jina-embeddings-code

About

What jina-embeddings-code is

Quick Start The easiest way to starting using jina-embeddings-v2-base-code is to use Jina AI's Embedding API. Intended Usage & Model Info 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 from various domains and were carefully selected through a thorough cleaning process. The embedding model was trained using 512 sequence length, but extrapolates to 8k sequence length (or even longer) thanks to ALiBi. This makes our model useful for a range of use cases, especially when processing long documents is needed, including technical question answering and code search. This model has 161 million parameters, which enables fast and memory efficient inference, while delivering impressive performance. Additionally, we provide the following embedding models: - jina-embeddings-v2-small-en: 33 million parameters. - jina-embeddings-v2-base-en: 137 million parameters. - jina-embeddings-v2-base-zh: Chinese-English Bilingual embeddings. - jina-embeddings-v2-base-de: German-English Bilingual embeddings. - jina-embeddings-v2-base-es: Spanish-English Bilingual embeddings (soon). - jina-embeddings-v2-base-code: 161 million parameters code embeddings. <details<summarySupported (Programming) Languages</summary - English - Assembly - Batchfile - C - C# - C++ - CMake - CSS - Dockerfile - FORTRAN - GO - Haskell - HTML - Java - JavaScript - Julia - Lua - Makefile - Markdown - PHP - Perl - PowerShell - Python - Ruby - Rust - SQL - Scala - Shell - TypeScript - TeX - Visual Basic Data & Parameters Jina Embeddings V2 technical report Usage <details<summaryPlease apply mean pooling when integrating the model.</summary Why mean pooling? mean poooling tak

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How embedding models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys jina-embeddings-code for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (jina-embeddings-code below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/embeddings \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"jina-embeddings-code","input":"text to embed"}'

Details

Languages, data & research

Papers

Licence

Open weights

Sources

Weights & code

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