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Scibert SQuAD QuAC

Developed by ixa-ehu
A QA model fine-tuned on SciBERT, trained with SQuAD2.0 and QuAC datasets, suitable for scientific text QA tasks
Downloads 19
Release Time : 3/2/2022

Model Overview

This model is a language representation model fine-tuned for QA tasks based on SciBERT, which is pre-trained on scientific texts. It is particularly suitable for handling QA scenarios in scientific literature.

Model Features

Scientific Text Optimization
Pre-trained on scientific domain corpora for better understanding of scientific terms and expressions
Multi-dataset Fusion
Trained with a combination of SQuAD2.0 and QuAC datasets to enhance QA capabilities
COVID-19 Research Applicability
Particularly suitable for handling QA tasks related to COVID-19 scientific literature

Model Capabilities

Scientific Text Understanding
Question Answering System Construction
Literature Information Extraction

Use Cases

Academic Research
COVID-19 Literature QA
Extracting and answering specific questions from COVID-19 related research papers
Showcased application results at the EMNLP2020 COVID-19 workshop
Scientific Knowledge Base Construction
Automatically extracting structured knowledge from scientific literature
Educational Technology
Intelligent Learning Assistant
Helping students answer questions related to science courses
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