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TText generation▧Image generation▷Video generation⌘Code generation◖Speech synthesis♫Music generation⬡3D modeling▤Document analysis
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This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

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This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

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> 🎯 **TL;DR**: State-of-the-art paired encoder and decoder models (17M-1B params) trained identically for fair comparison with open data. Encoders beat ModernBERT. Decoders beat Llama 3.2/SmolLM2. This model is part of the Ettin suite - the first collection of paired encoder-only and decoder-only models trained with identical data, architecture, and training recipes. Ettin enables fair comparisons between encoder and decoder architectures across multiple scales, providing state-of-the-art performance for open-data models in their respective size categories. **Architecture-specific advantages persist**: A 400M encoder outperforms a 1B decoder on classification tasks, while a 400M decoder outperforms a 1B encoder on generation tasks.

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> 🎯 **TL;DR**: State-of-the-art paired encoder and decoder models (17M-1B params) trained identically for fair comparison with open data. Encoders beat ModernBERT. Decoders beat Llama 3.2/SmolLM2. This model is part of the Ettin suite - the first collection of paired encoder-only and decoder-only models trained with identical data, architecture, and training recipes. Ettin enables fair comparisons between encoder and decoder architectures across multiple scales, providing state-of-the-art performance for open-data models in their respective size categories. **Architecture-specific advantages persist**: A 400M encoder outperforms a 1B decoder on classification tasks, while a 400M decoder outperforms a 1B encoder on generation tasks.

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`jina-clip-v1` is a state-of-the-art English **multimodal (text-image) embedding model**. Traditional text embedding models, such as jina-embeddings-v2-base-en, excel in text-to-text retrieval but incapable of cross-modal tasks. Models like openai/clip-vit-base-patch32 effectively align image and text embeddings but are not optimized for text-to-text retrieval due to their training methodologies and context limitations. `jina-clip-v1` bridges this gap by offering robust performance in both domains. Its text component matches the retrieval efficiency of `jina-embeddings-v2-base-en`, while its overall architecture sets a new benchmark for cross-modal retrieval. This dual capability makes it an excellent tool for multimodal retrieval-augmented generation (MuRAG) applications, enabling seamless text-to-text and text-to-image searches within a single model.

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This model is based on the paper jina-clip-v2: Multilingual Multimodal Embeddings for Text and Images. `jina-clip-v2` is a **general-purpose multilingual multimodal embedding model for text & images**. Multimodal embeddings enable searching and understanding data across different modalities through a coherent representation. They serve as the backbone of neural information retrieval and multimodal GenAI applications.

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`jina-code-embeddings` is an embedding model for code retrieval. The model supports various types of code retrieval (text-to-code, code-to-code, code-to-text, code-to-completion) and technical question answering across 15+ programming languages. Built on Qwen/Qwen2.5-Coder-0.5B, `jina-code-embeddings-0.5b` features: - **Multilingual support** (15+ programming languages) and compatibility with a wide range of domains, including web development, software development, machine learning, data science, and educational coding problems. - **Task-specific instruction prefixes** for NL2Code, Code2Code, Code2NL, Code2Completion, and Technical QA, which can be selected at inference time. - **Flexible embedding size**: dense embeddings are 896-dimensional by default but can be truncated to as low as 64 with minimal performance loss.

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`jina-code-embeddings` is an embedding model for code retrieval. The model supports various types of code retrieval (text-to-code, code-to-code, code-to-text, code-to-completion) and technical question answering across 15+ programming languages. Please refer to our technical report of jina-code-embeddings for training details and benchmarks. If you find it useful in your research, please cite the following paper:

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