:py:mod:`medkit.text.spacy.doc_pipeline`
========================================

.. py:module:: medkit.text.spacy.doc_pipeline


Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   medkit.text.spacy.doc_pipeline.SpacyDocPipeline




.. py:class:: SpacyDocPipeline(nlp: spacy.Language, medkit_labels_anns: list[str] | None = None, medkit_attrs: list[str] | None = None, spacy_entities: list[str] | None = None, spacy_span_groups: list[str] | None = None, spacy_attrs: list[str] | None = None, medkit_attribute_factories: dict[str, Callable[[spacy.tokens.Span, str], medkit.core.Attribute]] | None = None, name: str | None = None, uid: str | None = None)


   Bases: :py:obj:`medkit.core.DocOperation`

   
   DocPipeline to obtain annotations created using spacy.
















   ..
       !! processed by numpydoc !!
   .. py:method:: run(medkit_docs: list[medkit.core.text.TextDocument]) -> None

      
      Run a spacy pipeline on a list of medkit documents.

      Each medkit document is converted to spacy document (Doc object),
      with the selected annotations and attributes. Then, the spacy pipeline
      is executed and finally, the new annotations and attributes are
      converted into medkit annotations.

      :Parameters:

          **medkit_docs** : list of TextDocument
              List of TextDocuments on which to run the pipeline














      ..
          !! processed by numpydoc !!