The easiest way to starting using `jina-embeddings-v2-base-code` is to use Jina AI's Embedding API. `jina-embeddings-v2-base-code` is an multilingual **embedding model** speaks **English and 30 widely used programming languages**. Same as other jina-embeddings-v2 series, it supports **8192** sequence length. `jina-embeddings-v2-base-code` is based on a Bert architecture (JinaBert) that supports the symmetric bidirectional variant of ALiBi to allow longer sequence length. The backbone `jina-bert-v2-base-code` is pretrained on the github-code dataset. The model is further trained on Jina AI's collection of more than 150 millions of coding question answer and docstring source code pairs. These pairs were obtained from various domains and were carefully selected through a thorough cleaning process.
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The easiest way to starting using `jina-embeddings-v2-base-de` is to use Jina AI's Embedding API. `jina-embeddings-v2-base-de` is a German/English bilingual text **embedding model** supporting **8192 sequence length**. It is based on a BERT architecture (JinaBERT) that supports the symmetric bidirectional variant of ALiBi to allow longer sequence length. We have designed it for high performance in mono-lingual & cross-lingual applications and trained it specifically to support mixed German-English input without bias. Additionally, we provide the following embedding models: `jina-embeddings-v2-base-de` ist ein zweisprachiges **Text Embedding Modell** für Deutsch und Englisch, welches Texteingaben mit einer Länge von bis zu **8192 Token unterstützt**. Es basiert auf der adaptierten Bert-Modell-Architektur JinaBERT, welche mithilfe einer symmetrische Variante von ALiBi längere Eingabetexte erlaubt. Wir haben, das Model für hohe Performance in einsprachigen und cross-lingual Anwendungen entwickelt und speziell darauf trainiert, gemischte deutsch-englische Eingaben ohne einen Bias zu kodieren. Des Weiteren stellen wir folgende Embedding-Modelle bereit:
The easiest way to starting using `jina-embeddings-v2-base-en` is to use Jina AI's Embedding API. `jina-embeddings-v2-base-en` is an English, monolingual **embedding model** supporting **8192 sequence length**. It is based on a BERT architecture (JinaBERT) that supports the symmetric bidirectional variant of ALiBi to allow longer sequence length. The backbone `jina-bert-v2-base-en` is pretrained on the C4 dataset. The model is further trained on Jina AI's collection of more than 400 millions of sentence pairs and hard negatives. These pairs were obtained from various domains and were carefully selected through a thorough cleaning process. The embedding model was trained using 512 sequence length, but extrapolates to 8k sequence length (or even longer) thanks to ALiBi. This makes our model useful for a range of use cases, especially when processing long documents is needed, including long document retrieval, semantic textual similarity, text reranking, recommendation, RAG and LLM-based generative search, etc.
`jina-embeddings-v2-base-es` is a Spanish/English bilingual text **embedding model** supporting **8192 sequence length**. It is based on a BERT architecture (JinaBERT) that supports the symmetric bidirectional variant of ALiBi to allow longer sequence length. We have designed it for high performance in mono-lingual & cross-lingual applications and trained it specifically to support mixed Spanish-English input without bias. Additionally, we provide the following embedding models: `jina-embeddings-v2-base-es` es un modelo (embedding) de texto bilingüe Inglés/Español que admite una longitud de secuencia de 8192. Se basa en la arquitectura BERT (JinaBERT) que incorpora la variante bi-direccional simétrica de ALiBi para permitir una mayor longitud de secuencia. Hemos diseñado este modelo para un alto rendimiento en aplicaciones monolingües y bilingües, y está entrenando específicamente para admitir entradas mixtas de español e inglés sin sesgo. Adicionalmente, proporcionamos los siguientes modelos (embeddings): `mean pooling` takes all token embeddings from model output and averaging them at sentence/paragraph level. It has been proved to be the most effective way to produce high-quality sentence embeddings. We offer an `encode` function to deal with this.
`jina-embeddings-v2-base-zh` is a Chinese/English bilingual text **embedding model** supporting **8192 sequence length**. It is based on a BERT architecture (JinaBERT) that supports the symmetric bidirectional variant of ALiBi to allow longer sequence length. We have designed it for high performance in mono-lingual & cross-lingual applications and trained it specifically to support mixed Chinese-English input without bias. Additionally, we provide the following embedding models: `jina-embeddings-v2-base-zh` 是支持中英双语的**文本向量**模型，它支持长达**8192字符**的文本编码。 该模型的研发基于BERT架构(JinaBERT)，JinaBERT是在BERT架构基础上的改进，首次将ALiBi应用到编码器架构中以支持更长的序列。 不同于以往的单语言/多语言向量模型，我们设计双语模型来更好的支持单语言（中搜中）以及跨语言（中搜英）文档检索。 除此之外，我们也提供其它向量模型: `mean poooling` takes all token embeddings from model output and averaging them at sentence/paragraph level. It has been proved to be the most effective way to produce high-quality sentence embeddings. We offer an `encode` function to deal with this.
jina-embeddings-v3-hf
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*Average score vs. parameter count across image (MIEB-Lite), video (MMEB-V), and audio (MAEB) benchmarks — `jina-v5-omni-nano` and `jina-v5-omni-small` define the open-weight frontier (Table 1 in the ArXiv report).* `jina-embeddings-v5-omni-nano` is a multimodal embedding model that accepts **text, images, video, and audio** and produces embeddings in a shared vector space aligned with the text-only `jinaai/jina-embeddings-v5-text-nano` — so you can index with text and query with any modality, no reindexing. For higher performance at a larger size, see `jinaai/jina-embeddings-v5-omni-small`. This is the **base** repository — it holds all task adapters (retrieval, classification, clustering, text-matching). For a single task, pre-merged task-specific variants are also available: - `jinaai/jina-embeddings-v5-omni-nano-retrieval` — query–document semantic search and RAG (raw-transformers users prepend `Query: ` / `Document: ` to text; sentence-transformers users call `encode_query()` / `encode_document()`). - `jinaai/jina-embeddings-v5-omni-nano-classification` — assigning labels via embedding similarity — zero-shot and few-shot classification across modalities. - `jinaai/jina-embeddings-v5-omni-nano-clustering` — grouping semantically similar items — topic discovery, deduplication, exploratory analysis. - `jinaai/jina-embeddings-v5-omni-nano-text-matching` — symmetric pairwise similarity scoring — STS, paraphrase and near-duplicate detection.
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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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*Average score vs. parameter count across image (MIEB-Lite), video (MMEB-V), and audio (MAEB) benchmarks — `jina-v5-omni-nano` and `jina-v5-omni-small` define the open-weight frontier (Table 1 in the ArXiv report).* `jina-embeddings-v5-omni-nano-retrieval` is a multimodal embedding model that accepts **text, images, video, and audio** and produces embeddings in a shared vector space aligned with the text-only `jinaai/jina-embeddings-v5-text-nano-retrieval` — so you can index with text and query with any modality, no reindexing. For higher performance at a larger size, see `jinaai/jina-embeddings-v5-omni-small-retrieval`. This is the **retrieval-targeted** variant of the `jina-embeddings-v5-omni-nano` family. The combined repo containing all task adapters is `jinaai/jina-embeddings-v5-omni-nano`.
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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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