<!--Copyright 2022 The HuggingFace Team. All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->

# Data2Vec

## Overview

The Data2Vec model was proposed in [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language](https://arxiv.org/pdf/2202.03555) by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu and Michael Auli.
Data2Vec proposes a unified framework for self-supervised learning across different data modalities - text, audio and images.
Importantly, predicted targets for pre-training are contextualized latent representations of the inputs, rather than modality-specific, context-independent targets.

The abstract from the paper is the following:

*While the general idea of self-supervised learning is identical across modalities, the actual algorithms and
objectives differ widely because they were developed with a single modality in mind. To get us closer to general
self-supervised learning, we present data2vec, a framework that uses the same learning method for either speech,
NLP or computer vision. The core idea is to predict latent representations of the full input data based on a
masked view of the input in a selfdistillation setup using a standard Transformer architecture.
Instead of predicting modality-specific targets such as words, visual tokens or units of human speech which
are local in nature, data2vec predicts contextualized latent representations that contain information from
the entire input. Experiments on the major benchmarks of speech recognition, image classification, and
natural language understanding demonstrate a new state of the art or competitive performance to predominant approaches.
Models and code are available at www.github.com/pytorch/fairseq/tree/master/examples/data2vec.*

Tips:

- Data2VecAudio, Data2VecText, and Data2VecVision have all been trained using the same self-supervised learning method.
- For Data2VecAudio, preprocessing is identical to [`Wav2Vec2Model`], including feature extraction
- For Data2VecText, preprocessing is identical to [`RobertaModel`], including tokenization.
- For Data2VecVision, preprocessing is identical to [`BeitModel`], including feature extraction.

This model was contributed by [edugp](https://huggingface.co/edugp) and [patrickvonplaten](https://huggingface.co/patrickvonplaten).
[sayakpaul](https://github.com/sayakpaul) contributed Data2Vec for vision in TensorFlow.

The original code (for NLP and Speech) can be found [here](https://github.com/pytorch/fairseq/tree/main/examples/data2vec).
The original code for vision can be found [here](https://github.com/facebookresearch/data2vec_vision/tree/main/beit).


## Data2VecTextConfig

[[autodoc]] Data2VecTextConfig

## Data2VecAudioConfig

[[autodoc]] Data2VecAudioConfig

## Data2VecVisionConfig

[[autodoc]] Data2VecVisionConfig


## Data2VecAudioModel

[[autodoc]] Data2VecAudioModel
    - forward

## Data2VecAudioForAudioFrameClassification

[[autodoc]] Data2VecAudioForAudioFrameClassification
    - forward

## Data2VecAudioForCTC

[[autodoc]] Data2VecAudioForCTC
    - forward

## Data2VecAudioForSequenceClassification

[[autodoc]] Data2VecAudioForSequenceClassification
    - forward

## Data2VecAudioForXVector

[[autodoc]] Data2VecAudioForXVector
    - forward

## Data2VecTextModel

[[autodoc]] Data2VecTextModel
    - forward

## Data2VecTextForCausalLM

[[autodoc]] Data2VecTextForCausalLM
    - forward

## Data2VecTextForMaskedLM

[[autodoc]] Data2VecTextForMaskedLM
    - forward

## Data2VecTextForSequenceClassification

[[autodoc]] Data2VecTextForSequenceClassification
    - forward

## Data2VecTextForMultipleChoice

[[autodoc]] Data2VecTextForMultipleChoice
    - forward

## Data2VecTextForTokenClassification

[[autodoc]] Data2VecTextForTokenClassification
    - forward

## Data2VecTextForQuestionAnswering

[[autodoc]] Data2VecTextForQuestionAnswering
    - forward

## Data2VecVisionModel

[[autodoc]] Data2VecVisionModel
    - forward

## Data2VecVisionForImageClassification

[[autodoc]] Data2VecVisionForImageClassification
    - forward

## Data2VecVisionForSemanticSegmentation

[[autodoc]] Data2VecVisionForSemanticSegmentation
    - forward

## TFData2VecVisionModel

[[autodoc]] TFData2VecVisionModel
    - call

## TFData2VecVisionForImageClassification

[[autodoc]] TFData2VecVisionForImageClassification
    - call