mirror of
https://github.com/huggingface/transformers.git
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* add dia model * add tokenizer files * cleanup some stuff * brut copy paste code * rough cleanup of the modeling code * nuke some stuff * more nuking * more cleanups * updates * add mulitLayerEmbedding vectorization * nits * more modeling simplifications * updates * update rope * update rope * just fixup * update configuration files * more cleanup! * default config values * update * forgotten comma * another comma! * update, more cleanups * just more nits * more config cleanups * time for the encoder * fix * sa=mall nit * nits * n * refacto a bit * cleanup * update cv scipt * fix last issues * fix last nits * styling * small fixes * just run 1 generation * fixes * nits * fix conversion * fix * more fixes * full generate * ouf! * fixes! * updates * fix * fix cvrt * fixup * nits * delete wrong test * update * update * test tokenization * let's start changing things bit by bit - fix encoder step * removing custom generation, moving to GenerationMixin * add encoder decoder attention masks for generation * mask changes, correctness checked against ad29837 in dia repo * refactor a bit already --> next cache * too important not to push :) * minimal cleanup + more todos * make main overwrite modeling utils * add cfg filter & eos filter * add eos countdown & delay pattern * update eos countdown * add max step eos countdown * fix tests * fix some things * fix generation with testing * move cfg & eos stuff to logits processor * make RepetitionPenaltyLogitsProcessor flexible - can accept 3D scores like (batch_size, channel, vocab) * fix input_ids concatenation dimension in GenerationMixin for flexibility * Add DiaHangoverLogitsProcessor and DiaExponentialDecayLengthPenalty classes; refactor logits processing in DiaForConditionalGeneration to utilize new configurations and improve flexibility. * Add stopping criteria * refactor * move delay pattern from processor to modeling like musicgen. - add docs - change eos countdown to eos delay pattern * fix processor & fix tests * refactor types * refactor imports * format code * fix docstring to pass ci * add docstring to DiaConfig & add DiaModel to test * fix docstring * add docstring * fix some bugs * check * porting / merging results from other branch - IMPORTANT: it very likely breaks generation, the goal is to have a proper forward path first * experimental testing of left padding for first channel * whoops * Fix merge to make generation work * fix cfg filter * add position ids * add todos, break things * revert changes to generation --> we will force 2d but go 3d on custom stuff * refactor a lot, change prepare decoder ids to work with left padding (needs testing), add todos * some first fixes to get to 10. in generation * some more generation fixes / adjustment * style + rope fixes * move cfg out, simplify a few things, more todos * nit * start working on custom logit processors * nit * quick fixes * cfg top k * more refactor of logits processing, needs a decision if gen config gets the new attributes or if we move it to config or similar * lets keep changes to core code minimal, only eos scaling is questionable atm * simpler eos delay logits processor * that was for debugging :D * proof of concept rope * small fix on device mismatch * cfg fixes + delay logits max len * transformers rope * modular dia * more cleanup * keep modeling consistently 3D, generate handles 2D internally * decoder starts with bos if nothing * post processing prototype * style * lol * force sample / greedy + fixes on padding * style * fixup tokenization * nits * revert * start working on dia tests * fix a lot of tests * more test fixes * nit * more test fixes + some features to simplify code more * more cleanup * forgot that one * autodocs * small consistency fixes * fix regression * small fixes * dia feature extraction * docs * wip processor * fix processor order * processing goes brrr * transpose before * small fix * fix major bug but needs now a closer look into the custom processors esp cfg * small thing on logits * nits * simplify indices and shifts * add simpler version of padding tests back (temporarily) * add logit processor tests * starting tests on processor * fix mask application during generation * some fixes on the weights conversion * style + fixup logits order * simplify conversion * nit * remove padding tests * nits on modeling * hmm * fix tests * trigger * probably gonna be reverted, just a quick design around audio tokenizer * fixup typing * post merge + more typing * initial design for audio tokenizer * more design changes * nit * more processor tests and style related things * add to init * protect import * not sure why tbh * add another protect * more fixes * wow * it aint stopping :D * another missed type issue * ... * change design around audio tokenizer to prioritize init and go for auto - in regards to the review * change to new causal mask function + docstrings * change ternary * docs * remove todo, i dont think its essential tbh * remove pipeline as current pipelines do not fit in the current scheme, same as csm * closer to wrapping up the processor * text to audio, just for demo purposes (will likely be reverted) * check if it's this * save audio function * ensure no grad * fixes on prefixed audio, hop length is used via preprocess dac, device fixes * integration tests (tested locally on a100) + some processor utils / fixes * style * nits * another round of smaller things * docs + some fixes (generate one might be big) * msytery solved * small fix on conversion * add abstract audio tokenizer, change init check to abstract class * nits * update docs + fix some processing :D * change inheritance scheme for audio tokenizer * delete dead / unnecessary code in copied generate loop * last nits on new pipeline behavior (+ todo on tests) + style * trigger --------- Co-authored-by: Arthur Zucker <arthur.zucker@gmail.com> Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> Co-authored-by: Vasqu <antonprogamer@gmail.com>
402 lines
8.8 KiB
Markdown
402 lines
8.8 KiB
Markdown
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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# Auto Classes
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In many cases, the architecture you want to use can be guessed from the name or the path of the pretrained model you
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are supplying to the `from_pretrained()` method. AutoClasses are here to do this job for you so that you
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automatically retrieve the relevant model given the name/path to the pretrained weights/config/vocabulary.
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Instantiating one of [`AutoConfig`], [`AutoModel`], and
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[`AutoTokenizer`] will directly create a class of the relevant architecture. For instance
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```python
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model = AutoModel.from_pretrained("google-bert/bert-base-cased")
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```
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will create a model that is an instance of [`BertModel`].
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There is one class of `AutoModel` for each task, and for each backend (PyTorch, TensorFlow, or Flax).
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## Extending the Auto Classes
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Each of the auto classes has a method to be extended with your custom classes. For instance, if you have defined a
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custom class of model `NewModel`, make sure you have a `NewModelConfig` then you can add those to the auto
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classes like this:
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```python
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from transformers import AutoConfig, AutoModel
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AutoConfig.register("new-model", NewModelConfig)
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AutoModel.register(NewModelConfig, NewModel)
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```
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You will then be able to use the auto classes like you would usually do!
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<Tip warning={true}>
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If your `NewModelConfig` is a subclass of [`~transformers.PretrainedConfig`], make sure its
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`model_type` attribute is set to the same key you use when registering the config (here `"new-model"`).
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Likewise, if your `NewModel` is a subclass of [`PreTrainedModel`], make sure its
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`config_class` attribute is set to the same class you use when registering the model (here
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`NewModelConfig`).
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</Tip>
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## AutoConfig
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[[autodoc]] AutoConfig
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## AutoTokenizer
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[[autodoc]] AutoTokenizer
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## AutoFeatureExtractor
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[[autodoc]] AutoFeatureExtractor
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## AutoImageProcessor
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[[autodoc]] AutoImageProcessor
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## AutoVideoProcessor
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[[autodoc]] AutoVideoProcessor
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## AutoProcessor
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[[autodoc]] AutoProcessor
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## Generic model classes
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The following auto classes are available for instantiating a base model class without a specific head.
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### AutoModel
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[[autodoc]] AutoModel
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### TFAutoModel
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[[autodoc]] TFAutoModel
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### FlaxAutoModel
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[[autodoc]] FlaxAutoModel
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## Generic pretraining classes
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The following auto classes are available for instantiating a model with a pretraining head.
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### AutoModelForPreTraining
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[[autodoc]] AutoModelForPreTraining
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### TFAutoModelForPreTraining
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[[autodoc]] TFAutoModelForPreTraining
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### FlaxAutoModelForPreTraining
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[[autodoc]] FlaxAutoModelForPreTraining
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## Natural Language Processing
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The following auto classes are available for the following natural language processing tasks.
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### AutoModelForCausalLM
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[[autodoc]] AutoModelForCausalLM
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### TFAutoModelForCausalLM
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[[autodoc]] TFAutoModelForCausalLM
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### FlaxAutoModelForCausalLM
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[[autodoc]] FlaxAutoModelForCausalLM
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### AutoModelForMaskedLM
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[[autodoc]] AutoModelForMaskedLM
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### TFAutoModelForMaskedLM
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[[autodoc]] TFAutoModelForMaskedLM
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### FlaxAutoModelForMaskedLM
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[[autodoc]] FlaxAutoModelForMaskedLM
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### AutoModelForMaskGeneration
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[[autodoc]] AutoModelForMaskGeneration
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### TFAutoModelForMaskGeneration
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[[autodoc]] TFAutoModelForMaskGeneration
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### AutoModelForSeq2SeqLM
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[[autodoc]] AutoModelForSeq2SeqLM
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### TFAutoModelForSeq2SeqLM
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[[autodoc]] TFAutoModelForSeq2SeqLM
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### FlaxAutoModelForSeq2SeqLM
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[[autodoc]] FlaxAutoModelForSeq2SeqLM
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### AutoModelForSequenceClassification
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[[autodoc]] AutoModelForSequenceClassification
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### TFAutoModelForSequenceClassification
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[[autodoc]] TFAutoModelForSequenceClassification
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### FlaxAutoModelForSequenceClassification
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[[autodoc]] FlaxAutoModelForSequenceClassification
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### AutoModelForMultipleChoice
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[[autodoc]] AutoModelForMultipleChoice
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### TFAutoModelForMultipleChoice
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[[autodoc]] TFAutoModelForMultipleChoice
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### FlaxAutoModelForMultipleChoice
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[[autodoc]] FlaxAutoModelForMultipleChoice
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### AutoModelForNextSentencePrediction
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[[autodoc]] AutoModelForNextSentencePrediction
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### TFAutoModelForNextSentencePrediction
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[[autodoc]] TFAutoModelForNextSentencePrediction
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### FlaxAutoModelForNextSentencePrediction
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[[autodoc]] FlaxAutoModelForNextSentencePrediction
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### AutoModelForTokenClassification
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[[autodoc]] AutoModelForTokenClassification
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### TFAutoModelForTokenClassification
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[[autodoc]] TFAutoModelForTokenClassification
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### FlaxAutoModelForTokenClassification
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[[autodoc]] FlaxAutoModelForTokenClassification
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### AutoModelForQuestionAnswering
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[[autodoc]] AutoModelForQuestionAnswering
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### TFAutoModelForQuestionAnswering
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[[autodoc]] TFAutoModelForQuestionAnswering
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### FlaxAutoModelForQuestionAnswering
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[[autodoc]] FlaxAutoModelForQuestionAnswering
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### AutoModelForTextEncoding
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[[autodoc]] AutoModelForTextEncoding
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### TFAutoModelForTextEncoding
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[[autodoc]] TFAutoModelForTextEncoding
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## Computer vision
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The following auto classes are available for the following computer vision tasks.
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### AutoModelForDepthEstimation
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[[autodoc]] AutoModelForDepthEstimation
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### AutoModelForImageClassification
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[[autodoc]] AutoModelForImageClassification
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### TFAutoModelForImageClassification
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[[autodoc]] TFAutoModelForImageClassification
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### FlaxAutoModelForImageClassification
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[[autodoc]] FlaxAutoModelForImageClassification
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### AutoModelForVideoClassification
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[[autodoc]] AutoModelForVideoClassification
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### AutoModelForKeypointDetection
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[[autodoc]] AutoModelForKeypointDetection
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### AutoModelForMaskedImageModeling
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[[autodoc]] AutoModelForMaskedImageModeling
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### TFAutoModelForMaskedImageModeling
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[[autodoc]] TFAutoModelForMaskedImageModeling
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### AutoModelForObjectDetection
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[[autodoc]] AutoModelForObjectDetection
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### AutoModelForImageSegmentation
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[[autodoc]] AutoModelForImageSegmentation
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### AutoModelForImageToImage
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[[autodoc]] AutoModelForImageToImage
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### AutoModelForSemanticSegmentation
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[[autodoc]] AutoModelForSemanticSegmentation
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### TFAutoModelForSemanticSegmentation
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[[autodoc]] TFAutoModelForSemanticSegmentation
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### AutoModelForInstanceSegmentation
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[[autodoc]] AutoModelForInstanceSegmentation
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### AutoModelForUniversalSegmentation
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[[autodoc]] AutoModelForUniversalSegmentation
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### AutoModelForZeroShotImageClassification
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[[autodoc]] AutoModelForZeroShotImageClassification
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### TFAutoModelForZeroShotImageClassification
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[[autodoc]] TFAutoModelForZeroShotImageClassification
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### AutoModelForZeroShotObjectDetection
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[[autodoc]] AutoModelForZeroShotObjectDetection
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## Audio
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The following auto classes are available for the following audio tasks.
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### AutoModelForAudioClassification
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[[autodoc]] AutoModelForAudioClassification
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### AutoModelForAudioFrameClassification
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[[autodoc]] TFAutoModelForAudioClassification
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### TFAutoModelForAudioFrameClassification
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[[autodoc]] AutoModelForAudioFrameClassification
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### AutoModelForCTC
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[[autodoc]] AutoModelForCTC
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### AutoModelForSpeechSeq2Seq
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[[autodoc]] AutoModelForSpeechSeq2Seq
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### TFAutoModelForSpeechSeq2Seq
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[[autodoc]] TFAutoModelForSpeechSeq2Seq
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### FlaxAutoModelForSpeechSeq2Seq
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[[autodoc]] FlaxAutoModelForSpeechSeq2Seq
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### AutoModelForAudioXVector
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[[autodoc]] AutoModelForAudioXVector
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### AutoModelForTextToSpectrogram
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[[autodoc]] AutoModelForTextToSpectrogram
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### AutoModelForTextToWaveform
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[[autodoc]] AutoModelForTextToWaveform
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### AutoModelForAudioTokenization
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[[autodoc]] AutoModelForAudioTokenization
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## Multimodal
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The following auto classes are available for the following multimodal tasks.
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### AutoModelForTableQuestionAnswering
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[[autodoc]] AutoModelForTableQuestionAnswering
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### TFAutoModelForTableQuestionAnswering
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[[autodoc]] TFAutoModelForTableQuestionAnswering
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### AutoModelForDocumentQuestionAnswering
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[[autodoc]] AutoModelForDocumentQuestionAnswering
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### TFAutoModelForDocumentQuestionAnswering
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[[autodoc]] TFAutoModelForDocumentQuestionAnswering
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### AutoModelForVisualQuestionAnswering
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[[autodoc]] AutoModelForVisualQuestionAnswering
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### AutoModelForVision2Seq
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[[autodoc]] AutoModelForVision2Seq
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### TFAutoModelForVision2Seq
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[[autodoc]] TFAutoModelForVision2Seq
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### FlaxAutoModelForVision2Seq
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[[autodoc]] FlaxAutoModelForVision2Seq
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### AutoModelForImageTextToText
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[[autodoc]] AutoModelForImageTextToText
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## Time Series
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### AutoModelForTimeSeriesPrediction
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[[autodoc]] AutoModelForTimeSeriesPrediction
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