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…
Open weights
apache-2.0
161M parameters
8,192 tokens
sentence-transformers
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…
Open weights
apache-2.0
164M parameters
pytorch
This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
Open weights
122M parameters
transformers
A headless four-layer text backbone distilled from Qwen3.5-0.8B, with a vocabulary cut to English and Korean. It has no language-model head, no classification head, no labels and no thresholds. You attach a head and train it. This card is the record of how it was made. It walks the whole path — prompting the original model, turning it into a classifier, removing layers, changing precision, cutting the vocabulary, and cutting it again by rule — and shows what each step measured. We are not arguing that this model is better than anything. We built it, we measured it beside other models, and where we have a guess about why a number moved we say it is a guess. Every arm below was trained…
Open weights
apache-2.0
121M parameters
262,144 tokens
transformers
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 which supports all three retrieval methods, achieving new SOTA on multi-lingual (MIRACL) and cross-lingual (MKQA) benchmarks. Technical…
Open weights
mit
109M parameters
512 tokens
sentence-transformers
Recommend switching to newest BAAI/bge-base-en-v1.5, which has more reasonable similarity distribution and same method of usage. More details please refer to our Github: FlagEmbedding. FlagEmbedding can map any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification, clustering, or semantic search. And it also can be used in vector databases for LLMs. Updates - 10/12/2023: Release LLM-Embedder, a unified embedding model to support diverse retrieval augmentation needs for LLMs. Paper:fire: - 09/15/2023: The technical report of BGE has been released - 09/15/2023: The masive training data of BGE has been released - 09/12/2023: New models: - 09/07/2023…
Open weights
mit
109M parameters
512 tokens
transformers
