ALBERT

E435869

ALBERT is a lightweight, parameter-efficient variant of the BERT language model designed to achieve strong natural language understanding performance with reduced memory and computation costs.

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ALBERT canonical 3

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Statements (48)

Predicate Object
instanceOf BERT variant ⓘ
language model ⓘ
neural network model ⓘ
transformer-based model ⓘ
achieves state-of-the-art results on several benchmarks at release ⓘ
acronymFor A Lite BERT ⓘ
linked to: DistilBERT
aimsTo maintain strong performance ⓘ
reduce computation cost ⓘ
reduce memory usage ⓘ
basedOn BERT ⓘ
compatibleWith Hugging Face Transformers ⓘ
describedInPaper ALBERT: A Lite BERT for Self-supervised Learning of Language Representations ⓘ
designedFor natural language understanding ⓘ
evaluatedOn GLUE benchmark ⓘ
RACE dataset ⓘ
SQuAD ⓘ
linked to: SQuAD 2.0
fullName A Lite BERT ⓘ
linked to: DistilBERT
hasArchitecture Transformer ⓘ
hasFeature cross-layer parameter sharing ⓘ
smaller embedding size with projection ⓘ
hasObjective sentence-order prediction ⓘ
hasOpenSourceImplementation Yes ⓘ
hasProperty computationally-efficient ⓘ
lightweight ⓘ
memory-efficient ⓘ
parameter-efficient ⓘ
hasVariant ALBERT-base ⓘ
ALBERT-large ⓘ
ALBERT-xlarge ⓘ
ALBERT-xxlarge ⓘ
linked to: ALBERT-xlarge
implementedIn TensorFlow ⓘ
introducedBy Google Research ⓘ
Toyota Technological Institute at Chicago ⓘ
introducedIn 2019 ⓘ
language English (pretrained models) ⓘ
paperAuthorsInclude Kevin Gimpel ⓘ
Mingda Chen ⓘ
Piyush Sharma ⓘ
Radu Soricut ⓘ
linked to: Mihai Surdeanu

Sebastian Goodman ⓘ
Zhenzhong Lan ⓘ
replacesObjective next sentence prediction ⓘ
supports sentence-level tasks ⓘ
token-level tasks ⓘ
trainedWith masked language modeling ⓘ
uses self-attention ⓘ
usesTechnique factorized embedding parameterization ⓘ
parameter sharing across layers ⓘ

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Referenced by (3)

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