Methods › Natural Language Processing › Autoencoding Transformers › AutoTinyBERT
AutoTinyBERT
Introduced by Yichun Yin et al. in AutoTinyBERT: Automatic Hyper-parameter Optimization for Efficient Pre-trained Language Models
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
AutoTinyBERT is a an efficient BERT variant found through neural architecture search. Specifically, one-shot learning is used to obtain a big Super Pretrained Language Model (SuperPLM), where the objectives of pre-training or task-agnostic BERT distillation are used. Then, given a specific latency constraint, an evolutionary algorithm is run on the SuperPLM to search optimal architectures. Finally, we extract the corresponding sub-models based on the optimal architectures and further train these models.
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.
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SpeedLimit: Neural Architecture Search for Quantized Transformer Models 25 Sep 2022 · 0 repositories · arXiv:2209.12127
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AutoTinyBERT: Automatic Hyper-parameter Optimization for Efficient Pre-trained Language Models 29 Jul 2021 · 1 repository · arXiv:2107.13686
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.
| Task | Papers |
|---|---|
| Neural Architecture Search | 2 |
| One-Shot Learning | 1 |
| Quantization | 1 |
| Vocal Bursts Valence Prediction | 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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