jina-colbert-v1-en is an open-source English ColBERT-style embedding model supporting 8192 sequence length.
ColBERT (Contextualized Late Interaction over BERT) leverages the deep language understanding of BERT while introducing a novel interaction mechanism. This mechanism, known as late interaction, allows for efficient and precise retrieval by processing queries and documents separately until the final stages of the retrieval process.
This state-of-the-art AI embedding model enables many applications, such as document clustering, classification, content personalization, vector search, or retrieval augmented generation.

Highlights:

Jina-colbert-v1-en's main advancement is its backbone, jina-bert-v2-base-en, which enables processing of significantly longer contexts (up to 8192 tokens) compared to the original ColBERT that uses bert-base-uncased. This capability is crucial for handling documents with extensive content, providing more detailed and contextual search results.
jina-colbert-v1-en's has a superior performance, especially in scenarios requiring longer context lengths vs the original ColBERTv2. Note that jina-embeddings-v2-base-en uses more training data, whereas jina-colbert-v1-en only uses MSMARCO, which may justify the good performance of jina-embeddings-v2-base-en on some tasks.
Use-cases: Fine-grained vector search, retrieval augmented generation, long document clustering, sentiment analysis.

Jina ColBERT v1 - enJina AI

Jina ColBERT v1 - en