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Bort

5 papers tagged archive 2025-07-28

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

Bort is a parametric architectural variant of the BERT architecture. It extracts an optimal subset of architectural parameters for the BERT architecture through a neural architecture search approach; in particular, a fully polynomial-time approximation scheme (FPTAS). This optimal subset - “Bort” - is demonstrably smaller, having an effective size of 5.5 % the original BERT-large architecture, and 16% of the net size. Bort is also able to be pretrained in $288$ GPU hours, which is 1.2% less than the time required to pretrain the highest-performing BERT parametric architecture variant, RoBERTa-large (RoBERTa), and about $33\%

Source: Optimal Subarchitecture Extraction For BERT

Papers archive 2025-07-28

5 shown of 5, 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

7 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
Denoising2
CPU1
GPU1
Lightweight Deployment1
Natural Language Understanding1
Neural Architecture Search1
model1

Usage over time archive 2025-07-28

Papers per year tagged with Bort: 2020 to 2022, peak 3 3 0 2020: 1 paper 2020 2021: 1 paper 2021 2022: 3 papers 2022
Papers per year the archive tags with this method, by the paper's archive date (5 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

Language Models

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