bert-base-uncased-finetuned-docvqa Open-source Model - Effectively Solve the Difficulties of Document Visual Question Answering

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Bert Base Uncased Finetuned Docvqa

Developed by tiennvcs

BERT-based model fine-tuned on Document Visual Question Answering (DocVQA) tasks

Question Answering System

Transformers

Open Source License:Apache-2.0 #Document QA #BERT Fine-tuning #Text Understanding

Downloads 60

Release Time : 3/2/2022

Model Overview

This model is a fine-tuned version of bert-base-uncased for document visual question answering tasks, suitable for handling Q&A tasks in document images.

Model Features

Document Visual QA Capability

Optimized specifically for Q&A tasks in document images

BERT-based Architecture

Utilizes BERT's powerful language understanding capabilities to process document content

Fine-tuning Optimization

Fine-tuned on specific datasets to enhance performance on target tasks

Model Capabilities

Document Understanding

Visual Question Answering

Text Understanding

Use Cases

Document Processing

Contract Analysis

Automatically answer questions about contract document content

Table Data Extraction

Extract and answer related questions from document tables

Education

Automatic Test Grading

Answer test-related questions based on document images

Training Loss	Epoch	Step	Validation Loss
2.2151	0.1	1000	2.6299
1.8885	0.21	2000	2.2217
1.7353	0.31	3000	2.1675
1.6188	0.41	4000	2.2436
1.5802	0.52	5000	2.0539
1.4875	0.62	6000	2.0551
1.4675	0.73	7000	1.9368
1.3485	0.83	8000	1.9456
1.3273	0.93	9000	1.9281
1.1048	1.04	10000	1.9333
0.9529	1.14	11000	2.2019
0.9418	1.24	12000	2.0381
0.9209	1.35	13000	1.8753
0.8788	1.45	14000	1.9964
0.8729	1.56	15000	1.9690
0.8671	1.66	16000	1.8513
0.8379	1.76	17000	1.9627
0.8722	1.87	18000	1.8988
0.7842	1.97	19000	1.9146

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Bert Base Uncased Finetuned Docvqa

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 bert-base-uncased-finetuned-docvqa

🚀 Quick Start

📚 Documentation

Model description

Intended uses & limitations

Training and evaluation data

🔧 Technical Details

Training procedure

Training hyperparameters

Training results

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