Methods › Natural Language Processing › Autoencoding Transformers › SqueezeBERT
SqueezeBERT
Introduced by Forrest N. Iandola et al. in SqueezeBERT: What can computer vision teach NLP about efficient neural networks?
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
SqueezeBERT is an efficient architectural variant of BERT for natural language processing that uses grouped convolutions. It is much like BERT-base, but with positional feedforward connection layers implemented as convolutions, and grouped convolution for many of the layers.
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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SqueezeBERT: What can computer vision teach NLP about efficient neural networks? 19 Jun 2020 · 6 repositories · arXiv:2006.11316Syntology ran 0 of 1 samples · 1 unverified
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.
| Task | Papers |
|---|---|
| Linguistic Acceptability | 1 |
| Natural Language Inference | 1 |
| Question Answering | 1 |
| Semantic Textual Similarity | 1 |
| Sentiment Analysis | 1 |
| Text Classification | 1 |
| Transfer Learning | 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
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections