Got {NamedSplit('train'): ('json', {}), NamedSplit('validation'): ('json', {}), NamedSplit('test'): ('csv', {})}
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 55, in compute_config_names_response
                  for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 351, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1512, in dataset_module_factory
                  raise e1 from None
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1489, in dataset_module_factory
                  return HubDatasetModuleFactoryWithoutScript(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1054, in get_module
                  module_name, default_builder_kwargs = infer_module_for_data_files(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 513, in infer_module_for_data_files
                  raise ValueError(f"Couldn't infer the same data file format for all splits. Got {split_modules}")
              ValueError: Couldn't infer the same data file format for all splits. Got {NamedSplit('train'): ('json', {}), NamedSplit('validation'): ('json', {}), NamedSplit('test'): ('csv', {})}

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AutoTrain Dataset for project: kor_hate_eval

Dataset Descritpion

This dataset has been automatically processed by AutoTrain for project kor_hate_eval.

Languages

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

Dataset Structure

Data Instances

A sample from this dataset looks as follows:

[
  {
    "text": "(\ud604\uc7ac \ud638\ud154\uc8fc\uc778 \uc2ec\uc815) \uc54418 \ub09c \ub9c8\ub978\ud558\ub298\uc5d0 \ub0a0\ubcbc\ub77d\ub9de\uace0 \ud638\ud154\ub9dd\ud558\uac8c\uc0dd\uacbc\ub294\ub370 \ub204\uad70 \uacc4\uc18d \ucd94\ubaa8\ubc1b\ub124....",
    "target": 1
  },
  {
    "text": "....\ud55c\uad6d\uc801\uc778 \ubbf8\uc778\uc758 \ub300\ud45c\uc801\uc778 \ubd84...\ub108\ubb34\ub098 \uacf1\uace0\uc544\ub984\ub2e4\uc6b4\ubaa8\uc2b5...\uadf8\ubaa8\uc2b5\ub4a4\uc758 \uc2ac\ud514\uc744 \ubbf8\ucc98 \uc54c\uc9c0\ubabb\ud588\ub124\uc694\u3160",
    "target": 0
  }
]

Dataset Fields

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

{
  "text": "Value(dtype='string', id=None)",
  "target": "ClassLabel(num_classes=2, names=['Default', 'Spoiled'], 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 7896
valid 3770
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