@prefix rdf: . @prefix sl: . @prefix skos: . @prefix rdfs: . @prefix tag: . @prefix foaf: . @prefix dc: . tag:lora a sl:Tag ; skos:prefLabel "LoRA" . tag:arxiv_doc a sl:Tag ; skos:prefLabel "Arxiv Doc" . tag:tweet a sl:Tag ; skos:prefLabel "Tweet" . tag:hugging_face_dev a sl:Tag ; skos:prefLabel "Hugging Face: dev" . dc:title "peft/examples/token_classification/peft_lora_token_cls.ipynb at main · huggingface/peft" ; sl:creationDate "2023-08-27" ; sl:tag tag:parameter_efficient_fine_tuning_peft , tag:nlp_sample_code , tag:lora , tag:layoutlm , tag:hugging_face_dev . tag:tutorial a sl:Tag ; skos:prefLabel "Tutorial" . dc:title "[2111.15664] OCR-free Document Understanding Transformer" ; sl:comment "> The #LayoutLM family, used by a lot of document AI companies, gets a strong competitor: Donut, now available in Hugging Face Transformers! [src](https://www.linkedin.com/posts/niels-rogge-a3b7a3127_layoutlm-huggingface-transformers-activity-6963894171640205313-N2_U/)\r\n\r\n[HuggingFace Docs](https://huggingface.co/docs/transformers/main/en/model_doc/donut) ; [Gradio demo](https://huggingface.co/spaces/nielsr/donut-cord) ; [Tutorial notebooks](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/Donut)" ; sl:creationDate "2023-02-13" ; sl:tag tag:layoutlm , tag:document_processing , tag:arxiv_doc . dc:title "Revolutionizing Document AI with Multimodal Document Foundation Models - Microsoft Research" ; sl:creationDate "2023-01-30" ; sl:tag tag:nlp_microsoft , tag:microsoft_research , tag:layoutlm , tag:document_processing . dc:title "Document AI: LiLT a better language agnostic LayoutLM model" ; sl:creationDate "2022-11-22" ; sl:tag tag:multilingual_nlp , tag:layoutlm , tag:document_processing . dc:title "[Tutorial] How to Train LayoutLM on a Custom Dataset with Hugging Face" ; sl:comment "> This guide is intended to walk you through the process of training LayoutLM on your own custom documents." ; sl:creationDate "2023-01-09" ; sl:tag tag:tutorial , tag:layoutlm , tag:hugging_face , tag:annotation_tools . dc:title "Philip Vollet sur Twitter : \"Extracting information from PDFs or scanned documents is still a challenge! Use the @huggingface LayoutLMv3 model and Prodigy...\"" ; sl:comment "[A framework for designing document processing solutions](doc:2022/09/a_framework_for_designing_docum)" ; sl:creationDate "2022-09-02" ; sl:tag tag:tweet , tag:pdf_parsing , tag:layoutlm , tag:2d_nlp . tag:huggingface_transformers a sl:Tag ; skos:prefLabel "huggingface / transformers" . dc:title "LayoutLM" ; sl:comment "> The LayoutLM model was proposed in the paper [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](doc:2022/10/1912_13318_layoutlm_pre_trai). It’s a simple but effective pretraining method of text and layout for document image understanding and information extraction tasks, such as form understanding and receipt understanding." ; sl:creationDate "2022-10-04" ; sl:tag tag:layoutlm . tag:prodigy a sl:Tag ; skos:prefLabel "Prodigy" . dc:title "A framework for designing document processing solutions" ; sl:creationDate "2022-09-02" ; sl:tag tag:prodigy , tag:layoutlm , tag:document_processing , tag:annotations_ml , tag:2d_nlp . tag:2d_nlp a sl:Tag ; skos:prefLabel "2D-NLP" . tag:annotation_tools a sl:Tag ; skos:prefLabel "Annotation tools" . dc:title "Document AI: Fine-tuning LayoutLM for document-understanding using Hugging Face Transformers" ; sl:creationDate "2022-10-04" ; sl:tag tag:layoutlm , tag:document_processing . tag:microsoft_research a sl:Tag ; skos:prefLabel "Microsoft Research" . tag:hugging_face a sl:Tag ; skos:prefLabel "Hugging Face" . dc:title "[1912.13318] LayoutLM: Pre-training of Text and Layout for Document Image Understanding" ; sl:comment "> we propose the LayoutLM to jointly model interactions between text and layout information across scanned document images, which is beneficial for a great number of real-world document image understanding tasks such as information extraction from scanned documents. Furthermore, we also leverage image features to incorporate words' visual information into LayoutLM. To the best of our knowledge, this is the first time that text and layout are jointly learned in a single framework for document-level pre-training\r\n\r\n[At Hugging Face](doc:2022/10/layoutlm)" ; sl:creationDate "2022-10-04" ; sl:tag tag:scanned_documents , tag:layoutlm , tag:good , tag:arxiv_doc . tag:multilingual_nlp a sl:Tag ; skos:prefLabel "Multilingual NLP" . tag:pdf_parsing a sl:Tag ; skos:prefLabel "pdf parsing" . tag:nlp_microsoft a sl:Tag ; skos:prefLabel "NLP@Microsoft" . tag:layout_parser a sl:Tag ; skos:prefLabel "Layout-Parser" . tag:vision_language_models a sl:Tag ; skos:prefLabel "Vision Language Models" . dc:title "Data-Efficient Information Extraction from Documents with Pre-Trained Language Models" ; sl:creationDate "2023-02-14" ; sl:tag tag:layoutlm , tag:document_processing . tag:layoutlmv3 a sl:Tag ; skos:broader tag:layoutlm ; skos:prefLabel "LayoutLMv3" . tag:annotations_ml a sl:Tag ; skos:prefLabel "Annotations (ML)" . tag:scanned_documents a sl:Tag ; skos:prefLabel "Scanned documents" . tag:good a sl:Tag ; skos:prefLabel "Good" . tag:parameter_efficient_fine_tuning_peft a sl:Tag ; skos:prefLabel "Parameter-efficient fine-tuning (PEFT)" . tag:nlp_sample_code a sl:Tag ; skos:prefLabel "NLP sample code" . dc:title "LayoutLM Explained" ; sl:creationDate "2022-12-21" ; sl:tag tag:tutorial , tag:layoutlm . tag:document_processing a sl:Tag ; skos:prefLabel "Document AI" . tag:layoutlm a sl:Tag ; rdfs:isDefinedBy tag:layoutlm.n3 ; skos:broader tag:scanned_documents , tag:document_processing , tag:multimodal_models , tag:2d_nlp ; skos:prefLabel "LayoutLM" ; skos:related tag:layout_parser , tag:huggingface_transformers , tag:nlp_microsoft , tag:vision_language_models ; foaf:page tag:layoutlm.html . tag:multimodal_models a sl:Tag ; skos:prefLabel "Multimodal Models" .