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

Resources

Text classification task guide
Token classification task guide
Question answering task guide
Causal language modeling task guide
Masked language modeling task guide
Multiple choice task guide

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

