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Bert Base Uncased Squadv1 X2.32 F86.6 D15 Hybrid V1

Developed by madlag
A QA model fine-tuned on SQuAD v1 based on BERT-base uncased, with 66% of linear layer weights pruned via nn_pruning library, achieving 2.32x inference speedup
Downloads 16
Release Time : 3/2/2022

Model Overview

This is a pruned and optimized QA model specifically designed for extracting answers from given texts. The model balances speed and accuracy through structured pruning techniques

Model Features

Efficient Inference
Achieves 2.32x acceleration through structured pruning while maintaining 86.6% F1 score
Attention Head Optimization
Removed 43.8% of attention heads (144→81) to optimize computational efficiency
Knowledge Distillation
Distilled from bert-large-uncased model to enhance small model performance

Model Capabilities

Text Understanding
Answer Extraction
Context Analysis

Use Cases

Customer Support
Automated QA System
Automatically answers user questions from knowledge base documents
F1 score 86.64
Educational Technology
Learning Assistant Tool
Helps students quickly find answers to questions from textbooks
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