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Original model: mxbai-embed-large-v1 This is our base sentence embedding model. It was trained using AnglE loss on our high-quality large scale data. It achieves SOTA performance on BERT-large scale. Find out more in our blog post. This repo contains GGUF format files for the mxbai-embed-large-v1 embedding model.

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GLInt

chungimungi

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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The **SecureBERT 2.0 Bi-Encoder** is a cybersecurity-domain sentence-similarity and document-embedding model fine-tuned from SecureBERT 2.0. It independently encodes queries and documents into a shared vector space for **semantic search**, **information retrieval**, and **cybersecurity knowledge retrieval**. The Bi-Encoder encodes queries and documents **independently** into a joint vector space. This architecture enables scalable **approximate nearest-neighbor search** for candidate retrieval and semantic ranking. The model was optimized to maximize semantic similarity between relevant cybersecurity text pairs using contrastive learning.

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The **SecureBERT 2.0 Cross-Encoder** is a cybersecurity domain-specific model fine-tuned from SecureBERT 2.0. It computes **pairwise similarity scores** between two texts, enabling use in **text reranking, semantic search, and cybersecurity intelligence retrieval** tasks. Can be integrated into: - Cyber threat intelligence search engines - SOC automation pipelines - Cybersecurity knowledge graph enrichment - Threat hunting and incident response systems The model reflects the distribution of cybersecurity-related data used during fine-tuning. Potential risks include: - Overrepresentation of specific malware, technologies, or threat actors - Bias toward technical English sources - Reduced performance on non-English or mixed technical/natural text

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ruri-v3-130m

cl-nagoya

**Ruri v3** is a general-purpose Japanese text embedding model built on top of **ModernBERT-Ja**. Ruri v3 offers several key technical advantages: - **State-of-the-art performance** for Japanese text embedding tasks. - **Supports sequence lengths up to 8192 tokens** - Previous versions of Ruri (v1, v2) were limited to 512. - **Expanded vocabulary of 100K tokens**, compared to 32K in v1 and v2 - The larger vocabulary make input sequences shorter, improving efficiency. - **Integrated FlashAttention**, following ModernBERT's architecture - Enables faster inference and fine-tuning. - **Tokenizer based solely on SentencePiece** - Unlike previous versions, which relied on Japanese-specific BERT tokenizers and required pre-tokenized input, Ruri v3 performs tokenization with SentencePiece only—no external word segmentation tool is required. Additionally, if your GPUs support Flash Attention 2, we recommend using our models with Flash Attention 2. - **Model Type:** Sentence Transformer - **Base model:** cl-nagoya/ruri-v3-pt-130m - **Maximum Sequence Length:** 8192 tokens - **Output Dimensionality:** 512 - **Similarity Function:** Cosine Similarity - **Language:** Japanese - **License:** Apache 2.0 - **Paper:** https://arxiv.org/abs/2409.07737

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ruri-v3-30m

cl-nagoya

**Ruri v3** is a general-purpose Japanese text embedding model built on top of **ModernBERT-Ja**. Ruri v3 offers several key technical advantages: - **State-of-the-art performance** for Japanese text embedding tasks. - **Supports sequence lengths up to 8192 tokens** - Previous versions of Ruri (v1, v2) were limited to 512. - **Expanded vocabulary of 100K tokens**, compared to 32K in v1 and v2 - The larger vocabulary make input sequences shorter, improving efficiency. - **Integrated FlashAttention**, following ModernBERT's architecture - Enables faster inference and fine-tuning. - **Tokenizer based solely on SentencePiece** - Unlike previous versions, which relied on Japanese-specific BERT tokenizers and required pre-tokenized input, Ruri v3 performs tokenization with SentencePiece only—no external word segmentation tool is required. Additionally, if your GPUs support Flash Attention 2, we recommend using our models with Flash Attention 2. - **Model Type:** Sentence Transformer - **Base model:** cl-nagoya/ruri-v3-pt-30m - **Maximum Sequence Length:** 8192 tokens - **Output Dimensionality:** 256 - **Similarity Function:** Cosine Similarity - **Language:** Japanese - **License:** Apache 2.0 - **Paper:** https://arxiv.org/abs/2409.07737

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ruri-v3-70m

cl-nagoya

**Ruri v3** is a general-purpose Japanese text embedding model built on top of **ModernBERT-Ja**. Ruri v3 offers several key technical advantages: - **State-of-the-art performance** for Japanese text embedding tasks. - **Supports sequence lengths up to 8192 tokens** - Previous versions of Ruri (v1, v2) were limited to 512. - **Expanded vocabulary of 100K tokens**, compared to 32K in v1 and v2 - The larger vocabulary make input sequences shorter, improving efficiency. - **Integrated FlashAttention**, following ModernBERT's architecture - Enables faster inference and fine-tuning. - **Tokenizer based solely on SentencePiece** - Unlike previous versions, which relied on Japanese-specific BERT tokenizers and required pre-tokenized input, Ruri v3 performs tokenization with SentencePiece only—no external word segmentation tool is required. Additionally, if your GPUs support Flash Attention 2, we recommend using our models with Flash Attention 2. - **Model Type:** Sentence Transformer - **Base model:** cl-nagoya/ruri-v3-pt-70m - **Maximum Sequence Length:** 8192 tokens - **Output Dimensionality:** 384 - **Similarity Function:** Cosine Similarity - **Language:** Japanese - **License:** Apache 2.0 - **Paper:** https://arxiv.org/abs/2409.07737

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mfaq

clips

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This is a sentence-transformers model finetuned from keepitreal/vietnamese-sbert on the csv dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

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This Model2Vec model was created by using Tokenlearn, with nomic-embed-text-v2-moe as a base. The evaluation in the model card, was executed using this model (distilled), not the original. The process to create this one, was not a simple model2vec distill, this involved generating embeddings for 23M triplets (msmarco) with the original model, then training the tokenlearn model on it, with the nomic model as a base.

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