Text Ranking Models

Model · Text ranking

ms-marco-MiniLM-L6-v2

Sentence Transformers - Cross-Encoders

This model was trained on the MS Marco Passage Ranking task. The model can be used for Information Retrieval: Given a query, encode the query with all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See SBERT.net Retrieve & Re-rank for more details. The training code is available here: SBERT.net Training MS Marco The usage is easy when you have SentenceTransformers installed. Then you can use the pre-trained models like this: In the following table, we provide various pre-trained Cross-Encoders together with their performance on the TREC Deep Learning 2019 and the MS Marco Passage Reranking dataset.

Model · Text ranking

ms-marco-MiniLM-L4-v2

Sentence Transformers - Cross-Encoders

This model was trained on the MS Marco Passage Ranking task. The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See SBERT.net Retrieve & Re-rank for more details. The training code is available here: SBERT.net Training MS Marco The usage is easy when you have SentenceTransformers installed. Then you can use the pre-trained models like this: In the following table, we provide various pre-trained Cross-Encoders together with their performance on the TREC Deep Learning 2019 and the MS Marco Passage Reranking dataset.

Model · Text ranking

Qwen3-Reranker-4B

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…

Model · Text ranking

mmarco-mMiniLMv2-L12-H384-v1

Sentence Transformers - Cross-Encoders

This model was trained on the MMARCO dataset. It is a machine translated version of MS MARCO using Google Translate. It was translated to 14 languages. In our experiments, we observed that it performs also well for other languages. As a base model, we used the multilingual MiniLMv2 model. The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See SBERT.net Retrieve & Re-rank for more details. The training code is available here: SBERT.net Training MS Marco The usage becomes easy when you have SentenceTransformers installed. Then, you can use the pre-trained…

Model · Text ranking

gte-reranker-modernbert-base

Alibaba-NLP

We are excited to introduce the gte-modernbert series of models, which are built upon the latest modernBERT pre-trained encoder-only foundation models. The gte-modernbert series models include both text embedding models and rerank models. The gte-modernbert models demonstrates competitive performance in several text embedding and text retrieval evaluation tasks when compared to similar-scale models from the current open-source community. This includes assessments such as MTEB, LoCO, and COIR evaluation. Use with transformers Use with sentence-transformers: Before you start, install the sentence-transformers libraries: Use with transformers.js Additionally, you can also deploy…

Model · Text ranking

ms-marco-MiniLM-L12-v2

Sentence Transformers - Cross-Encoders

This model was trained on the MS Marco Passage Ranking task. The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See SBERT.net Retrieve & Re-rank for more details. The training code is available here: SBERT.net Training MS Marco The usage is easy when you have SentenceTransformers installed. Then you can use the pre-trained models like this: In the following table, we provide various pre-trained Cross-Encoders together with their performance on the TREC Deep Learning 2019 and the MS Marco Passage Reranking dataset.

Model · Text ranking

Qwen3-Reranker-0.6B

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…

Model · Text ranking

Qwen3-VL-Reranker-2B

Qwen

The Qwen3-VL-Embedding and Qwen3-VL-Reranker model series are the latest additions to the Qwen family, built upon the recently open-sourced and powerful Qwen3-VL foundation model. Specifically designed for multimodal information retrieval and cross-modal understanding, this suite accepts diverse inputs including text, images, screenshots, and videos, as well as inputs containing a mixture of these modalities. While the Embedding model generates high-dimensional vectors for broad applications like retrieval and clustering, the Reranker model is engineered to refine these results, establishing a comprehensive pipeline for state-of-the-art multimodal search. Qwen3-VL-Reranker-2B has the…

Model · Text ranking

jina-reranker-v2-base-multilingual

Jina AI

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…

Model · Text ranking

llama-nemotron-rerank-1b-v2

NVIDIA

The Llama Nemotron Reranking 1B model is optimized for providing a logit score that represents how relevant a document(s) is to a given query. The model was fine-tuned for multilingual, cross-lingual text question-answering retrieval, with support for long documents (up to 8192 tokens). This model was evaluated on 26 languages: English, Arabic, Bengali, Chinese, Czech, Danish, Dutch, Finnish, French, German, Hebrew, Hindi, Hungarian, Indonesian, Italian, Japanese, Korean, Norwegian, Persian, Polish, Portuguese, Russian, Spanish, Swedish, Thai, and Turkish. This model is a component in a text retrieval system to improve the overall accuracy. A text retrieval system often uses an embedding…

Model · Text ranking

jina-reranker-v3

Jina AI

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…

Model · Text ranking

mxbai-rerank-xsmall-v1

Mixedbread

This is the smallest model in our family of powerful reranker models. You can learn more about the models in our blog post. Currently, the best way to use our models is with the most recent version of sentence-transformers. pip install -U sentence-transformers Let's say you have a query, and you want to rerank a set of documents. You can do that with only one line of code: Install transformers.js npm i @xenova/transformers Let's say you have a query, and you want to rerank a set of documents. In JavaScript, you need to add a function: You can use the large model via our API as follows: The API comes with additional features, such as a continous trained reranker! Check out the docs for more…

Model · Text ranking

ms-marco-electra-base

Sentence Transformers - Cross-Encoders

This model was trained on the MS Marco Passage Ranking task. The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See SBERT.net Retrieve & Re-rank for more details. The training code is available here: SBERT.net Training MS Marco The usage is easy when you have SentenceTransformers installed. Then you can use the pre-trained models like this: In the following table, we provide various pre-trained Cross-Encoders together with their performance on the TREC Deep Learning 2019 and the MS Marco Passage Reranking dataset.

Model · Text ranking

ms-marco-TinyBERT-L2-v2

Sentence Transformers - Cross-Encoders

This model was trained on the MS Marco Passage Ranking task. The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See SBERT.net Retrieve & Re-rank for more details. The training code is available here: SBERT.net Training MS Marco The usage becomes easier when you have SentenceTransformers installed. Then, you can use the pre-trained models like this: In the following table, we provide various pre-trained Cross-Encoders together with their performance on the TREC Deep Learning 2019 and the MS Marco Passage Reranking dataset.

Model · Text ranking

ms-marco-MiniLM-L2-v2

Sentence Transformers - Cross-Encoders

This model was trained on the MS Marco Passage Ranking task. The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See SBERT.net Retrieve & Re-rank for more details. The training code is available here: SBERT.net Training MS Marco The usage is easy when you have SentenceTransformers installed. Then you can use the pre-trained models like this: In the following table, we provide various pre-trained Cross-Encoders together with their performance on the TREC Deep Learning 2019 and the MS Marco Passage Reranking dataset.

Model · Text ranking

cross-encoder-russian-msmarco

Dmitry Tishencko

This is a sentence-transformers model based on a pre-trained DeepPavlov/rubert-base-cased and finetuned with MS-MARCO Russian passage ranking dataset. The model can be used for Information Retrieval in the Russian language: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See SBERT.net Retrieve & Re-rank for more details. Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you need to get the logits from the…

Model · Text ranking

qnli-electra-base

Sentence Transformers - Cross-Encoders

This model was trained using SentenceTransformers Cross-Encoder class. Given a question and paragraph, can the question be answered by the paragraph? The models have been trained on the GLUE QNLI dataset, which transformed the SQuAD dataset into an NLI task. For performance results of this model, see [SBERT.net Pre-trained Cross-Encoder][https://www.sbert.net/docs/pretrainedcross-encoders.html]. Pre-trained models can be used like this: You can use the model also directly with Transformers library (without SentenceTransformers library)

Model · Text ranking

SKILLRET-Reranker-0.6B

ThakiCloud

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. Technical report: SkillRet: A Large-Scale Benchmark for Skill Retrieval in LLM Agents (arXiv:2605.05726) Each skill document is body, the same representation used by the SkillRet…

Model · Text ranking

KaLM-Reranker-V1-Nano-R2

KaLM-Embedding

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…

Model · Text ranking

GemmaDecision-270M

Rajan Shukla

A fully tuned Gemma-270M candidate ranker: it scores each supplied choice together with the request, using a scalar decision head. Intended research tasks are banking-support topic selection and three-way evidence relations. It does not generate answers. The complete modified encoder and head are included. On the private fresh-prompt test, accuracy is 96.50% for four-way banking topics and 78.00% for three-way NLI, with an equal-family mean of 87.25%. Both frozen task targets were met on this private test. See RESULTS.md for both baselines, paired uncertainty, coverage, synthetic challenge results and failures. Package 0.2.0 uses ONNX Runtime on CPU by default. The standard install requires…

Model · Text ranking

KaLM-Reranker-V1-Small-R2

KaLM-Embedding

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…

Model · Text ranking

KaLM-Reranker-V1-Large-R2

KaLM-Embedding

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…

Model · Text ranking

ettin-150m-memory-reranker-ft-v1

Daecore

An English cross-encoder for ranking passages from notes, procedures, decisions and technical documentation. Give it a query and candidate passages; it returns one relevance score per pair. It is a fine-tune of Ettin Reranker 150M and runs locally through ONNX Runtime. This revision adds a Vulkan FP32 graph, model.vulkan.onnx; the learned weights and the CUDA FP16 graph are unchanged. It omits the DirectML graph, which remains in the previous revision. Install tokenizers, numpy and onnxruntime-gpu[cuda,cudnn]==1.24.4. Keep model.onnx, model.onnx.data and the tokenizer from the same package together. This CUDA example assumes that package is in downloaded-model and scores two passages…

Model · Text ranking

CLM-v0.1-8B-gguf

EvoAwaken Workshop

This repository contains the state and action projection heads only from CLM scores candidate actions against a state; it does not generate text. A separate, frozen Qwen3-8B encoder is required to turn text into embeddings. The source checkpoint is CLMv0.1-8B.pt (SHA-256 b2b4a8c9c2d39263eff78a351eb909a342ce9b3bf21a3f07c1d1bf15f1c4eda5). The converter preserves the F32 weight and bias bytes. It stores the reference scoring scale min(exp(logitscale), 100) = 100 in the GGUF. The GGUF does not contain the Qwen3-8B encoder, tokenizer, or original.pt file. The verified encoder file is SHA-256 5e416a2020fe63e76ea13c8979be35fc6070aaf3578f7876400c55c2f5c3eb30. Use rust-model-inference at or after…

Why the disagreement is the feature, not the bug: kev downgrades high-frequency auto-recovering alerts (the #1 source of alert fatigue) and escalates orchestration-core outages that static rules treat as routine. - None-injection (p=0.1): teaches the head to abstain, not everything deserves a label We ran the obvious next experiment: fine-tune it on its own production data (5-fold grouped CV, 4-arm temperature calibration). Result: 0 gain over the deployed version — and we did not deploy it, per preset criteria defined before the experiment. That's the discipline this model is proud of: judge the upgrade on pre-registered criteria, not vibes. Training receipts and the arbitration table are…

Model · Text ranking

KielEmbed-Rerank

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…

Model · Text ranking

nb-asr-qwen06b-bokmal-nynorsk-reranker

Nasjonalbiblioteket AI Lab

This repository is under construction. No selected, validated model has been released yet. This model was created by Thea Tollersrud and Per Egil Kummervold in the NB-ASR project. The planned model recognizes equivalent Bokmål–Nynorsk text while detecting changed numbers, pronouns, entities, and participant roles. Meaning-preserving differences and reorderings are accepted. Training starts from Qwen/Qwen3-Reranker-0.6B and uses revision 619e36b85e4943f23cd85b66edcde331995e4872. Validation is automatic, without manual review. Accuracy will be conditional on the supplied positive pairs and the automatic corruption rules. See trainingstatus.json for the latest recorded job status. A complete…

Model · Text ranking

rerank-indonesia-Q8_0-GGUF

Radhi Fadlillah

This model was converted to GGUF format from madebyaris/rerank-indonesia using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model. Install llama.cpp through brew (works on Mac and Linux) Invoke the llama.cpp server or the CLI. Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well. Step 2: Move into the llama.cpp folder and build it with LLAMACURL=1 flag along with other hardware-specific flags (for ex: LLAMACUDA=1 for Nvidia GPUs on Linux).

本仓库提供由 Qwen/Qwen3-Reranker-0.6B 转换的 RKNN 模型。 - Model ID：RKNNAI/RK182X-EMBEDDING-Qwen3-Reranker-0.6B - 模型显示名称：RK182X-EMBEDDING-Qwen3-Reranker-0.6B - 源模型：Qwen/Qwen3-Reranker-0.6B - 模型类型：EMBEDDING ModelScope 完整下载： Hugging Face 完整下载： ModelScope 指定配置下载： Hugging Face 指定配置下载： - 使用配套 RKNN Runtime 和驱动；运行前用 rknn-smi -v 检查设备端版本。 - 源模型许可证：Apache License 2.0，正文见 LICENSE，归属与转换修改说明见 NOTICE。 - 同时遵守 RKNN Toolkit、RKNN Runtime 相关许可；本包不包含运行库。

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…