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Bert Base Uncased Squadv1 X1.96 F88.3 D27 Hybrid Filled Opt V1

Developed by madlag
A question-answering model fine-tuned and optimized on SQuAD v1 based on BERT-base uncased, retaining 43% of original weights through pruning techniques, achieving 1.96x faster inference speed
Downloads 20
Release Time : 3/2/2022

Model Overview

This is a BERT model optimized for question-answering tasks, pruned using the nn_pruning tool to significantly improve inference speed while maintaining high accuracy

Model Features

Efficient Pruning Technology
Uses nn_pruning tool for pruning, retaining 27% of linear layer weights and 43% of original weights overall
Inference Acceleration
Achieves 1.96x faster inference speed than the original version through structured matrix pruning
Accuracy Retention
F1 score only drops by 0.17 compared to the original version (88.33 vs 88.5), maintaining high accuracy while significantly speeding up
Attention Head Optimization
Prunes 55 out of 144 attention heads (38.2%) to optimize computational efficiency

Model Capabilities

Question Answering System
Text Understanding
Context Extraction

Use Cases

Intelligent Q&A
Factual Question Answering
Answers specific questions based on given context
F1 score 88.33, EM score 81.31
Educational Assistance
Learning Material Comprehension
Helps students quickly locate key information in textbooks
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