Newer model architectures like sentence-T5 and other recent models generally outperform classic BERT-based Sentence Transformers in both performance and efficiency, though the specifics depend on the task and implementation. For example, sentence-T5 leverages the T5 architecture, which is pre-trained on a diverse set of tasks framed as text-to-text problems. This broad training allows it to generate higher-quality embeddings for semantic similarity, clustering, and retrieval tasks. Benchmarks like the Massive Text Embedding Benchmark (MTEB) show that models like sentence-T5 (especially larger variants) achieve higher scores in tasks such as classification and retrieval compared to BERT-based models. However, BERT-based models like Sentence-BERT (SBERT) remain strong in scenarios requiring domain-specific fine-tuning, as their narrower pre-training can sometimes align better with specialized data.
In terms of speed, newer models often strike a better balance between performance and inference time. For instance, sentence-T5-base (110M parameters) is comparable in size to BERT-base (110M) but benefits from T5's efficient transformer architecture, which includes techniques like relative position embeddings and a unified text-to-text framework. This allows sentence-T5 to process sequences faster in practice, especially when leveraging optimized implementations. Meanwhile, distilled versions of newer architectures (e.g., TinyBERT or smaller T5 variants) further reduce latency without significant performance drops. BERT-based models, while heavily optimized over time, still face challenges with long sequences due to their quadratic attention complexity. Newer architectures sometimes address this with sparse attention mechanisms or other optimizations, making them more scalable for real-time applications.
A concrete example is embedding generation for search engines: sentence-T5-xxl might outperform SBERT-large by 5-10% on retrieval accuracy but requires significantly more GPU memory. Conversely, a distilled sentence-T5-small could match SBERT-base’s accuracy while running 2x faster on a CPU. Developers should choose based on their constraints—newer models excel where hardware allows, while BERT remains viable for legacy systems. Hybrid approaches, like using sentence-T5 for offline indexing and SBERT for real-time queries, are also common. Ultimately, the trend favors newer architectures as they continue to refine the trade-offs between accuracy, speed, and resource usage.