Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents

Understand

Text embedding models have emerged as powerful tools for transforming sentences into fixed-sized feature vectors that encapsulate semantic information.

• While these models are essential for tasks like information retrieval, semantic clustering, and text re-ranking, most existing open-source models, especially those built on architectures like BERT, struggle to represent lengthy documents and often resort to truncation.
• One common approach to mitigate this challenge involves splitting documents into smaller paragraphs for embedding.
• However, this strategy results in a much larger set of vectors, consequently leading to increased memory consumption and computationally intensive vector searches with elevated latency.