Jinaai

Models by this creator

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jina-embeddings-v2-base-en

jinaai

Total Score

625

The jina-embeddings-v2-base-en model is a text embedding model created by Jina AI. It is based on a BERT architecture called JinaBERT that supports longer sequence length up to 8192 tokens using the symmetric bidirectional variant of ALiBi. The model was further trained on over 400 million sentence pairs and hard negatives from various domains. This makes it useful for a range of use cases like long document retrieval, semantic textual similarity, text reranking, and more. Compared to the smaller jina-embeddings-v2-small-en model, this base version has 137 million parameters, allowing for fast inference while delivering better performance. Model inputs and outputs Inputs Text sequences up to 8192 tokens long Outputs 4096-dimensional text embeddings Capabilities The jina-embeddings-v2-base-en model can generate high-quality embeddings for long text sequences, enabling applications like semantic search, text similarity, and document understanding. Its ability to handle 8192 token sequences makes it particularly useful for working with long-form content like research papers, legal contracts, or product descriptions. What can I use it for? The embeddings produced by this model can be used in a variety of downstream natural language processing tasks. Some potential use cases include: Long document retrieval: Finding relevant documents from a large corpus based on semantic similarity to a query. Semantic textual similarity: Measuring the semantic similarity between text pairs, which can be useful for applications like plagiarism detection or textual entailment. Text reranking: Reordering a list of documents or passages based on their relevance to a given query. Recommendation systems: Suggesting relevant content to users based on the semantic similarity of items. RAG and LLM-based generative search: Enabling more powerful and flexible search experiences powered by large language models. Things to try One interesting aspect of the jina-embeddings-v2-base-en model is its ability to handle very long text sequences, up to 8192 tokens. This makes it well-suited for working with long-form content like research papers, legal contracts, or product descriptions. You could try using the model to perform semantic search or text similarity analysis on a corpus of long-form documents, and see how the performance compares to models with shorter sequence lengths. Another interesting area to explore would be the model's use in recommendation systems or generative search applications. The high-quality embeddings produced by the model could be leveraged to suggest relevant content to users or to enable more flexible and powerful search experiences powered by large language models.

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Updated 5/27/2024

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jina-clip-v1

jinaai

Total Score

124

jina-clip-v1 is a state-of-the-art English multimodal (text-image) embedding model trained by Jina AI. It bridges the gap between traditional text embedding models, such as jina-embeddings-v2-base-en, which excel in text-to-text retrieval but are incapable of cross-modal tasks, and models like openai/clip-vit-base-patch32 that effectively align image and text embeddings but are not optimized for text-to-text retrieval. jina-clip-v1 offers robust performance in both domains, matching the retrieval efficiency of jina-embeddings-v2-base-en for text-to-text tasks while setting a new benchmark for cross-modal retrieval. Model inputs and outputs Inputs Sentences**: The model can encode meaningful sentences in English. Images**: The model can also encode images, either by providing the public image URLs or directly passing in the PIL.Image objects. Outputs Text embeddings**: The model outputs dense vector representations for the input sentences. Image embeddings**: The model outputs dense vector representations for the input images. Similarity scores**: The model can compute the cosine similarity between text and image embeddings, enabling cross-modal retrieval. Capabilities jina-clip-v1 excels at both text-to-text and text-to-image retrieval tasks. Its dual capability makes it an excellent tool for multimodal retrieval-augmented generation (MuRAG) applications, allowing seamless text-to-text and text-to-image searches within a single model. What can I use it for? jina-clip-v1 can be used for a variety of multimodal applications, such as: Image search**: Users can search for images by describing them in text. Cross-modal retrieval**: The model can retrieve relevant text or images based on a query in the opposite modality. Multimodal question answering**: The model can be used to answer questions that require understanding both text and images. Multimodal content generation**: The model can be used to generate relevant text or images based on a prompt in the opposite modality. Jina AI has also provided the Embeddings API as an easy-to-use interface for working with jina-clip-v1 and their other embedding models. Things to try One key advantage of jina-clip-v1 is its ability to handle longer sequences of text, up to 8,192 tokens, thanks to its use of the symmetric bidirectional variant of ALiBi. This makes the model well-suited for tasks involving long-form content, such as document retrieval, long-form question answering, and summarization. Researchers and developers can explore how the model's performance scales with longer input sequences compared to traditional text embedding models.

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Updated 6/11/2024

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jina-embeddings-v2-base-zh

jinaai

Total Score

121

The jina-embeddings-v2-base-zh model is a Chinese/English bilingual text embedding model developed by Jina AI. It is based on a BERT architecture (JinaBERT) that supports the symmetric bidirectional variant of ALiBi to allow longer sequence lengths of up to 8192 tokens. Compared to other Jina embedding models, jina-embeddings-v2-base-zh is a 161 million parameter model trained specifically on mixed Chinese-English input to provide high performance in both mono-lingual and cross-lingual applications. Similar Jina AI embedding models include jina-embeddings-v2-small-en, jina-embeddings-v2-base-en, jina-embeddings-v2-base-de, and an upcoming jina-embeddings-v2-base-es model for Spanish-English bilingual embeddings. Model Inputs and Outputs Inputs Text sequence**: The model takes in text sequences of up to 8192 tokens, supporting both Chinese and English, as well as a mix of the two. Outputs Text embeddings**: The model outputs 768-dimensional embedding vectors that capture the semantic meaning of the input text. These can be used for a variety of downstream tasks like information retrieval, text similarity, and multilingual applications. Capabilities The jina-embeddings-v2-base-zh model has been designed to excel at both mono-lingual and cross-lingual tasks involving Chinese and English text. Its long sequence length support of up to 8192 tokens makes it useful for applications that need to process long-form content, such as document retrieval, semantic textual similarity, and text reranking. What Can I Use It For? The jina-embeddings-v2-base-zh model can be used for a wide range of natural language processing tasks that require high-quality text embeddings, especially those involving a mix of Chinese and English text. Some potential use cases include: Information Retrieval**: Use the embeddings for semantic search and retrieval of Chinese or English documents, or documents containing a mix of both languages. Text Similarity**: Compute the similarity between Chinese, English, or bilingual text passages to detect paraphrases, identify related content, or perform clustering. Multilingual Applications**: Leverage the model's cross-lingual capabilities to build applications that seamlessly handle Chinese and English input, such as chatbots or question-answering systems. Things to Try An interesting aspect of the jina-embeddings-v2-base-zh model is its ability to handle long input sequences of up to 8192 tokens. This makes it well-suited for tasks involving lengthy documents or multi-paragraph inputs. You could experiment with using the model for tasks like: Long-form text summarization, where the model's ability to capture semantic meaning in long passages could improve the quality of generated summaries. Cross-lingual document retrieval, where the model's bilingual capabilities and long sequence support could help surface relevant content even when the query and target documents are in different languages. Multilingual dialog systems, where the model's embeddings could be used to maintain context and coherence across language switches within a conversation. By exploring the model's unique features, you can uncover novel applications that leverage its strengths in handling long, multilingual text inputs.

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Updated 5/28/2024

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jina-embeddings-v2-small-en

jinaai

Total Score

110

jina-embeddings-v2-small-en is an English text embedding model trained by Jina AI. It is based on a BERT architecture called JinaBERT that supports longer sequence lengths of up to 8192 tokens using the ALiBi technique. The model was further trained on over 400 million sentence pairs and hard negatives from various domains. Compared to the larger jina-embeddings-v2-base-en model, this smaller 33 million parameter version enables fast and efficient inference while still delivering impressive performance. Model inputs and outputs Inputs Text sequences**: The model can handle text inputs up to 8192 tokens in length. Outputs Sentence embeddings**: The model outputs 768-dimensional dense vector representations that capture the semantic meaning of the input text. Capabilities jina-embeddings-v2-small-en is a highly capable text encoding model that can be used for a variety of natural language processing tasks. Its ability to handle long input sequences makes it particularly useful for applications like long document retrieval, semantic textual similarity, text reranking, recommendation, and generative search. What can I use it for? The jina-embeddings-v2-small-en model can be used for a wide range of applications, including: Information Retrieval**: Encoding long documents or queries into semantic vectors for efficient similarity-based search and ranking. Recommendation Systems**: Generating embeddings of items (e.g. articles, products) or user queries to enable content-based recommendation. Text Classification**: Using the sentence embeddings as input features for downstream classification tasks. Semantic Similarity**: Computing the semantic similarity between text pairs, such as for paraphrase detection or question answering. Natural Language Generation**: Incorporating the model into RAG (Retrieval-Augmented Generation) or other LLM-based systems to improve the coherence and relevance of generated text. Things to try A key advantage of the jina-embeddings-v2-small-en model is its ability to handle long input sequences. This makes it well-suited for tasks involving lengthy documents, such as legal contracts, research papers, or product manuals. You could explore using this model to build intelligent search or recommendation systems that can effectively process and understand these types of complex, information-rich text inputs. Additionally, the model's strong performance on semantic similarity tasks suggests it could be useful for building chatbots or dialogue systems that need to understand the meaning behind user queries and provide relevant, context-aware responses.

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Updated 5/28/2024

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jina-colbert-v1-en

jinaai

Total Score

76

Jina-ColBERT Jina-ColBERT is a variant of the ColBERT retrieval model that is based on the JinaBERT architecture. Like the original ColBERT, Jina-ColBERT uses a late interaction approach to achieve fast and accurate retrieval. The key difference is that Jina-ColBERT supports a longer context length of up to 8,192 tokens, enabled by the JinaBERT backbone which incorporates the symmetric bidirectional variant of ALiBi. Model inputs and outputs Inputs Text passages to be indexed and searched Outputs Ranked lists of the most relevant passages for a given query Capabilities Jina-ColBERT is designed for efficient and effective passage retrieval, outperforming standard BERT-based models. Its ability to handle long documents up to 8,192 tokens makes it well-suited for tasks involving large amounts of text, such as document search and question-answering over long-form content. What can I use it for? Jina-ColBERT can be used to power a wide range of search and retrieval applications, including enterprise search, academic literature search, and question-answering systems. Its performance characteristics make it particularly useful in scenarios where users need to search large document collections quickly and accurately. Things to try One interesting aspect of Jina-ColBERT is its ability to leverage the JinaBERT architecture to support longer input sequences. Practitioners could experiment with using Jina-ColBERT to search through long-form content like books, legal documents, or research papers, and compare its performance to other retrieval models.

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Updated 5/28/2024

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jina-embeddings-v2-base-de

jinaai

Total Score

53

The jina-embeddings-v2-base-de is a German/English bilingual text embedding model developed by Jina AI. It supports input sequences up to 8192 tokens and is based on a BERT architecture (JinaBERT) that uses the symmetric bidirectional variant of ALiBi to handle longer sequences. Jina AI has also released several other embedding models, including jina-embeddings-v2-small-en, jina-embeddings-v2-base-en, jina-embeddings-v2-base-zh, and jina-embeddings-v2-base-code. Model inputs and outputs Inputs Text sequences up to 8192 tokens in length, supporting mixed German-English input. Outputs A 768-dimensional embedding vector representing the semantic meaning of the input text. Capabilities The jina-embeddings-v2-base-de model is designed for high performance in both monolingual and cross-lingual applications. It has been trained to handle mixed German-English input without bias, making it useful for applications involving multiple languages. What can I use it for? The jina-embeddings-v2-base-de model can be used for a variety of NLP tasks, such as: Long document retrieval Semantic textual similarity Text re-ranking Recommendation systems RAG (Retrieval-Augmented Generation) and LLM-based generative search According to a recent blog post from LLamaIndex, the combination of Jina AI's base embeddings with the CohereRerank/bge-reranker-large model stands out for achieving peak performance in both hit rate and MRR for RAG applications. Things to try When using the jina-embeddings-v2-base-de model, it's important to apply mean pooling to the token embeddings to produce high-quality sentence-level embeddings. Jina AI provides an encode function to handle this automatically, but you can also implement mean pooling manually if needed.

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Updated 6/1/2024