https://store-images.s-microsoft.com/image/apps.10812.55764ada-d595-43de-8260-9f879c0225d0.0066962b-6aae-4d5a-89df-efa3de900aff.43cc8ac3-b203-48cf-b094-fdba255fe327
Jina ColBERT v1 - en
de Jina AI
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ColBERT multi-embedding model for text input of size up to 8192 tokens.
- 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.