Skip to content

Auto Classes

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 AutoConfig, AutoModel, and AutoTokenizer will directly create a class of the relevant architecture. For instance

model = AutoModel.from_pretrained("google-bert/bert-base-cased")

will create a model that is an instance of BertModel.

There is one class of AutoModel for each task.

Each of the auto classes has a method to be extended with your custom classes. For instance, if you have defined a custom class of model NewModel, make sure you have a NewModelConfig then you can add those to the auto classes like this:

from transformers import AutoConfig, AutoModel
AutoConfig.register("new-model", NewModelConfig)
AutoModel.register(NewModelConfig, NewModel)

You will then be able to use the auto classes like you would usually do!

If your NewModelConfig is a subclass of PreTrainedConfig, make sure its model_type attribute is set to the same key you use when registering the config (here "new-model").

Likewise, if your NewModel is a subclass of PreTrainedModel, make sure its config_class attribute is set to the same class you use when registering the model (here NewModelConfig).

[[autodoc]] AutoConfig

[[autodoc]] AutoTokenizer

[[autodoc]] AutoFeatureExtractor

[[autodoc]] AutoImageProcessor

[[autodoc]] AutoVideoProcessor

[[autodoc]] AutoProcessor

The following auto classes are available for instantiating a base model class without a specific head.

[[autodoc]] AutoModel

The following auto classes are available for instantiating a model with a pretraining head.

[[autodoc]] AutoModelForPreTraining

The following auto classes are available for the following natural language processing tasks.

[[autodoc]] AutoModelForCausalLM

[[autodoc]] AutoModelForMaskedLM

[[autodoc]] AutoModelForMaskGeneration

[[autodoc]] AutoModelForSeq2SeqLM

[[autodoc]] AutoModelForSequenceClassification

[[autodoc]] AutoModelForMultipleChoice

[[autodoc]] AutoModelForNextSentencePrediction

[[autodoc]] AutoModelForTokenClassification

[[autodoc]] AutoModelForQuestionAnswering

[[autodoc]] AutoModelForTextEncoding

The following auto classes are available for the following computer vision tasks.

[[autodoc]] AutoModelForDepthEstimation

[[autodoc]] AutoModelForImageClassification

[[autodoc]] AutoModelForVideoClassification

[[autodoc]] AutoModelForKeypointDetection

[[autodoc]] AutoModelForKeypointMatching

[[autodoc]] AutoModelForMaskedImageModeling

[[autodoc]] AutoModelForObjectDetection

[[autodoc]] AutoModelForImageSegmentation

[[autodoc]] AutoModelForImageToImage

[[autodoc]] AutoModelForSemanticSegmentation

[[autodoc]] AutoModelForInstanceSegmentation

[[autodoc]] AutoModelForUniversalSegmentation

[[autodoc]] AutoModelForZeroShotImageClassification

[[autodoc]] AutoModelForZeroShotObjectDetection

The following auto classes are available for the following audio tasks.

[[autodoc]] AutoModelForAudioClassification

[[autodoc]] AutoModelForAudioFrameClassification

[[autodoc]] AutoModelForCTC

[[autodoc]] AutoModelForSpeechSeq2Seq

[[autodoc]] AutoModelForAudioXVector

[[autodoc]] AutoModelForTextToSpectrogram

[[autodoc]] AutoModelForTextToWaveform

[[autodoc]] AutoModelForAudioTokenization

The following auto classes are available for the following multimodal tasks.

[[autodoc]] AutoModelForMultimodalLM

[[autodoc]] AutoModelForTableQuestionAnswering

[[autodoc]] AutoModelForDocumentQuestionAnswering

[[autodoc]] AutoModelForVisualQuestionAnswering

[[autodoc]] AutoModelForVision2Seq

[[autodoc]] AutoModelForImageTextToText

[[autodoc]] AutoModelForTimeSeriesPrediction