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Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>

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Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>

2023-06-20 18:07:47 -04:00

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RoBERTa-PreLayerNorm

Overview

The RoBERTa-PreLayerNorm model was proposed in fairseq: A Fast, Extensible Toolkit for Sequence Modeling by Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, Michael Auli. It is identical to using the --encoder-normalize-before flag in fairseq.

The abstract from the paper is the following:

fairseq is an open-source sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling, and other text generation tasks. The toolkit is based on PyTorch and supports distributed training across multiple GPUs and machines. We also support fast mixed-precision training and inference on modern GPUs.

Tips:

The implementation is the same as Roberta except instead of using Add and Norm it does Norm and Add. Add and Norm refers to the Addition and LayerNormalization as described in Attention Is All You Need.
This is identical to using the --encoder-normalize-before flag in fairseq.

This model was contributed by andreasmaden. The original code can be found here.

Documentation resources

RobertaPreLayerNormConfig

autodoc RobertaPreLayerNormConfig

RobertaPreLayerNormModel

autodoc RobertaPreLayerNormModel - forward

RobertaPreLayerNormForCausalLM

autodoc RobertaPreLayerNormForCausalLM - forward

RobertaPreLayerNormForMaskedLM

autodoc RobertaPreLayerNormForMaskedLM - forward

RobertaPreLayerNormForSequenceClassification

autodoc RobertaPreLayerNormForSequenceClassification - forward

RobertaPreLayerNormForMultipleChoice

autodoc RobertaPreLayerNormForMultipleChoice - forward

RobertaPreLayerNormForTokenClassification

autodoc RobertaPreLayerNormForTokenClassification - forward

RobertaPreLayerNormForQuestionAnswering

autodoc RobertaPreLayerNormForQuestionAnswering - forward

TFRobertaPreLayerNormModel

autodoc TFRobertaPreLayerNormModel - call

TFRobertaPreLayerNormForCausalLM

autodoc TFRobertaPreLayerNormForCausalLM - call

TFRobertaPreLayerNormForMaskedLM

autodoc TFRobertaPreLayerNormForMaskedLM - call

TFRobertaPreLayerNormForSequenceClassification

autodoc TFRobertaPreLayerNormForSequenceClassification - call

TFRobertaPreLayerNormForMultipleChoice

autodoc TFRobertaPreLayerNormForMultipleChoice - call

TFRobertaPreLayerNormForTokenClassification

autodoc TFRobertaPreLayerNormForTokenClassification - call

TFRobertaPreLayerNormForQuestionAnswering

autodoc TFRobertaPreLayerNormForQuestionAnswering - call

FlaxRobertaPreLayerNormModel

autodoc FlaxRobertaPreLayerNormModel - call

FlaxRobertaPreLayerNormForCausalLM

autodoc FlaxRobertaPreLayerNormForCausalLM - call

FlaxRobertaPreLayerNormForMaskedLM

autodoc FlaxRobertaPreLayerNormForMaskedLM - call

FlaxRobertaPreLayerNormForSequenceClassification

autodoc FlaxRobertaPreLayerNormForSequenceClassification - call

FlaxRobertaPreLayerNormForMultipleChoice

autodoc FlaxRobertaPreLayerNormForMultipleChoice - call

FlaxRobertaPreLayerNormForTokenClassification

autodoc FlaxRobertaPreLayerNormForTokenClassification - call

FlaxRobertaPreLayerNormForQuestionAnswering

autodoc FlaxRobertaPreLayerNormForQuestionAnswering - call

4.7 KiB Raw Blame History