https://github.com/PaddlePaddle/PaddleNLP/tree/develop/model_zoo/ernie-layout
공식 코드ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding
Papers with Code 아카이브
Semantic entity labeling
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 2
- 보존된 평가 기록
- 23
- One of Form Understanding task (Word grouping, Semantic entity labeling, Entity linking)
- Classifying entities into one of four pre-defined categories: question, answer, header and, other.
cited from
G. Jaume, H. K. Ekenel, J. Thiran "FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents," 2019
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/PaddlePaddle/VIMER/tree/main/StrucTexT/v2
공식 코드StrucTexTv2: Masked Visual-Textual Prediction for Document Image Pre-training
https://github.com/ZeningLin/PEneo
공식 코드https://github.com/alibabaresearch/advancedliteratemachinery
공식 코드GeoLayoutLM: Geometric Pre-training for Visual Information Extraction
https://github.com/andreagemelli/doc2graph
공식 코드Doc2Graph: a Task Agnostic Document Understanding Framework based on Graph Neural Networks
https://github.com/chongzhangFDU/ROOR
공식 코드Rethinking the Evaluation of Pre-trained Text-and-Layout Models from an Entity-Centric Perspective
https://github.com/chongzhangfdu/tpp
공식 코드Reading Order Matters: Information Extraction from Visually-rich Documents by Token Path Prediction
https://github.com/jpwang/lilt
공식 코드https://github.com/microsoft/unilm
공식 코드LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
https://github.com/microsoft/unilm/tree/master/layoutlmv3
공식 코드LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking
https://github.com/microsoft/unilm/tree/master/xdoc
공식 코드XDoc: Unified Pre-training for Cross-Format Document Understanding
https://github.com/MS-P3/code3/tree/main/layoutlmv2
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
https://github.com/MS-P3/code3/tree/main/lilt
https://github.com/MindSpore-scientific-2/code-14/tree/main/layoutlmv2
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
https://github.com/MindSpore-scientific-2/code-14/tree/main/layoutlmv3
LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking
https://github.com/MindSpore-scientific-2/code-14/tree/main/lilt
https://github.com/MindSpore-scientific/code-7/tree/main/LayoutLMv2
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
https://github.com/NormXU/ERNIE-Layout-Pytorch
ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/paddlenlp/transformers/layoutlmv2
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
https://github.com/PaddlePaddle/PaddleOCR
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
https://github.com/WinterShiver/Token-Path-Prediction
Reading Order Matters: Information Extraction from Visually-rich Documents by Token Path Prediction
https://github.com/facebookresearch/data2vec_vision
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
https://github.com/huggingface/transformers
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
https://github.com/pwc-1/Paper-9/tree/main/layoutlmv2
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
https://github.com/pwc-1/Paper-9/tree/main/layoutlmv3
LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking
https://github.com/pwc-1/Paper-9/tree/main/lilt
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.