To reduce embedding size without losing critical information, developers can use dimensionality reduction, quantization, and model distillation. These methods compress embeddings while preserving their utility for tasks like search or classification. The key is to balance compression with retaining enough structure to maintain performance in downstream applications.
One effective approach is dimensionality reduction. Techniques like PCA (Principal Component Analysis) project high-dimensional embeddings into a lower-dimensional space by identifying the most informative directions in the data. For example, reducing 768-dimensional BERT embeddings to 128 dimensions using PCA often retains most of the variance. Autoencoders—neural networks trained to reconstruct inputs through a bottleneck layer—offer another option. A simple autoencoder with a 768→256→128→256→768 architecture can learn compact representations. These methods work best when the reduced dimensions capture the essential relationships in the original embeddings.
Quantization reduces storage size by lowering numerical precision. For instance, converting 32-bit floating-point embeddings to 16-bit or 8-bit integers can shrink their size by 50–75% with minimal accuracy loss. Libraries like PyTorch support this via methods like tensor.half(). For extreme compression, product quantization divides embeddings into subvectors and replaces each with a code from a pre-trained dictionary. Facebook’s FAISS library uses this to enable efficient similarity search on billion-scale datasets. While quantization introduces some approximation, it’s often negligible in practice if calibrated properly.
Model distillation trains smaller models to replicate the embeddings of larger ones. For example, a compact BERT variant like TinyBERT can mimic the embedding behavior of full-sized BERT with 4x fewer parameters. This leverages the larger model’s knowledge while producing smaller outputs. When choosing a method, evaluate trade-offs: PCA is fast but linear, autoencoders handle non-linear patterns but require training, and distillation depends on task-specific data. Always validate by testing compressed embeddings on real tasks (e.g., classification accuracy) rather than relying solely on theoretical metrics like reconstruction error.