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docs/source/en/model_doc/roformer.mdx

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+<!--Copyright 2021 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# 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.*
+
+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.
+
+
+This model was contributed by [junnyu](https://huggingface.co/junnyu). The original code can be found [here](https://github.com/ZhuiyiTechnology/roformer).
+
+## 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__