The LayoutLMv3 model was proposed in LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking by Yupan Huang, Tengchao Lv, Lei Cui, Yutong Lu, Furu Wei. LayoutLMv3 simplifies LayoutLMv2 by using patch embeddings (as in ViT) instead of leveraging a CNN backbone, and pre-trains the model on 3 objectives: masked language modeling (MLM), masked image modeling (MIM) and word-patch alignment (WPA).
The abstract from the paper is the following:
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.
Tips:
- In terms of data processing, LayoutLMv3 is identical to its predecessor LayoutLMv2, except that:
- 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.
- text is tokenized using byte-pair encoding (BPE), as opposed to WordPiece.
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.
- Regarding usage of [
LayoutLMv3Processor], we refer to the usage guide of its predecessor. - Demo notebooks for LayoutLMv3 can be found here.
LayoutLMv3 architecture. Taken from the original paper.
This model was contributed by nielsr. The original code can be found here.
[[autodoc]] LayoutLMv3Config
[[autodoc]] LayoutLMv3FeatureExtractor - call
[[autodoc]] LayoutLMv3Tokenizer - call - save_vocabulary
[[autodoc]] LayoutLMv3TokenizerFast - call
[[autodoc]] LayoutLMv3Processor - call
[[autodoc]] LayoutLMv3Model - forward
[[autodoc]] LayoutLMv3ForSequenceClassification - forward
[[autodoc]] LayoutLMv3ForTokenClassification - forward
[[autodoc]] LayoutLMv3ForQuestionAnswering - forward