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Xlnet Base Squadv2
XLNet is a pre-trained language model jointly developed by Google and Carnegie Mellon University, fine-tuned on the SQuAD 2.0 question answering dataset
Downloads 21
Release Time : 3/2/2022
Model Overview
This model is a question answering system based on the XLNet architecture, specifically fine-tuned for the SQuAD 2.0 QA task, capable of handling reading comprehension tasks that include both answerable and unanswerable questions
Model Features
Bi-directional Context Understanding
XLNet achieves bi-directional context understanding through permutation language modeling, outperforming traditional uni-directional language models
Handling Unanswerable Questions
Specifically optimized for unanswerable questions in SQuAD 2.0
Relative Position Encoding
Adopts relative position encoding scheme to better handle long-range dependencies
Model Capabilities
Reading Comprehension
Question Answering System
Text Understanding
Answer Extraction
Use Cases
Education
Automated Question Answering System
Used in educational settings for automated question answering systems that respond to students' questions about textbook content
Achieved 75.68% exact match rate on the SQuAD 2.0 test set
Customer Service
FAQ Auto-Response
Automatically answers common customer questions and identifies unanswerable queries
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