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探索 AI 模型 84999
`jina-embeddings-v5-text-small-text-matching` is a compact, high-performance text embedding model designed for text-matching. 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-text-matching` outperforms existing state-of-the-art models of similar size across diverse embedding benchmarks. | Feature | Value | | --- | --- | | Parameters | 677M | | Supported Tasks | `text-matching`| | 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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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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jinaaiThis model is designed for **blazing-fast** reranking while maintaining **competitive performance**. What's more, it leverages the power of our JinaBERT model as its foundation. `JinaBERT` itself is a unique variant of the BERT architecture that supports the symmetric bidirectional variant of ALiBi. This allows `jina-reranker-v1-tiny-en` to process significantly longer sequences of text compared to other reranking models, up to an impressive **8,192** tokens. To achieve the remarkable speed, the `jina-reranker-v1-tiny-en` employ a technique called knowledge distillation. Here, a complex, but slower, model (like our original jina-reranker-v1-base-en) acts as a teacher, condensing its knowledge into a smaller, faster student model. This student retains most of the teacher's knowledge, allowing it to deliver similar accuracy in a fraction of the time. > Currently, the `jina-reranker-v1-base-en` model is not available on Hugging Face. You can access it via the Jina AI Reranker API.
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查看详情 ↗This model is designed for **blazing-fast** reranking while maintaining **competitive performance**. What's more, it leverages the power of our JinaBERT model as its foundation. `JinaBERT` itself is a unique variant of the BERT architecture that supports the symmetric bidirectional variant of ALiBi. This allows `jina-reranker-v1-turbo-en` to process significantly longer sequences of text compared to other reranking models, up to an impressive **8,192** tokens. To achieve the remarkable speed, the `jina-reranker-v1-turbo-en` employ a technique called knowledge distillation. Here, a complex, but slower, model (like our original jina-reranker-v1-base-en) acts as a teacher, condensing its knowledge into a smaller, faster student model. This student retains most of the teacher's knowledge, allowing it to deliver similar accuracy in a fraction of the time. > Currently, the `jina-reranker-v1-base-en` model is not available on Hugging Face. You can access it via the Jina AI Reranker API.
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查看详情 ↗The **Jina Reranker v2** (`jina-reranker-v2-base-multilingual`) is a transformer-based model that has been fine-tuned for text reranking task, which is a crucial component in many information retrieval systems. It is a cross-encoder model that takes a query and a document pair as input and outputs a score indicating the relevance of the document to the query. The model is trained on a large dataset of query-document pairs and is capable of reranking documents in multiple languages with high accuracy. Compared with the state-of-the-art reranker models, including the previous released `jina-reranker-v1-base-en`, the **Jina Reranker v2** model has demonstrated competitiveness across a series of benchmarks targeting for text retrieval, multilingual capability, function-calling-aware and text-to-SQL-aware reranking, and code retrieval tasks. The `jina-reranker-v2-base-multilingual` model is capable of handling long texts with a context length of up to `1024` tokens, enabling the processing of extensive inputs. To enable the model to handle long texts that exceed 1024 tokens, the model uses a sliding window approach to chunk the input text into smaller pieces and rerank each chunk separately.
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jinaai> [!IMPORTANT] > **Prefer `jina-reranker-v3.5`** for new projects — it is a drop-in upgrade with stronger domain / multilingual / structured rankings and faster listwise inference. Same API; switch the model id from `jinaai/jina-reranker-v3` to `jinaai/jina-reranker-v3.5`. > [!TIP] > GGUF with quantizations and MLX versions are now available. `jina-reranker-v3` is a 0.6B parameter multilingual document reranker with a novel *last but not late interaction* architecture. Unlike ColBERT's separate encoding with multi-vector matching, this model performs causal self-attention between query and documents within the same context window, extracting contextual embeddings from the last token of each document.
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jinaai> [!TIP] > GGUF with quantizations and MLX versions are now available. `jina-reranker-v3.5` is a 0.6B-parameter multilingual listwise document reranker and a drop-in upgrade to `jina-reranker-v3`. It keeps the same *last-but-not-late* (LBNL) listwise interface — ranking a query against many candidates in one forward pass — while improving **domain robustness**, **structured-data ranking**, **multilingual retrieval**, and **inference efficiency**. Built on Qwen3-0.6B with 28 layers (hybrid **3L2G** attention: sliding-window layers interleaved with global layers, window = 1024, pinned terminal global layer required by LBNL) and a lightweight MLP projector (1024→512→512), it supports up to 131K tokens of context and ranks lists of documents jointly via causal self-attention and cosine scoring.
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查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
仅比较同一渠道的基础文本 Token 报价
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
MODEL HANDBOOK
读懂模型，再做选择
API 价格应该怎么比较？+
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
开放权重，就代表可以免费商用吗？+
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
排行榜第一，就是最适合我的模型吗？+
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。