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`jina-embeddings-v5-text-small-retrieval` is a compact, high-performance text embedding model designed for information retrieval. 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-retrieval` outperforms existing state-of-the-art models of similar size across diverse embedding benchmarks. | Feature | Value | | --- | --- | | Parameters | 677M | | Supported Tasks | `retrieval`| | 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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`jina-embeddings-v5-text-small-text-matching` is a compact, high-performance text embedding model designed for text-matching. 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-text-matching` outperforms existing state-of-the-art models of similar size across diverse embedding benchmarks. | Feature | Value | | --- | --- | | Parameters | 677M | | Supported Tasks | `text-matching`| | 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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BROS (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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Reproducibility report can be found here: https://chaochunhsu.github.io/patterns/blogs/modernbert_dpr/ This is a sentence-transformers model finetuned from answerdotai/ModernBERT-base on the msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1 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. I finetune ModernBERT-base using script from offical repo train_st.py on a RTX 4090 GPU with the only change of setting mini-batch size of `CachedMultipleNegativesRankingLoss` to 128. Training for 1 epoch takes less than an hour.

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A 300-dimensional skip-gram ingredient embedding over a 1,790-ingredient canonical vocabulary, trained on a blend of (i) typed FlavorDB ingredient-compound metapath walks and (ii) injected pure ingredient-ingredient walks at `ii_repeat=10`. Core is the middle sibling on the chemistry-vs-recipe-context spectrum. The 10x I-I injection is the design lever that concentrates Core's geometry: participation ratio drops to 94.2 of 300 (vs ~180 for the isotropic Cooc and Chem siblings), average pairwise cosine rises to 0.35, and the resulting concentration coincides with the tightest emergent modes of the three. Core's concentrated geometry pulls both pole tightness and the all-pairs floor upward; the tightness margin (mode coherence minus baseline) is comparable to Cooc and Chem at ~0.5, so the concentration is a design lever, not a defect.

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This repository contains more than 25 different types of embedding models, together with practical information about embeddings, document retrieval, chunking, context length, and local RAG (Retrieval-Augmented Generation). Some models might need a mmproj-file for image/video/audio - but i focus on txt here. * the quality and structure of your source documents * how the documents are converted to text * how the text is split into chunks * the embedding model * the vector database and retrieval settings * optional keyword or reranking steps * the context given to the main LLM * the main LLM itself * the system prompt Changing only the embedding model therefore does not automatically make a RAG system better.

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**KaLM-Embedding-Gemma3-12B-2511** is a versatile and compact embedding model, which achieves SOTA performance in MMTEB (due to 11-2025). - Model Size: 11.76B - Embedding Dimension: 3840 - Max Input Tokens: 32k - MRL dimensions: 3840, 2048, 1024, 512, 256, 128, and 64 - Pooling: lasttoken pooling Using this model becomes easy when you have sentence-transformers installed:

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