jina-embeddings-v2-base-code
Maintainer: jinaai
57
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The jina-embeddings-v2-base-code model is a multilingual text embedding model trained by Jina AI. It supports 8192 sequence length and can encode text in English and 30 widely used programming languages. The model is based on a BERT architecture (JinaBERT) that uses the symmetric bidirectional variant of ALiBi to allow for longer sequence length.
The backbone jina-bert-v2-base-code model was pretrained on the github-code dataset. It was then further trained on Jina AI's collection of over 150 million coding question-answer and docstring source code pairs from various domains. This allows the model to effectively encode code and technical language.
Jina AI also provides several other embedding models that differ in size and language support, including [object Object], [object Object], [object Object], [object Object], and jina-embeddings-v2-base-es.
Model inputs and outputs
Inputs
- Text: The model can take English or programming language text as input, with a maximum sequence length of 8192 tokens.
Outputs
- Text embeddings: The model outputs 384-dimensional dense vector representations that capture the semantic meaning of the input text. These embeddings can be used for a variety of downstream tasks like semantic search, text ranking, and question answering.
Capabilities
The jina-embeddings-v2-base-code model is particularly well-suited for applications that require encoding long-form technical text, such as programming code, documentation, and scientific literature. Its ability to handle sequences up to 8192 tokens makes it useful for tasks that involve processing entire documents or passages, rather than just short sentences or paragraphs.
What can I use it for?
The jina-embeddings-v2-base-code model can be used for a variety of applications, including:
- Code search and retrieval: The model can be used to encode code snippets or docstrings and perform semantic search to find relevant code examples.
- Technical question answering: The model's ability to encode long-form technical text can be leveraged to build systems that can answer complex questions by retrieving and understanding relevant information from a knowledge base.
- Documentation summarization: The model's embeddings can be used to identify the most salient parts of long technical documents, enabling efficient summarization.
- Recommendation systems: The model's embeddings can be used to find related programming resources, such as libraries, frameworks, or online tutorials, based on a user's interests or the context of their current project.
Things to try
One interesting aspect of the jina-embeddings-v2-base-code model is its ability to handle longer sequences of text, up to 8192 tokens. This makes it well-suited for tasks that involve processing entire documents or code repositories, rather than just individual sentences or paragraphs.
For example, you could try using the model to encode a large codebase and then perform semantic search to find relevant code snippets for a specific programming task. Or you could use the model to generate embeddings for technical papers or blog posts, and then use those embeddings to power a recommendation system that suggests related content to users.
Another potential application is using the model's embeddings as input to a language model or question-answering system, to build a more powerful technical assistant that can understand and respond to complex queries about programming, software engineering, or scientific topics.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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