mmarco-mMiniLMv2-L12-H384-v1
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View details ↗DISCOVER & COMPARE
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bert-base-uncased-mnli
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View details ↗roberta-base-cola
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View details ↗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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View details ↗The easiest way to starting using `jina-embeddings-v2-small-en` is to use Jina AI's Embedding API. `jina-embeddings-v2-small-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-small-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.
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View details ↗jina-embeddings-v3
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View details ↗*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-small` 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-small` — so you can index with text and query with any modality, no reindexing. For a more compact alternative, see `jinaai/jina-embeddings-v5-omni-nano`. 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-small-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-small-classification` — assigning labels via embedding similarity — zero-shot and few-shot classification across modalities. - `jinaai/jina-embeddings-v5-omni-small-clustering` — grouping semantically similar items — topic discovery, deduplication, exploratory analysis. - `jinaai/jina-embeddings-v5-omni-small-text-matching` — symmetric pairwise similarity scoring — STS, paraphrase and near-duplicate detection.
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View details ↗`jina-embeddings-v5-text-nano-classification` is a compact, high-performance text embedding model designed for classification. It is part of the **jina-embeddings-v5-text** model family, which also includes jina-embeddings-v5-text-small, for better performance at a bigger size. Trained using a novel approach that combines distillation with task-specific contrastive losses, `jina-embeddings-v5-text-nano-classification` outperforms existing state-of-the-art models of similar size across diverse embedding benchmarks. | Feature | Value | | --- | --- | | Parameters | 239M | | Supported Tasks | `classification` | | Max Sequence Length | 8192 | | Embedding Dimension | 768 | | Matryoshka Dimensions | 32, 64, 128, 256, 512, 768 | | Pooling Strategy | Last-token pooling | | Base Model | jinaai/jina-embeddings-v5-text-nano |
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View details ↗`jina-embeddings-v5-text-small-classification` is a compact, high-performance text embedding model designed for classification. It is part of the **jina-embeddings-v5-text** model family, which also includes jina-embeddings-v5-text-nano, a smaller model for more resource-constrained use cases. Trained using a novel approach that combines distillation with task-specific contrastive losses, `jina-embeddings-v5-text-small-classification` outperforms existing state-of-the-art models of similar size across diverse embedding benchmarks. | Feature | Value | | --- | --- | | Parameters | 677M | | Supported Tasks | `classification`| | Max Sequence Length | 32768 | | Embedding Dimension | 1024 | | Matryoshka Dimensions | 32, 64, 128, 256, 512, 768, 1024 | | Pooling Strategy | Last-token pooling | | Base Model | jinaai/jina-embeddings-v5-text-small |
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View details ↗jina-reranker-m0
jinaai--- pipeline_tag: text-classification tags: - sentence-transformers - vidore - reranker - qwen2_vl language: - multilingual base_model: - Qwen/Qwen2-VL-2B-Instruct inference: false license: cc-by-nc-4.0 library_name: transformers --- **jina-reranker-m0** is our new **multilingual multimodal reranker** model for ranking visual documents across multiple languages: it accepts a query alongside a collection of visually rich document images, including pages with text, figures, tables, infographics, and various layouts across multiple domains and over 29 languages. It outputs a ranked list of documents ordered by their relevance to the input query. Compared to `jina-reranker-v2-base-multilingual`, `jina-reranker-m0` also improves text reranking for multilingual content, long documents, and code searching tasks. **jina-reranker-m0** is built on a decoder-only vision language model architecture, specifically:
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View details ↗jina-reranker-m0-debug
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View details ↗emotion-english
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View details ↗Note that there is another endpoint, namely `jjzha/escoxlmr_skill_extraction`. Knowledge can be seen as hard skills and Skills are both soft and applied skills.
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View details ↗Note that there is another endpoint, namely `jjzha/escoxlmr_skill_extraction`. Knowledge can be seen as hard skills and Skills are both soft and applied skills.
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View details ↗gheim-ch-560m
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View details ↗Chinese-Emotion
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