Embedding Recycling for Language Models

Real-world applications of neural language models often involve running many different models over the same corpus. The high computational cost of these runs has led to interest in techniques that can reuse the contextualized embeddings produced in previous runs to speed training and inference of fu...

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Veröffentlicht in:	arXiv.org 2023-01
Hauptverfasser:	Saad-Falcon, Jon, Singh, Amanpreet, Soldaini, Luca, D'Arcy, Mike, Cohan, Arman, Downey, Doug
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Classification Domains Embedding Freezing Inference Recycling Training
Online-Zugang:	Volltext
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