Binary Embeddings: All the AI, 3.125% of the Fat
Embeddings have become the cornerstone of a variety of AI and natural language processing applications, offering a way to represent the meanings of texts as high-dimensional vectors. However, between the increasing size of models and the growing quantities of data AI models process, the computational and storage demands for traditional embeddings have escalated. Binary embeddings have been introduced as a compact, efficient alternative that maintains high performance while drastically reducing resource requirements.
Binary embeddings are one way to mitigate these resource requirements by reducing the size of embedding vectors by as much as 96% (96.875% in the case of Jina Embeddings). Users can leverage the power of compact binary embeddings within their AI applications with minimal loss of accuracy.
Binary embeddings are a specialized form of data representation where traditional high-dimensional floating-point vectors are transformed into binary vectors. This not only compresses the embeddings but also retains nearly all of the vectors' integrity and utility. The essence of this technique lies in its ability to maintain the semantics and relational distances between the data points even after conversion. The magic behind binary embeddings is quantization, a method that turns high-precision numbers into lower-precision ones. In AI modeling, this often means converting the 32-bit floating-point numbers in embeddings into representations with fewer bits, like 8-bit integers.
