KURE-v1-gguf
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View details ↗DISCOVER & COMPARE
Explore AI models 2043
Qwen3-Embedding-0.6B-GGUF
n24q02m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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View details ↗Qwen3-Embedding-0.6B-ONNX
n24q02m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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View details ↗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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View details ↗indus-sde-st-v0.2
nasa-impact> **Paper:** INDUS-SDE: A Language Model for Scientific Content Curation and Discovery — **KDD 2026, AI for Sciences Track**. INDUS-SDE-ST is a sentence transformer for semantic scientific discovery, built on the INDUS-SDE encoder. > **Code:** NASA-IMPACT/st-training-workflow · **Binary (EQAT) variant:** indus-sde-st-equat-v0.1 The model was first further fine tuned on sentence embedding task on top of previous (nasa-impact/indus-sde-st-v0.1) using stage 2 dataset (scientific dataset) for a epoch. Then this model is again fined tuned for 2 more epoches on NASA SDE and NASA ADS corpus. The initial stage of Indus-SDE-ST training focused on adapting the base Indus-SDE model to comprehend general domain semantics and sentence-pair relationships. The stage 2 dataset was designed for scieinfic domain adaptation. The primary objective was to establish a broad linguistic foundation before specializing in scientific content (for subsequent stages). This was achieved using a diverse corpus comprising pairs from S2ORC, arxiv, PubMed, NASA ADS and NASA SDE set in a contrastive learning objective: Multiple Negatives Ranking loss.
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View details ↗nasa-smd-ibm-st-v2
nasa-impactIndus-Retriever (`nasa-smd-ibm-st-v2`) is a Bi-encoder sentence transformer model, that is fine-tuned from nasa-smd-ibm-v0.1 encoder model. it is an updated version of `nasa-smd-ibm-st` with better performance (shown below). It's trained with 271 million examples along with a domain-specific dataset of 2.6 million examples from documents curated by NASA Science Mission Directorate (SMD). With this model, we aim to enhance natural language technologies like information retrieval and intelligent search as it applies to SMD NLP applications. you can also use distilled version of the model here: https://huggingface.co/nasa-impact/nasa-ibm-st.38m 1. All-MiniLM-l6-v2 [sentence-transformers/all-MiniLM-L6-v2] 2. BGE-base [BAAI/bge-base-en-v1.5] 3. RoBERTa-base [roberta-base] 4. nasa-smd-ibm-rtvr_v0.1 [nasa-impact/nasa-smd-ibm-st]
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View details ↗embeddinggemma-2-8bit
nativ-community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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View details ↗indonesian-sbert-large
naufalihsanThis is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. 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. For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: https://seb.sbert.net
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View details ↗bros-base-uncased
naver-clova-ocrBROS (BERT Relying On Spatiality) is a pre-trained language model focusing on text and layout for better key information extraction from documents. Given the OCR results of the document image, which are text and bounding box pairs, it can perform various key information extraction tasks, such as extracting an ordered item list from receipts. For more details, please refer to our paper: BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from Documents Teakgyu Hong, Donghyun Kim, Mingi Ji, Wonseok Hwang, Daehyun Nam, Sungrae Park AAAI 2022 - Main Technical Track
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View details ↗Efficient SPLADE model for passage retrieval. This architecture uses two distinct models for query and document inference. This is the **doc** one, please also download the **query** one (https://huggingface.co/naver/efficient-splade-VI-BT-large-query). For additional details, please visit: * paper: https://dl.acm.org/doi/10.1145/3477495.3531833 * code: https://github.com/naver/splade | | MRR@10 (MS MARCO dev) | R@1000 (MS MARCO dev) | Latency (PISA) ms | Latency (Inference) ms | --- | --- | --- | --- | --- | | `naver/efficient-splade-V-large` | 38.8 | 98.0 | 29.0 | 45.3 | `naver/efficient-splade-VI-BT-large` | 38.0 | 97.8 | 31.1 | 0.7 This is a Asymmetric SPLADE Sparse Encoder model. It maps sentences & paragraphs to a 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval. - **Model Type:** SPLADE Sparse Encoder - **Maximum Sequence Length:** 512 tokens (256 for evaluation reproduction) - **Output Dimensionality:** 30522 dimensions - **Similarity Function:** Dot Product
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View details ↗SPLADE model for passage retrieval. For additional details, please visit: * paper: https://arxiv.org/abs/2205.04733 * code: https://github.com/naver/splade This is a SPLADE Sparse Encoder model. It maps sentences & paragraphs to a 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval. - **Model Type:** SPLADE Sparse Encoder - **Base model:** Luyu/co-condenser-marco - **Maximum Sequence Length:** 512 tokens (256 for evaluation reproduction) - **Output Dimensionality:** 30522 dimensions - **Similarity Function:** Dot Product
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View details ↗SPLADE model for passage retrieval. For additional details, please visit: * paper: https://arxiv.org/abs/2205.04733 * code: https://github.com/naver/splade This is a SPLADE Sparse Encoder model. It maps sentences & paragraphs to a 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval. - **Model Type:** SPLADE Sparse Encoder - **Base model:** Luyu/co-condenser-marco - **Maximum Sequence Length:** 512 tokens (256 for evaluation reproduction) - **Output Dimensionality:** 30522 dimensions - **Similarity Function:** Dot Product
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View details ↗splade-code-06B
naver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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View details ↗splade-code-8B
naver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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View details ↗splade_v2_distil
naverSPLADE model for passage retrieval. For additional details, please visit: * paper: https://arxiv.org/abs/2109.10086 * code: https://github.com/naver/splade This is a SPLADE Sparse Encoder model. It maps sentences & paragraphs to a 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval. - **Model Type:** SPLADE Sparse Encoder - **Base model:** distilbert/distilbert-base-uncased - **Maximum Sequence Length:** 512 tokens (256 for evaluation reproduction) - **Output Dimensionality:** 30522 dimensions - **Similarity Function:** Dot Product
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View details ↗splade-v3
naver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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View details ↗splade-v3-distilbert
naverFor more details, see our arXiv companion book: https://arxiv.org/abs/2403.06789 To use SPLADE, please visit our GitHub repository: https://github.com/naver/splade This is a SPLADE Sparse Encoder model. It maps sentences & paragraphs to a 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval.
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View details ↗borealis-embed-212m
NbAiLab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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View details ↗nb-sbert-base
NbAiLab> [!NOTE] > As of April 13th 2026, there are now new versions of this model, with improved performance and increased context length. > - NbAiLab/nb-sbert-v2-base > - NbAiLab/nb-sbert-v2-large NB-SBERT-BASE is a SentenceTransformers model trained on a machine translated version of the MNLI dataset, starting from nb-bert-base. The model maps sentences & paragraphs to a 768 dimensional dense vector space. This vector can be used for tasks like clustering and semantic search. Below we give some examples on how to use the model. The easiest way is to simply measure the cosine distance between two sentences. Sentences that are close to each other in meaning, will have a small cosine distance and a similarity close to 1. The model is trained in such a way that similar sentences in different languages should also be close to each other. Ideally, an English-Norwegian sentence pair should have high similarity.
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View details ↗nb-sbert-v2-base
NbAiLab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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View details ↗nb-sbert-v2-large
NbAiLab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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View details ↗**MedCPT generates embeddings of biomedical texts that can be used for semantic search (dense retrieval)**. The model contains two encoders: - MedCPT Query Encoder: compute the embeddings of short texts (e.g., questions, search queries, sentences). - MedCPT Article Encoder: compute the embeddings of articles (e.g., PubMed titles & abstracts). **MedCPT has been pre-trained by an unprecedented scale of 255M query-article pairs from PubMed search logs**, and has been shown to achieve state-of-the-art performance on several zero-shot biomedical IR datasets. In general, there are three use cases: 1. Query-to-article search with both encoders. 2. Query representation for clustering or query-to-query search with the query encoder. 3. Article representation for clustering or article-to-article search with the article encoder. For more details, please check out our paper (Bioinformatics, 2023). Please note that the released version is slightly different from the version reported in the paper.
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View details ↗**MedCPT generates embeddings of biomedical texts that can be used for semantic search (dense retrieval)**. The model contains two encoders: - MedCPT Query Encoder: compute the embeddings of short texts (e.g., questions, search queries, sentences). - MedCPT Article Encoder: compute the embeddings of articles (e.g., PubMed titles & abstracts). **MedCPT has been pre-trained by an unprecedented scale of 255M query-article pairs from PubMed search logs**, and has been shown to achieve state-of-the-art performance on several zero-shot biomedical IR datasets. In general, there are three use cases: 1. Query-to-article search with both encoders. 2. Query representation for clustering or query-to-query search with the query encoder. 3. Article representation for clustering or article-to-article search with the article encoder. For more details, please check out our paper (Bioinformatics, 2023). Please note that the released version is slightly different from the version reported in the paper.
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View details ↗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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