mmarco-mMiniLMv2-L12-H384-v1
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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 2428
bert-base-uncased-mnli
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查看详情 ↗roberta-base-cola
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查看详情 ↗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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查看详情 ↗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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查看详情 ↗jina-embeddings-v3
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查看详情 ↗*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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查看详情 ↗`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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查看详情 ↗`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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查看详情 ↗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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查看详情 ↗jina-reranker-m0-debug
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查看详情 ↗emotion-english
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查看详情 ↗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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查看详情 ↗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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查看详情 ↗gheim-ch-560m
joelbarmettler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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查看详情 ↗Chinese-Emotion
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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。