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Deberta Base Combined Squad1 Aqa And Newsqa
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Deberta Base Combined Squad1 Aqa And Newsqa

Developed by stevemobs
A Q&A model based on DeBERTa-base architecture, jointly fine-tuned on SQuAD1, AQA, and NewsQA datasets
Question Answering System
Transformers
Open Source License:MIT#Q&A system optimization#Multi-dataset fine-tuning#Reading comprehension enhancement
Downloads 15
Release Time : 5/28/2022

Model Overview

This model is a Q&A system based on the DeBERTa-base architecture, specifically optimized for reading comprehension tasks, capable of extracting answers from given texts.

Model Features

Multi-dataset joint training
The model is jointly fine-tuned on three Q&A datasets: SQuAD1, AQA, and NewsQA, enhancing its generalization capability
Advantages of DeBERTa architecture
Utilizes an improved Transformer architecture with better positional encoding and attention mechanisms
Efficient fine-tuning
Achieves good performance with just 2 training epochs, reducing training loss from 0.6729 to 0.4631

Model Capabilities

Text understanding
Answer extraction
Contextual Q&A

Use Cases

Education
Reading comprehension assistance
Helps students quickly find answers to questions from articles
Information retrieval
Document Q&A system
Extracts answers to specific questions from long documents
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