EELBERT: Tiny Models through Dynamic Embeddings

EELBERT is an approach for compressing transformer-based models like BERT while preserving accuracy in downstream tasks. It replaces the input embedding layer with dynamic embedding computations, reducing model size. Evaluations on the GLUE benchmark demonstrate the effectiveness of this approach.

 EELBERT: Tiny Models through Dynamic Embeddings

EELBERT: Tiny Models through Dynamic Embeddings

Introducing EELBERT, an innovative approach for compressing transformer-based models like BERT while minimizing the impact on downstream task accuracy. By replacing the input embedding layer with dynamic on-the-fly embedding computations, we significantly reduce the model size. Empirical evaluation on the GLUE benchmark validates the effectiveness of our BERT variants.

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For AI KPI management advice, connect with us at hello@itinai.com. Stay updated on leveraging AI by following our Telegram channel t.me/itinainews or Twitter @itinaicom.

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