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TernaryBERT

2 papers tagged archive 2025-07-28

Introduced by Wei Zhang et al. in TernaryBERT: Distillation-aware Ultra-low Bit BERT

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

TernaryBERT is a Transformer-based model which ternarizes the weights of a pretrained BERT model to {-1,0,+1}, with different granularities for word embedding and weights in the Transformer layer. Instead of directly using knowledge distillation to compress a model, it is used to improve the performance of ternarized student model with the same size as the teacher model. In this way, we transfer the knowledge from the highly-accurate teacher model to the ternarized student model with smaller capacity.

PaperSource

Papers archive 2025-07-28

2 shown of 2, 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

4 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
Quantization2
Binarization1
Knowledge Distillation1
Model Compression1

Usage over time archive 2025-07-28

Papers per year tagged with TernaryBERT: 2020 to 2020, peak 2 2 0 2020: 2 papers 2020
Papers per year the archive tags with this method, by the paper's archive date (2 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

Autoencoding TransformersTransformers

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