transformers/docs/source/en/model_doc/rembert.md

RemBERT

Overview

The RemBERT model was proposed in Rethinking Embedding Coupling in Pre-trained Language Models 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
• Token classification task guide
• Question answering task guide
• Causal language modeling task guide
• Masked language modeling task guide
• Multiple choice task guide

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