jinaai
/

jina-embeddings-v4

Visual Document Retrieval

sentence-transformers

feature-extraction

multimodal-embedding

multilingual-embedding

Text-to-Visual Document (T→VD) retrieval

sentence-similarity

🇪🇺 Region: EU

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bwang0911 commited on Jun 24

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@@ -41,7 +41,7 @@ Embeddings produced by `jina-embeddings-v4` serve as the backbone for neural inf
 Built based on [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct), `jina-embeddings-v4` has the following features:
 - **Unified embeddings** for text, images, and visual documents, supporting both dense (single-vector) and late-interaction (multi-vector) retrieval.
-- **Multilingual support** (20+ languages) and compatibility with a wide range of domains, including technical and visually complex documents.
 - **Task-specific adapters** for retrieval, text matching, and code-related tasks, which can be selected at inference time.
 - **Flexible embedding size**: dense embeddings are 2048 dimensions by default but can be truncated to as low as 128 with minimal performance loss.

 Built based on [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct), `jina-embeddings-v4` has the following features:
 - **Unified embeddings** for text, images, and visual documents, supporting both dense (single-vector) and late-interaction (multi-vector) retrieval.
+- **Multilingual support** (30+ languages) and compatibility with a wide range of domains, including technical and visually complex documents.
 - **Task-specific adapters** for retrieval, text matching, and code-related tasks, which can be selected at inference time.
 - **Flexible embedding size**: dense embeddings are 2048 dimensions by default but can be truncated to as low as 128 with minimal performance loss.