Jina Embeddings v2 Base EN
Jina Embeddings v2 Base EN is an English-language text embedding model from Jina AI, built on the JinaBERT architecture which extends BERT with support for longer input sequences through ALiBi positional encoding. A key differentiator is its ability to handle sequences up to 8,192 tokens, enabling direct embedding of long documents without chunking strategies.
Jina AI positions this model for developers who need to embed full documents, lengthy code files, or extended passages in a single pass. It is well suited for enterprise search, document-level similarity, and pipelines where chunking would lose important contextual continuity. The base size keeps inference costs manageable while still delivering solid retrieval performance.
Key Features
Supports input sequences up to 8,192 tokens natively
Built on JinaBERT with ALiBi positional encoding for long contexts
Strong performance on long-document semantic similarity tasks
Enables embedding of full pages or articles without chunking
Open-weight model available for self-hosted deployment
Optimized for English-language retrieval and similarity tasks
Ideal Use Cases
Embedding full research papers or legal briefs without chunking
Long-form document similarity search in content repositories
Code file-level semantic retrieval in developer tooling
RAG pipelines where large context preservation is critical
Academic or enterprise search over extended text corpora
Example Prompts for Jina Embeddings v2 Base EN
Technical Specifications
| Provider | Jina |
| Category | Embedding |
| Modality | Text -> Vector |
| Context Window | 8,192 tokens |
Frequently Asked Questions
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