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156 lines
4.2 KiB
Plaintext
<!--Copyright 2021 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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# RoFormer
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## Overview
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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.
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The abstract from the paper is the following:
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*Position encoding in transformer architecture provides supervision for dependency modeling between elements at
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different positions in the sequence. We investigate various methods to encode positional information in
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transformer-based language models and propose a novel implementation named Rotary Position Embedding(RoPE). The
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proposed RoPE encodes absolute positional information with rotation matrix and naturally incorporates explicit relative
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position dependency in self-attention formulation. Notably, RoPE comes with valuable properties such as flexibility of
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being expand to any sequence lengths, decaying inter-token dependency with increasing relative distances, and
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capability of equipping the linear self-attention with relative position encoding. As a result, the enhanced
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transformer with rotary position embedding, or RoFormer, achieves superior performance in tasks with long texts. We
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release the theoretical analysis along with some preliminary experiment results on Chinese data. The undergoing
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experiment for English benchmark will soon be updated.*
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Tips:
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- RoFormer is a BERT-like autoencoding model with rotary position embeddings. Rotary position embeddings have shown
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improved performance on classification tasks with long texts.
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This model was contributed by [junnyu](https://huggingface.co/junnyu). The original code can be found [here](https://github.com/ZhuiyiTechnology/roformer).
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## RoFormerConfig
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[[autodoc]] RoFormerConfig
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## RoFormerTokenizer
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[[autodoc]] RoFormerTokenizer
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- build_inputs_with_special_tokens
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- get_special_tokens_mask
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- create_token_type_ids_from_sequences
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- save_vocabulary
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## RoFormerTokenizerFast
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[[autodoc]] RoFormerTokenizerFast
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- build_inputs_with_special_tokens
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## RoFormerModel
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[[autodoc]] RoFormerModel
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- forward
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## RoFormerForCausalLM
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[[autodoc]] RoFormerForCausalLM
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- forward
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## RoFormerForMaskedLM
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[[autodoc]] RoFormerForMaskedLM
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- forward
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## RoFormerForSequenceClassification
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[[autodoc]] RoFormerForSequenceClassification
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- forward
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## RoFormerForMultipleChoice
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[[autodoc]] RoFormerForMultipleChoice
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- forward
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## RoFormerForTokenClassification
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[[autodoc]] RoFormerForTokenClassification
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- forward
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## RoFormerForQuestionAnswering
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[[autodoc]] RoFormerForQuestionAnswering
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- forward
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## TFRoFormerModel
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[[autodoc]] TFRoFormerModel
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- call
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## TFRoFormerForMaskedLM
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[[autodoc]] TFRoFormerForMaskedLM
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- call
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## TFRoFormerForCausalLM
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[[autodoc]] TFRoFormerForCausalLM
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- call
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## TFRoFormerForSequenceClassification
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[[autodoc]] TFRoFormerForSequenceClassification
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- call
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## TFRoFormerForMultipleChoice
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[[autodoc]] TFRoFormerForMultipleChoice
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- call
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## TFRoFormerForTokenClassification
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[[autodoc]] TFRoFormerForTokenClassification
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- call
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## TFRoFormerForQuestionAnswering
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[[autodoc]] TFRoFormerForQuestionAnswering
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- call
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## FlaxRoFormerModel
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[[autodoc]] FlaxRoFormerModel
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- __call__
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## FlaxRoFormerForMaskedLM
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[[autodoc]] FlaxRoFormerForMaskedLM
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- __call__
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## FlaxRoFormerForSequenceClassification
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[[autodoc]] FlaxRoFormerForSequenceClassification
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- __call__
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## FlaxRoFormerForMultipleChoice
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[[autodoc]] FlaxRoFormerForMultipleChoice
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- __call__
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## FlaxRoFormerForTokenClassification
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[[autodoc]] FlaxRoFormerForTokenClassification
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- __call__
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## FlaxRoFormerForQuestionAnswering
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[[autodoc]] FlaxRoFormerForQuestionAnswering
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- __call__
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