# RoFormer

## Overview The RoFormer model was proposed in [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/pdf/2104.09864v1.pdf) by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu. The abstract from the paper is the following: *Position encoding in transformer architecture provides supervision for dependency modeling between elements at different positions in the sequence. We investigate various methods to encode positional information in transformer-based language models and propose a novel implementation named Rotary Position Embedding(RoPE). The proposed RoPE encodes absolute positional information with rotation matrix and naturally incorporates explicit relative position dependency in self-attention formulation. Notably, RoPE comes with valuable properties such as flexibility of being expand to any sequence lengths, decaying inter-token dependency with increasing relative distances, and capability of equipping the linear self-attention with relative position encoding. As a result, the enhanced transformer with rotary position embedding, or RoFormer, achieves superior performance in tasks with long texts. We release the theoretical analysis along with some preliminary experiment results on Chinese data. The undergoing experiment for English benchmark will soon be updated.* This model was contributed by [junnyu](https://huggingface.co/junnyu). The original code can be found [here](https://github.com/ZhuiyiTechnology/roformer). ## Usage tips RoFormer is a BERT-like autoencoding model with rotary position embeddings. Rotary position embeddings have shown improved performance on classification tasks with long texts. ## 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) ## RoFormerConfig [[autodoc]] RoFormerConfig ## RoFormerTokenizer [[autodoc]] RoFormerTokenizer - build_inputs_with_special_tokens - get_special_tokens_mask - create_token_type_ids_from_sequences - save_vocabulary ## RoFormerTokenizerFast [[autodoc]] RoFormerTokenizerFast - build_inputs_with_special_tokens ## RoFormerModel [[autodoc]] RoFormerModel - forward ## RoFormerForCausalLM [[autodoc]] RoFormerForCausalLM - forward ## RoFormerForMaskedLM [[autodoc]] RoFormerForMaskedLM - forward ## RoFormerForSequenceClassification [[autodoc]] RoFormerForSequenceClassification - forward ## RoFormerForMultipleChoice [[autodoc]] RoFormerForMultipleChoice - forward ## RoFormerForTokenClassification [[autodoc]] RoFormerForTokenClassification - forward ## RoFormerForQuestionAnswering [[autodoc]] RoFormerForQuestionAnswering - forward ## TFRoFormerModel [[autodoc]] TFRoFormerModel - call ## TFRoFormerForMaskedLM [[autodoc]] TFRoFormerForMaskedLM - call ## TFRoFormerForCausalLM [[autodoc]] TFRoFormerForCausalLM - call ## TFRoFormerForSequenceClassification [[autodoc]] TFRoFormerForSequenceClassification - call ## TFRoFormerForMultipleChoice [[autodoc]] TFRoFormerForMultipleChoice - call ## TFRoFormerForTokenClassification [[autodoc]] TFRoFormerForTokenClassification - call ## TFRoFormerForQuestionAnswering [[autodoc]] TFRoFormerForQuestionAnswering - call ## FlaxRoFormerModel [[autodoc]] FlaxRoFormerModel - __call__ ## FlaxRoFormerForMaskedLM [[autodoc]] FlaxRoFormerForMaskedLM - __call__ ## FlaxRoFormerForSequenceClassification [[autodoc]] FlaxRoFormerForSequenceClassification - __call__ ## FlaxRoFormerForMultipleChoice [[autodoc]] FlaxRoFormerForMultipleChoice - __call__ ## FlaxRoFormerForTokenClassification [[autodoc]] FlaxRoFormerForTokenClassification - __call__ ## FlaxRoFormerForQuestionAnswering [[autodoc]] FlaxRoFormerForQuestionAnswering - __call__