Data2vec8/9/2023 Although, you can provide your own encoder model. This implementation uses HuggingFace Transformers models as encoders for Data2Vec which you can inspect in the encoder.py files for each modality. HuggingFace Transformers/Fairseq models return transformer layers outputs separately out of the box. So the forward method must return outputs from all Encoder blocks of the transformer model. Masking: For each modality, the Dataset instance must return the masked source, the target and the mask tensor.įeature Extraction: Features are the outputs from the transformer/attention layers. The key concept is that there must be modality-specific feature extractions and masking strategies. This implementation differs in the fact that a single Data2Vec model is provided powered by a custom encoder (implemented using PyTorch + HuggingFace Transformers) and tries to unify the whole concept in a single module. This makes sense given the vastly different nature of the input data. Our primary is to design a single learning mechanism for different modalities.ĭespite the unified learning regime, we still use modality-specific features extractors and masking strategies. Implementationĭata2Vec is already implemented in fairseq in which for all modalities there is a seperate implementation (text, vision, audio). The loss is calculated from encoder outputs and teacher outputs.Encoder outputs are regressed by a projection block/layer.Optional normalizations are applied to the layers/outputs of the teacher.The teacher which is an EMA instance of the encoder (in eval model), extracts features from the unmasked inputs.These features are outputs of every transformer/linear layer. The encoder extracts features from the masked inputs.Such as understanding text for more spoken languages. This is a more scalable and efficient approach for machines to tackle new complex tasks, However, through self-supervised learning, machines are able to learn about the world just by observing itĪnd then figuring out the structure of images, speech or text. Most machines learn exclusively from labeled data. Data2vec-pytorch PyTorch implementation of " data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language" from Meta AI (FAIR) Disclaimer: This repo's goal is to make data2vec easier to understand hence it's not recommended to use for actual model pretraining but instead you'd better use the official version in fairseq or the ones provided on HuggingFace.ĭata2Vec is the first high-performance self-supervised algorithm that learns the same way in multiple modalities, including speech, vision and text.
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