Model · Text ranking
Qwen
The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B). This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining. Exceptional Versatility: The…
Open weights
apache-2.0
596M parameters
40,960 tokens
transformers
This is a reranker fine-tuned for AI agent skill retrieval. Given a natural-language user request and a candidate agent skill, it scores how relevant and useful the skill is for the request. It is designed as the second stage after a first-stage retriever such as SkillRet-Embedding-0.6B or SkillRet-Embedding-8B. The model is fine-tuned from Qwen/Qwen3-Reranker-0.6B on the SkillRet benchmark training split with binary cross-entropy on the yes/no token probability. It keeps the scoring interface of Qwen3-Reranker. Each skill document is body, the same representation used by the SkillRet embedding models. Evaluated on the SkillRet benchmark evaluation split (4,392 queries, 6,006 skills). The…
Open weights
apache-2.0
596M parameters
40,960 tokens
transformers
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. Built on Qwen3-0.6B with 28 transformer layers and a lightweight MLP projector (1024→512→256), it processes up to 64 documents simultaneously within 131K token context. The model achieves state-of-the-art BEIR performance with 61.94 nDCG@10 while being 10× smaller than generative listwise rerankers. Use transformers for local inference…
Open weights
cc-by-nc-4.0
597M parameters
131,072 tokens
transformers
KaLM-Reranker-V1-R2 is a substantially improved checkpoint release of KaLM-Reranker-V1. It keeps the same fast-but-not-late-interaction (FBNL) architecture and inference interface as the original release, while introducing a stronger multi-stage training recipe for three practical goals: 1. Stronger compression robustness. R2 supports Matryoshka embedding pooling (MEP) from 1x to 128x, extending the maximum validated compression ratio from 32x to 128x. Even at 128x compression, all three model sizes retain at least 92% of their average nDCG@10 at 2x compression on both BEIR and MIRACL. 2. Adjustable test-time compute scaling. A single KaLM-Reranker-V1-R2 checkpoint can trade compute for…
Open weights
apache-2.0
786M parameters
sentence-transformers
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…
Open weights
cc-by-nc-4.0
278M parameters
1,026 tokens
transformers
Model · Text ranking
Tech
This is a Cross Encoder model finetuned from BAAI/bge-reranker-base using the sentence-transformers library. It computes scores for pairs of texts, which can be used for text reranking and semantic search. First install the Sentence Transformers library: Then you can load this model and run inference. Approximate statistics based on the first 100 samples: - perdevicetrainbatchsize: 4 - numtrainepochs: 1 - learningrate: 2e-05 - warmupsteps: 0.1 - gradientaccumulationsteps: 8 - fp16: True - perdevicetrainbatchsize: 4 - numtrainepochs: 1 - maxsteps: -1 - learningrate: 2e-05 - lrschedulertype: linear - lrschedulerkwargs: None - warmupsteps: 0.1 - optim: adamwtorchfused - optimargs: None…
Open weights
278M parameters
514 tokens
sentence-transformers
