auto.rst

AutoModels
-----------

In many cases, the architecture you want to use can be guessed from the name or the path of the pretrained model you are supplying to the ``from_pretrained`` method.

AutoClasses are here to do this job for you so that you automatically retrieve the relevant model given the name/path to the pretrained weights/config/vocabulary:

Instantiating one of ``AutoModel``, ``AutoConfig`` and ``AutoTokenizer`` will directly create a class of the relevant architecture (ex: ``model = AutoModel.from_pretrained('bert-base-cased')`` will create a instance of ``BertModel``).


``AutoConfig``
~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.AutoConfig
    :members:


``AutoTokenizer``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.AutoTokenizer
    :members:


``AutoModel``
~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.AutoModel
    :members:


``AutoModelForPreTraining``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.AutoModelForPreTraining
    :members:


``AutoModelWithLMHead``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.AutoModelWithLMHead
    :members:


``AutoModelForSequenceClassification``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.AutoModelForSequenceClassification
    :members:


``AutoModelForQuestionAnswering``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.AutoModelForQuestionAnswering
    :members:


``AutoModelForTokenClassification``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.AutoModelForTokenClassification
    :members: