docs/source/en/model_doc/layoutlmv3.md
This model was released on 2022-04-18 and added to Hugging Face Transformers on 2022-05-24.
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.
<small> LayoutLMv3 architecture. Taken from the <a href="https://huggingface.co/papers/2204.08387">original paper</a>. </small>
This model was contributed by nielsr. The original code can be found here.
LayoutLMv3Processor] which internally combines a [LayoutLMv3ImageProcessor] (for the image modality) and a [LayoutLMv3Tokenizer]/[LayoutLMv3TokenizerFast] (for the text modality) to prepare all data for the model.LayoutLMv3Processor], we refer to the usage guide of its predecessor.A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with LayoutLMv3. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
<Tip>LayoutLMv3 is nearly identical to LayoutLMv2, so we've also included LayoutLMv2 resources you can adapt for LayoutLMv3 tasks. For these notebooks, take care to use [LayoutLMv2Processor] instead when preparing data for the model!
LayoutLMv2ForSequenceClassification] is supported by this notebook.LayoutLMv3ForTokenClassification] is supported by this example script and notebook.LayoutLMv2ForTokenClassification] and a notebook for how to perform inference when no labels are available with [LayoutLMv2ForTokenClassification].LayoutLMv2ForTokenClassification] with the 🤗 Trainer.LayoutLMv2ForQuestionAnswering] is supported by this notebook.Document question answering
[[autodoc]] LayoutLMv3Config
[[autodoc]] LayoutLMv3ImageProcessor - preprocess
[[autodoc]] LayoutLMv3ImageProcessorPil - preprocess
[[autodoc]] LayoutLMv3Tokenizer - call - save_vocabulary
[[autodoc]] LayoutLMv3TokenizerFast - call
[[autodoc]] LayoutLMv3Processor - call
[[autodoc]] LayoutLMv3Model - forward
[[autodoc]] LayoutLMv3ForSequenceClassification - forward
[[autodoc]] LayoutLMv3ForTokenClassification - forward
[[autodoc]] LayoutLMv3ForQuestionAnswering - forward