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Layoutlmv2 Base Uncased Finetuned Docvqa
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Layoutlmv2 Base Uncased Finetuned Docvqa

Developed by tiennvcs
A document visual question answering model based on the LayoutLMv2 architecture, fine-tuned for document understanding tasks
Text-to-Image
Transformers
#Document QA#Multimodal Understanding#Form Parsing
Downloads 983
Release Time : 3/2/2022

Model Overview

This model is a pre-trained model based on the LayoutLMv2 architecture, specifically fine-tuned for Document Visual Question Answering (DocVQA) tasks. It can understand both textual content and layout information in documents to answer content-related questions.

Model Features

Document Layout Understanding
Capable of processing both textual content and document layout information simultaneously
Visual Question Answering Capability
Can answer questions based on document image content
Fine-tuning Optimization
Specifically fine-tuned for DocVQA tasks

Model Capabilities

Document content understanding
Visual question answering
Document layout analysis

Use Cases

Document Processing
Form Information Extraction
Extract specific information from scanned forms
Contract Analysis
Answer specific questions about contract terms
Education
Test Paper Grading
Automatically grade scanned student test papers
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