Hugging Face's logo

Datasets:
pie
/
argmicro

Dataset card Files Community
2
Dataset Viewer
Full Screen
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 pie/argmicro, you need to install the following dependencies: pytorch_ie, pie_datasets.
Please install them using 'pip install pytorch_ie pie_datasets' 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 pie/argmicro, you need to install the following dependencies: pytorch_ie, pie_datasets.
              Please install them using 'pip install pytorch_ie pie_datasets' 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.

YAML Metadata Warning: empty or missing yaml metadata in repo card (https://huggingface.co/docs/hub/datasets-cards)

PIE Dataset Card for "argmicro"

This is a PyTorch-IE wrapper for the ArgMicro Huggingface dataset loading script.

Dataset Variants

The dataset contains two BuilderConfig's:

  • de: with the original texts collection in German
  • en: with the English-translated texts

Data Schema

The document type for this dataset is ArgMicroDocument which defines the following data fields:

  • text (str)
  • id (str, optional)
  • topic_id (str, optional)
  • metadata (dictionary, optional)

and the following annotation layers:

  • stance (annotation type: Label)
    • description: A document may contain one of these stance labels: pro, con, unclear, or no label when it is undefined (see here for reference).
  • edus (annotation type: Span, target: text)
  • adus (annotation type: LabeledAnnotationCollection, target: edus)
    • description: each element of adus may consist of several entries from edus, so we require LabeledAnnotationCollection as annotation type. This is originally indicated by seg edges in the data.
    • LabeledAnnotationCollection has the following fields:
      • annotations (annotation type: Span, target: text)
      • label (str, optional), values: opp, pro (see here)
  • relations (annotation type: MultiRelation, target: adus)
    • description: Undercut (und) relations originally target other relations (i.e. edges), but we let them target the head of the targeted relation instead. The original state can be deterministically reconstructed by taking the label into account. Furthermore, the head of additional source (add) relations are integrated into the head of the target relation (note that this propagates along und relations). We model this with MultiRelations whose head and tail are of type LabeledAnnotationCollection.
    • MultiRelation has the following fields:
      • head (tuple, annotation type: LabeledAnnotationCollection, target: adus)
      • tail (tuple, annotation type: LabeledAnnotationCollection, target: adus)
      • label (str, optional), values: sup, exa, reb, und (see here for reference, but note that helper relations seg and add are not there anymore, see above).

See here for the annotation type definitions.

Document Converters

The dataset provides document converters for the following target document types:

  • pytorch_ie.documents.TextDocumentWithLabeledSpansAndBinaryRelations
    • LabeledSpans, converted from ArgMicroDocument's adus
      • labels: opp, pro
      • if an ADU contains multiple spans (i.e. EDUs), we take the start of the first EDU and the end of the last EDU as the boundaries of the new LabeledSpan. We also raise exceptions if any newly created LabeledSpans overlap.
    • BinraryRelations, converted from ArgMicroDocument's relations
      • labels: sup, reb, und, joint, exa
      • if the head or tail consists of multiple adus, then we build BinaryRelations with all head-tail combinations and take the label from the original relation. Then, we build BinaryRelations' with label joint between each component that previously belongs to the same head or tail, respectively.
    • metadata, we keep the ArgMicroDocument's metadata, but stance and topic_id.

See here for the document type definitions.

Downloads last month
19
Edit dataset card