:py:mod:`medcat.ner.transformers_ner`
=====================================

.. py:module:: medcat.ner.transformers_ner


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

Classes
~~~~~~~

.. autoapisummary::

   medcat.ner.transformers_ner.TransformersNER



Functions
~~~~~~~~~

.. autoapisummary::

   medcat.ner.transformers_ner.func_has_kwarg



Attributes
~~~~~~~~~~

.. autoapisummary::

   medcat.ner.transformers_ner.logger


.. py:data:: logger

   

.. py:class:: TransformersNER(cdb, config = None, training_arguments=None)


   Bases: :py:obj:`object`

   TODO: Add documentation

   .. py:attribute:: name
      :value: 'transformers_ner'

      

   .. py:method:: __init__(cdb, config = None, training_arguments=None)


   .. py:method:: create_eval_pipeline()


   .. py:method:: get_hash()

      A partial hash trying to catch differences between models.

      :Returns: **str** -- The hex hash.


   .. py:method:: _prepare_dataset(json_path, ignore_extra_labels, meta_requirements, file_name='data.json')


   .. py:method:: train(json_path = None, ignore_extra_labels=False, dataset=None, meta_requirements=None, trainer_callbacks = None)

      Train or continue training a model give a json_path containing a MedCATtrainer export. It will
      continue training if an existing model is loaded or start new training if the model is blank/new.

      :param json_path: Path/Paths to a MedCATtrainer export containing the meta_annotations we want to train for.
      :type json_path: str or list
      :param ignore_extra_labels: Makes only sense when an existing deid model was loaded and from the new data we want to ignore
                                  labels that did not exist in the old model.
      :param dataset: Defaults to None.
      :param meta_requirements: Defaults to None
      :param trainer_callbacks: A list of trainer callbacks for collecting metrics during the training at the client side. The
                                transformers Trainer object will be passed in when each callback is called.
      :type trainer_callbacks: List[Callable[[Trainer], TrainerCallback]]]

      :raises ValueError: If something went wrong with model save path.

      :Returns: **Tuple** -- The dataframe, examples, and the dataset


   .. py:method:: eval(json_path = None, dataset=None, ignore_extra_labels=False, meta_requirements=None)


   .. py:method:: save(save_dir_path)

      Save all components of this class to a file

      :param save_dir_path: Path to the directory where everything will be saved.
      :type save_dir_path: str


   .. py:method:: expand_model_with_concepts(cui2preferred_name, use_avg_init = True)

      Expand the model with new concepts and their preferred names, which requires subsequent retraining on the model.

      :param cui2preferred_name: Dictionary where each key is the literal ID of the concept to be added and each value is its preferred name.
      :type cui2preferred_name: Dict[str, str]
      :param use_avg_init: Whether to use the average of existing weights or biases as the initial value for the new concept. Defaults to True.
      :type use_avg_init: bool


   .. py:method:: load(save_dir_path, config_dict = None)
      :classmethod:

      Load a meta_cat object.

      :param save_dir_path: The directory where all was saved.
      :type save_dir_path: str
      :param config_dict: This can be used to overwrite saved parameters for this meta_cat
                          instance. Why? It is needed in certain cases where we autodeploy stuff.
      :type config_dict: dict

      :Returns: **meta_cat** (`medcat.MetaCAT`) -- You don't say


   .. py:method:: batch_generator(stream, batch_size_chars)
      :staticmethod:


   .. py:method:: pipe(stream, *args, **kwargs)

      Process many documents at once.

      :param stream: List of spacy documents.
      :type stream: Iterable[spacy.tokens.Doc]
      :param \*args: Extra arguments (not used here).
      :param \*\*kwargs: Extra keyword arguments (not used here).

      :Yields: *Doc* -- The same document.

      :Returns: **Iterator[Doc]** -- If the stream is None or empty.


   .. py:method:: _process(stream, batch_size_chars)


   .. py:method:: __call__(doc)

      Process one document, used in the spacy pipeline for sequential
      document processing.

      :param doc: A spacy document
      :type doc: Doc

      :Returns: **Doc** -- The same spacy document.



.. py:function:: func_has_kwarg(func, keyword)