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Methods › Natural Language Processing › Transformers › ALBERT

ALBERT

172 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

ALBERT is a Transformer architecture based on BERT but with much fewer parameters. It achieves this through two parameter reduction techniques. The first is a factorized embeddings parameterization. By decomposing the large vocabulary embedding matrix into two small matrices, the size of the hidden layers is separated from the size of vocabulary embedding. This makes it easier to grow the hidden size without significantly increasing the parameter size of the vocabulary embeddings. The second technique is cross-layer parameter sharing. This technique prevents the parameter from growing with the depth of the network.

Additionally, ALBERT utilises a self-supervised loss for sentence-order prediction (SOP). SOP primary focuses on inter-sentence coherence and is designed to address the ineffectiveness of the next sentence prediction (NSP) loss proposed in the original BERT.

Source: ALBERT: A Lite BERT for Self-supervised Learning of...

Papers archive 2025-07-28

30 shown of 172, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 167 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Language Modelling29
Language Modeling24
Sentence23
Text Classification15
Sentiment Analysis14
Question Answering13
text-classification13
Named Entity Recognition (NER)10
Transfer Learning10
Named Entity Recognition9
NER8
named-entity-recognition8
Data Augmentation7
Natural Language Inference7
Reading Comprehension7
General Classification6
Machine Reading Comprehension6
Natural Language Understanding6
Word Embeddings6
Classification5

Usage over time archive 2025-07-28

Papers per year tagged with ALBERT: 2019 to 2025, peak 63 63 0 2019: 2 papers 2019 2020: 46 papers 2020 2021: 63 papers 2021 2022: 26 papers 2022 2023: 13 papers 2023 2024: 15 papers 2024 2025: 7 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (172 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Transformers

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