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+*This model was released on 2020-10-24 and added to Hugging Face Transformers on 2021-07-24.*
+
+# RemBERT
+
+<div class="flex flex-wrap space-x-1">
+<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
+</div>
+
+## Overview
+
+The RemBERT model was proposed in [Rethinking Embedding Coupling in Pre-trained Language Models](https://huggingface.co/papers/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