Transformers Reduce Training Time Compared to RNNs

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🤯 Did You Know (click to read)

Transformer models can be trained on hundreds of thousands of sentences per second using modern accelerators due to parallelization.

Unlike RNNs, which process sequences sequentially, Transformers compute self-attention across all tokens simultaneously. This enables efficient GPU and TPU utilization, allowing models to scale to massive datasets and billions of parameters without sequential bottlenecks.

💥 Impact (click to read)

Reduced training time accelerates research, allows larger model experimentation, and supports rapid iteration of NLP applications.

For practitioners, parallelized Transformers provide cost-effective solutions for training large-scale language models and deploying them efficiently.

Source

Vaswani et al., 2017 - Attention is All You Need

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