anchored-embeddinggemma-2-vi

Model · Feature extraction

mxbai-embed-large-v1

Mixedbread

Here, we provide several ways to produce sentence embeddings. Please note that you have to provide the prompt Represent this sentence for searching relevant passages: for query if you want to use it for retrieval. Besides that you don't need any prompt. Our model also supports Matryoshka Representation Learning and binary quantization. Here, we provide several ways to produce sentence embeddings. Please note that you have to provide the prompt Represent this sentence for searching relevant passages: for query if you want to use it for retrieval. Besides that you don't need any prompt. If you haven't already, you can install the Transformers.js JavaScript library from NPM using: You can then…

WhereIsAI/UAE-Large-V1 is licensed under MIT. Feel free to use it in any scenario. If you use it for academic papers, you could cite us via citation info. Welcome to using AnglE to train and infer powerful sentence embeddings. Achievements - May 16, 2024 | AnglE's paper is accepted by ACL 2024 Main Conference - Dec 4, 2023 | Our universal English sentence embedding WhereIsAI/UAE-Large-V1 achieves SOTA on the MTEB Leaderboard with an average score of 64.64! - WhereIsAI/UAE-Code-Large-V1: This model can be used for code or GitHub issue similarity measurement. There is no need to specify any prompts. For retrieval purposes, please use the prompt Prompts.C for query (not for document). Infinity…

Model · Feature extraction

bge-large-en-v1.5

Beijing Academy of Artificial Intelligence

For more details please refer to our Github: FlagEmbedding. If you are looking for a model that supports more languages, longer texts, and other retrieval methods, you can try using bge-m3. FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently: - 1/30/2024: Release BGE-M3, a new member to BGE model series! M3 stands for Multi-linguality (100+ languages), Multi-granularities (input length up to 8192), Multi-Functionality (unification of dense, lexical, multi-vec/colbert retrieval). It is the first embedding model that supports all three retrieval methods, achieving new SOTA on multi-lingual (MIRACL) and cross-lingual (MKQA) benchmarks. Technical…

Model · Feature extraction

Vela-1.0-Omni-Nano

vLLM Semantic Router

Text, images, speech and environmental audio in one embedding space. Vela Omni Nano supports multimodal search, routing and clustering with normalized vectors that can be compared directly. A frozen CLAP audio branch adds environmental-sound information to the existing speech representation. Text and image computations are retained; audio embeddings are newly trained and evaluated. Architecture and measured identity. Scores are 0–100; higher is better. The comparison uses the same held-out evaluation examples, complete retrieval pools and 128-token text cap for both models. Bold marks improvement over the original small model. These known test pools are reused across releases. The common…

Model · Feature extraction

jina-embeddings-v2-base-code

Jina AI

The easiest way to starting using jina-embeddings-v2-base-code is to use Jina AI's Embedding API. jina-embeddings-v2-base-code is an multilingual embedding model speaks English and 30 widely used programming languages. Same as other jina-embeddings-v2 series, it supports 8192 sequence length. jina-embeddings-v2-base-code is based on a Bert architecture (JinaBert) that supports the symmetric bidirectional variant of ALiBi to allow longer sequence length. The backbone jina-bert-v2-base-code is pretrained on the github-code dataset. The model is further trained on Jina AI's collection of more than 150 millions of coding question answer and docstring source code pairs. These pairs were obtained…

PhaGen-0404-step12800 is the frozen paper model: 152,421,601 parameters, three hierarchical stages with dimensions 512/256/196, maximum token canvas 131,072, and a nine-token character-level DNA tokenizer. This repository contains the standalone inference weights, tokenizer, model configuration and custom code. Weights are the selected April 2026 run's global step 12,800, not the final saved checkpoint or a later training implementation. Use PyTorch 2.5, Transformers 4.54.1, einops 0.8, beartype 0.22, accelerate and safetensors. The PhaGen source repository also provides an installable phagen CLI and an agent skill. Pin a Hub commit hash with revision for repeatable loading. Custom model…