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* Reorganize doc for multilingual support * Fix style * Style * Toc trees * Adapt templates
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47 lines
1.8 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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-->
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# Vision Encoder Decoder Models
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The [`VisionEncoderDecoderModel`] can be used to initialize an image-to-text-sequence model with any
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pretrained Transformer-based vision autoencoding model as the encoder (*e.g.* [ViT](vit), [BEiT](beit), [DeiT](deit), [Swin](swin))
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and any pretrained language model as the decoder (*e.g.* [RoBERTa](roberta), [GPT2](gpt2), [BERT](bert), [DistilBERT](distilbert)).
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The effectiveness of initializing image-to-text-sequence models with pretrained checkpoints has been shown in (for
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example) [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang,
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Zhoujun Li, Furu Wei.
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An example of how to use a [`VisionEncoderDecoderModel`] for inference can be seen in [TrOCR](trocr).
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## VisionEncoderDecoderConfig
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[[autodoc]] VisionEncoderDecoderConfig
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## VisionEncoderDecoderModel
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[[autodoc]] VisionEncoderDecoderModel
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- forward
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- from_encoder_decoder_pretrained
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## TFVisionEncoderDecoderModel
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[[autodoc]] TFVisionEncoderDecoderModel
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- call
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- from_encoder_decoder_pretrained
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## FlaxVisionEncoderDecoderModel
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[[autodoc]] FlaxVisionEncoderDecoderModel
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- __call__
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- from_encoder_decoder_pretrained
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