# RemBERT

## Overview

The RemBERT model was proposed in [Rethinking Embedding Coupling in Pre-trained Language Models](https://arxiv.org/abs/2010.12821) by Hyung Won Chung, Thibault Févry, Henry Tsai, Melvin Johnson, Sebastian Ruder.

The abstract from the paper is the following:

*We re-evaluate the standard practice of sharing weights between input and output embeddings in state-of-the-art

pre-trained language models. We show that decoupled embeddings provide increased modeling flexibility, allowing us to

significantly improve the efficiency of parameter allocation in the input embedding of multilingual models. By

reallocating the input embedding parameters in the Transformer layers, we achieve dramatically better performance on

standard natural language understanding tasks with the same number of parameters during fine-tuning. We also show that

allocating additional capacity to the output embedding provides benefits to the model that persist through the

fine-tuning stage even though the output embedding is discarded after pre-training. Our analysis shows that larger

output embeddings prevent the model's last layers from overspecializing to the pre-training task and encourage

Transformer representations to be more general and more transferable to other tasks and languages. Harnessing these

findings, we are able to train models that achieve strong performance on the XTREME benchmark without increasing the

number of parameters at the fine-tuning stage.*

## Usage tips

For fine-tuning, RemBERT can be thought of as a bigger version of mBERT with an ALBERT-like factorization of the

embedding layer. The embeddings are not tied in pre-training, in contrast with BERT, which enables smaller input

embeddings (preserved during fine-tuning) and bigger output embeddings (discarded at fine-tuning). The tokenizer is

also similar to the Albert one rather than the BERT one.

## Resources

- [Text classification task guide](../tasks/sequence_classification)

- [Token classification task guide](../tasks/token_classification)

- [Question answering task guide](../tasks/question_answering)

- [Causal language modeling task guide](../tasks/language_modeling)

- [Masked language modeling task guide](../tasks/masked_language_modeling)

- [Multiple choice task guide](../tasks/multiple_choice)

## RemBertConfig

[[autodoc]] RemBertConfig

## RemBertTokenizer

[[autodoc]] RemBertTokenizer

    - build_inputs_with_special_tokens

    - get_special_tokens_mask

    - create_token_type_ids_from_sequences

    - save_vocabulary

## RemBertTokenizerFast

[[autodoc]] RemBertTokenizerFast

    - build_inputs_with_special_tokens

    - get_special_tokens_mask

    - create_token_type_ids_from_sequences

    - save_vocabulary

## RemBertModel

[[autodoc]] RemBertModel

    - forward

## RemBertForCausalLM

[[autodoc]] RemBertForCausalLM

    - forward

## RemBertForMaskedLM

[[autodoc]] RemBertForMaskedLM

    - forward

## RemBertForSequenceClassification

[[autodoc]] RemBertForSequenceClassification

    - forward

## RemBertForMultipleChoice

[[autodoc]] RemBertForMultipleChoice

    - forward

## RemBertForTokenClassification

[[autodoc]] RemBertForTokenClassification

    - forward

## RemBertForQuestionAnswering

[[autodoc]] RemBertForQuestionAnswering

    - forward

## TFRemBertModel

[[autodoc]] TFRemBertModel

    - call

## TFRemBertForMaskedLM

[[autodoc]] TFRemBertForMaskedLM

    - call

## TFRemBertForCausalLM

[[autodoc]] TFRemBertForCausalLM

    - call

## TFRemBertForSequenceClassification

[[autodoc]] TFRemBertForSequenceClassification

    - call

## TFRemBertForMultipleChoice

[[autodoc]] TFRemBertForMultipleChoice

    - call

## TFRemBertForTokenClassification

[[autodoc]] TFRemBertForTokenClassification

    - call

## TFRemBertForQuestionAnswering

[[autodoc]] TFRemBertForQuestionAnswering

    - call