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Co-authored-by: Esben Toke Christensen <esben.christensen@visma.com> Co-authored-by: Lasse Reedtz <lasse.reedtz@visma.com> Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
107 lines
4.9 KiB
Plaintext
107 lines
4.9 KiB
Plaintext
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# LayoutLMv3
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## Overview
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The LayoutLMv3 model was proposed in [LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking](https://arxiv.org/abs/2204.08387) by Yupan Huang, Tengchao Lv, Lei Cui, Yutong Lu, Furu Wei.
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LayoutLMv3 simplifies [LayoutLMv2](layoutlmv2) by using patch embeddings (as in [ViT](vit)) instead of leveraging a CNN backbone, and pre-trains the model on 3 objectives: masked language modeling (MLM), masked image modeling (MIM)
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and word-patch alignment (WPA).
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The abstract from the paper is the following:
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*Self-supervised pre-training techniques have achieved remarkable progress in Document AI. Most multimodal pre-trained models use a masked language modeling objective to learn bidirectional representations on the text modality, but they differ in pre-training objectives for the image modality. This discrepancy adds difficulty to multimodal representation learning. In this paper, we propose LayoutLMv3 to pre-train multimodal Transformers for Document AI with unified text and image masking. Additionally, LayoutLMv3 is pre-trained with a word-patch alignment objective to learn cross-modal alignment by predicting whether the corresponding image patch of a text word is masked. The simple unified architecture and training objectives make LayoutLMv3 a general-purpose pre-trained model for both text-centric and image-centric Document AI tasks. Experimental results show that LayoutLMv3 achieves state-of-the-art performance not only in text-centric tasks, including form understanding, receipt understanding, and document visual question answering, but also in image-centric tasks such as document image classification and document layout analysis.*
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Tips:
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- In terms of data processing, LayoutLMv3 is identical to its predecessor [LayoutLMv2](layoutlmv2), except that:
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- images need to be resized and normalized with channels in regular RGB format. LayoutLMv2 on the other hand normalizes the images internally and expects the channels in BGR format.
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- text is tokenized using byte-pair encoding (BPE), as opposed to WordPiece.
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Due to these differences in data preprocessing, one can use [`LayoutLMv3Processor`] which internally combines a [`LayoutLMv3FeatureExtractor`] (for the image modality) and a [`LayoutLMv3Tokenizer`]/[`LayoutLMv3TokenizerFast`] (for the text modality) to prepare all data for the model.
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- Regarding usage of [`LayoutLMv3Processor`], we refer to the [usage guide](layoutlmv2#usage-layoutlmv2processor) of its predecessor.
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- Demo notebooks for LayoutLMv3 can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LayoutLMv3).
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- Demo scripts can be found [here](https://github.com/huggingface/transformers/tree/main/examples/research_projects/layoutlmv3).
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/layoutlmv3_architecture.png"
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alt="drawing" width="600"/>
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<small> LayoutLMv3 architecture. Taken from the <a href="https://arxiv.org/abs/2204.08387">original paper</a>. </small>
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This model was contributed by [nielsr](https://huggingface.co/nielsr). The TensorFlow version of this model was added by [chriskoo](https://huggingface.co/chriskoo), [tokec](https://huggingface.co/tokec), and [lre](https://huggingface.co/lre). The original code can be found [here](https://github.com/microsoft/unilm/tree/master/layoutlmv3).
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## LayoutLMv3Config
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[[autodoc]] LayoutLMv3Config
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## LayoutLMv3FeatureExtractor
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[[autodoc]] LayoutLMv3FeatureExtractor
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- __call__
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## LayoutLMv3Tokenizer
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[[autodoc]] LayoutLMv3Tokenizer
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- __call__
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- save_vocabulary
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## LayoutLMv3TokenizerFast
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[[autodoc]] LayoutLMv3TokenizerFast
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- __call__
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## LayoutLMv3Processor
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[[autodoc]] LayoutLMv3Processor
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- __call__
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## LayoutLMv3Model
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[[autodoc]] LayoutLMv3Model
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- forward
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## LayoutLMv3ForSequenceClassification
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[[autodoc]] LayoutLMv3ForSequenceClassification
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- forward
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## LayoutLMv3ForTokenClassification
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[[autodoc]] LayoutLMv3ForTokenClassification
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- forward
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## LayoutLMv3ForQuestionAnswering
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[[autodoc]] LayoutLMv3ForQuestionAnswering
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- forward
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## TFLayoutLMv3Model
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[[autodoc]] TFLayoutLMv3Model
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- call
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## TFLayoutLMv3ForSequenceClassification
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[[autodoc]] TFLayoutLMv3ForSequenceClassification
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- call
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## TFLayoutLMv3ForTokenClassification
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[[autodoc]] TFLayoutLMv3ForTokenClassification
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- call
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## TFLayoutLMv3ForQuestionAnswering
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[[autodoc]] TFLayoutLMv3ForQuestionAnswering
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- call
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