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111 lines
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111 lines
3.7 KiB
Plaintext
<!--Copyright 2022 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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# Data2Vec
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## Overview
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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.
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Data2Vec proposes a unified framework for self-supervised learning across different data modalities - text, audio and images.
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Importantly, predicted targets for pre-training are contextualized latent representations of the inputs, rather than modality-specific, context-independent targets.
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The abstract from the paper is the following:
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*While the general idea of self-supervised learning is identical across modalities, the actual algorithms and
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objectives differ widely because they were developed with a single modality in mind. To get us closer to general
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self-supervised learning, we present data2vec, a framework that uses the same learning method for either speech,
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NLP or computer vision. The core idea is to predict latent representations of the full input data based on a
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masked view of the input in a selfdistillation setup using a standard Transformer architecture.
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Instead of predicting modality-specific targets such as words, visual tokens or units of human speech which
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are local in nature, data2vec predicts contextualized latent representations that contain information from
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the entire input. Experiments on the major benchmarks of speech recognition, image classification, and
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natural language understanding demonstrate a new state of the art or competitive performance to predominant approaches.
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Models and code are available at www.github.com/pytorch/fairseq/tree/master/examples/data2vec.*
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Tips:
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- Both Data2VecAudio and Data2VecText have been trained using the same self-supervised learning method.
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In the case of Data2VecAudio, preprocessing is identical to [`RobertaModel`], including tokenization.
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This model was contributed by [edugp](https://huggingface.co/edugp).
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The original code can be found [here](https://github.com/pytorch/fairseq/tree/main/examples/data2vec).
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## Data2VecTextConfig
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[[autodoc]] Data2VecTextConfig
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## Data2VecAudioConfig
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[[autodoc]] Data2VecAudioConfig
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## Data2VecAudioModel
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[[autodoc]] Data2VecAudioModel
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- forward
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## Data2VecAudioForAudioFrameClassification
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[[autodoc]] Data2VecAudioForAudioFrameClassification
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- forward
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## Data2VecAudioForCTC
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[[autodoc]] Data2VecAudioForCTC
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- forward
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## Data2VecAudioForSequenceClassification
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[[autodoc]] Data2VecAudioForSequenceClassification
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- forward
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## Data2VecAudioForXVector
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[[autodoc]] Data2VecAudioForXVector
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- forward
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## Data2VecTextModel
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[[autodoc]] Data2VecTextModel
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- forward
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## Data2VecTextForCausalLM
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[[autodoc]] Data2VecTextForCausalLM
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- forward
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## Data2VecTextForMaskedLM
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[[autodoc]] Data2VecTextForMaskedLM
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- forward
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## Data2VecTextForSequenceClassification
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[[autodoc]] Data2VecTextForSequenceClassification
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- forward
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## Data2VecTextForMultipleChoice
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[[autodoc]] Data2VecTextForMultipleChoice
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- forward
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## Data2VecTextForTokenClassification
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[[autodoc]] Data2VecTextForTokenClassification
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- forward
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## Data2VecTextForQuestionAnswering
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[[autodoc]] Data2VecTextForQuestionAnswering
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- forward
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