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remove derived classes for now

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thomwolf
2019-08-05 19:08:19 +02:00
parent 13936a9621
commit 0b524b0848
4 changed files with 4 additions and 295 deletions
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21
docs/source/model_doc/auto.rst
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@@ -3,12 +3,9 @@ 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 retreive the relevant model given the name/path to the pretrained weights/config/vocabulary.
AutoClasses are here to do this job for you so that you automatically retreive the relevant model given the name/path to the pretrained weights/config/vocabulary:
There are two types of AutoClasses:
- ``AutoModel``, ``AutoConfig`` and ``AutoTokenizer``: instantiating these ones will directly create a class of the relevant architecture (ex: ``model = AutoModel.from_pretrained('bert-base-cased')`` will create a instance of ``BertModel``)
- All the others (``AutoModelWithLMHead``, ``AutoModelForSequenceClassification``...) are standardized Auto classes for finetuning. Instantiating these will create instance of the same class (``AutoModelWithLMHead``, ``AutoModelForSequenceClassification``...) comprising (i) the relevant base model class (as mentioned just above) and (ii) a standard fine-tuning head on top, convenient for the task.
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``
@@ -25,20 +22,6 @@ There are two types of AutoClasses:
:members:
``AutoModelWithLMHead``
~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: pytorch_transformers.AutoModelWithLMHead
:members:
``AutoModelForSequenceClassification``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: pytorch_transformers.AutoModelForSequenceClassification
:members:
``AutoTokenizer``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~