Methods › Natural Language Processing › Autoencoding Transformers › BinaryBERT
BinaryBERT
Introduced by Haoli Bai et al. in BinaryBERT: Pushing the Limit of BERT Quantization
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
BinaryBERT is a BERT-variant that applies quantization in the form of weight binarization. Specifically, ternary weight splitting is proposed which initializes BinaryBERT by equivalently splitting from a half-sized ternary network. To obtain BinaryBERT, we first train a half-sized ternary BERT model, and then apply a ternary weight splitting operator to obtain the latent full-precision and quantized weights as the initialization of the full-sized BinaryBERT. We then fine-tune BinaryBERT for further refinement.
Papers archive 2025-07-28
1 shown of 1, 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.
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BinaryBERT: Pushing the Limit of BERT Quantization 31 Dec 2020 · 1 repository · arXiv:2012.15701Syntology ran 4 of 6 samples · 2 unverified · 6 pointer-only (licence)
Tasks archive 2025-07-28
3 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Binarization | 1 |
| Model Compression | 1 |
| Quantization | 1 |
Usage over time archive 2025-07-28
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
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