data2vec.mdx

Data2Vec

Overview

The Data2Vec model was proposed in data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language 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 and patrickvonplaten. sayakpaul contributed Data2Vec for vision in TensorFlow.

The original code (for NLP and Speech) can be found here. The original code for vision can be found here.

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