WebThe training process of DynaBERT includes first training a width-adaptive BERT and then allowing both adaptive width and depth, by distilling knowledge from the full-sized model to small sub-networks. Network rewiring is also used to keep the more important attention heads and neurons shared by more sub-networks. WebJun 16, 2024 · Contributed by Xiaozhi Wang and Zhengyan Zhang. Introduction Pre-trained Languge Model (PLM) has achieved great success in NLP since 2024. In this repo, we list some representative work on PLMs and show their relationship with a diagram. Feel free to distribute or use it!
DynaBERT Explained Papers With Code
WebOct 27, 2024 · Motivated by such considerations, we propose a collaborative optimization for PLMs that integrates static model compression and dynamic inference acceleration. Specifically, the PLM is... WebFeb 18, 2024 · Reducing transformer depth on demand with structured dropout. arXiv preprint arXiv:1909.11556. Compressing bert: Studying the effects of weight pruning on … did nips candy go out of business
BinaryBERT: Pushing the Limit of BERT Quantization
WebDynaBERT: Dynamic BERT with Adaptive Width and Depth 2024 2: TernaryBERT TernaryBERT: Distillation-aware Ultra-low Bit BERT 2024 2: AutoTinyBERT AutoTinyBERT: Automatic Hyper-parameter Optimization for Efficient Pre-trained Language Models 2024 ... WebLanguage Models are models for predicting the next word or character in a document. Below you can find a continuously updating list of language models. Subcategories 1 Transformers Methods Add a Method WebApr 8, 2024 · The training process of DynaBERT includes first training a width-adaptive BERT and then allows both adaptive width and depth, by distilling knowledge from the … did nintendo switch replace wii