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

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


Submodules
----------
.. toctree::
   :titlesonly:
   :maxdepth: 1

   displacy_utils/index.rst
   doc_pipeline/index.rst
   edsnlp/index.rst
   pipeline/index.rst
   spacy_utils/index.rst


Package Contents
----------------

Classes
~~~~~~~

.. autoapisummary::

   medkit.text.spacy.SpacyDocPipeline
   medkit.text.spacy.SpacyPipeline




.. 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 !!


.. py:class:: SpacyPipeline(nlp: spacy.Language, 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.operation.Operation`

   
   Segment annotator relying on a Spacy pipeline.
















   ..
       !! processed by numpydoc !!
   .. py:method:: run(segments: list[medkit.core.text.Segment]) -> list[medkit.core.text.Segment]

      
      Run the operation.

      Run a spacy pipeline on a list of segments provided as input
      and returns a new list of segments.
      Each segment is converted to spacy document (Doc object).
      Then, the spacy pipeline is executed and finally, the new
      annotations and attributes are converted into medkit annotations.

      :Parameters:

          **segments** : list of Segment
              List of segments on which to run the spacy pipeline

      :Returns:

          list of Segment
              List of new annotations













      ..
          !! processed by numpydoc !!

   .. py:method:: _find_segments_in_spacy_doc(spacy_doc: spacy.tokens.Doc, medkit_source_ann: medkit.core.text.Segment)