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Relation Extraction On Funsd
Metrics
F1
Results
Performance results of various models on this benchmark
| Paper Title | Repository | ||
|---|---|---|---|
| LayoutLMv3 large EM + BBO + RSF | 90.81 | A LayoutLMv3-Based Model for Enhanced Relation Extraction in Visually-Rich Documents | - |
| GeoLayoutLM | 89.45 | GeoLayoutLM: Geometric Pre-training for Visual Information Extraction | |
| RORE (GeoLayoutLM) | 88.46 | Modeling Layout Reading Order as Ordering Relations for Visually-rich Document Understanding | |
| LayoutLMv3 large | 80.35 | LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking | |
| LayoutLMv3 large | 80.35 | GeoLayoutLM: Geometric Pre-training for Visual Information Extraction | |
| TPP (LayoutMask) | 79.20 | Reading Order Matters: Information Extraction from Visually-rich Documents by Token Path Prediction | |
| BROS | 77.01 | BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from Documents | |
| LayoutLMv2 large | 70.57 | LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding | |
| LayoutLM | 42.83 | LayoutLM: Pre-training of Text and Layout for Document Image Understanding |
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