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1
Introduction
2
Agenda
3
Background
4
Mask Language Model
5
Next Sentence Prediction
6
Recap
7
Motivation
8
Cross Layer Parameter Sharing
9
Comparison with BirdBase
10
Eliminate NSP
11
Sentence Order Prediction
12
Factorized Embedding Parameters
13
Embedding Matrix
14
Benchmarks
15
Glue Test
16
Battle Bottom Line
17
Summary
18
Questions
19
Parameter Sharing
20
References
Description:
Explore the innovative ALBERT model for natural language processing in this 31-minute Launchpad video. Dive into the key improvements over BERT, including cross-layer parameter sharing, factorized embedding parameters, and sentence order prediction. Learn about ALBERT's architecture, motivations behind its development, and performance on GLUE benchmarks. Gain insights into how ALBERT achieves state-of-the-art results with fewer parameters, making it more efficient for self-supervised learning of language representations. Understand the technical details, comparisons with BERT, and practical implications for NLP tasks.

ALBERT - A Lite BERT for Self-Supervised Learning of Language Representations

Launchpad
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