模型介绍
中文整理The text embedding set trained by Jina AI.
Quick Start
The easiest way to starting using jina-embeddings-v2-base-zh is to use Jina AI's Embedding API.
Intended Usage & Model Info
jina-embeddings-v2-base-zh is a Chinese/English bilingual text **embedding model** supporting **8192 sequence length**.
It is based on a BERT architecture (JinaBERT) that supports the symmetric bidirectional variant of ALiBi to allow longer sequence length.
We have designed it for high performance in mono-lingual & cross-lingual applications and trained it specifically to support mixed Chinese-English input without bias.
Additionally, we provide the following embedding models:
jina-embeddings-v2-base-zh 是支持中英双语的**文本向量**模型，它支持长达**8192字符**的文本编码。
该模型的研发基于BERT架构(JinaBERT)，JinaBERT是在BERT架构基础上的改进，首次将ALiBi应用到编码器架构中以支持更长的序列。
不同于以往的单语言/多语言向量模型，我们设计双语模型来更好的支持单语言（中搜中）以及跨语言（中搜英）文档检索。
除此之外，我们也提供其它向量模型:
jina-embeddings-v2-small-en : 33 million parameters.
jina-embeddings-v2-base-en : 137 million parameters.
jina-embeddings-v2-base-zh : 161 million parameters Chinese-English Bilingual embeddings **(you are here)**.
jina-embeddings-v2-base-de : 161 million parameters German-English Bilingual embeddings.
[ jina-embeddings-v2-base-es ](): Spanish-English Bilingual embeddings (soon).
Data & Parameters
We will publish a report with technical details about the training of the bilingual models soon.
The training of the English model is described in this technical report.
Usage
Please apply mean pooling when integrating the model.**
Why mean pooling?
mean poooling takes all token embeddings from model output and averaging them at sentence/paragraph level.
It has been proved to be the most effective way to produce high-quality sentence embeddings.
We offer an encode function to deal with this.
However, if you would like to do it without using the default encode function:
You can use Jina Embedding models directly from modelscope package:
If you only want to handle shorter sequence, such as 2k, pass the max length parameter to the encode function:
If you want to use the model together with the sentence-transformers package, make sure that you have installed the latest release and set trust remote code=True as well:
Alternatives to Using Transformers Package
- Managed SaaS : Get started with a free key on Jina AI's Embedding API.
- Private and high-performance deployment : Get started by picking from our suite of models and deploy them on AWS Sagemaker.
Use Jina Embeddings for RAG
According to the latest blog post from LLamaIndex,
In summary, to achieve the peak performance in both hit rate and MRR, the combination of OpenAI or JinaAI-Base embeddings with the CohereRerank/bge-reranker-large reranker stands out.
Contact
Join our Discord community and chat with other community members about ideas.
Citation
If you find Jina Embeddings useful in your research, please cite the following paper:
优先采用原作者提供的中文模型卡片；仅有英文说明时由闲社AI翻译整理。重要参数、许可证和商用范围请以原文为准。
你将获得什么
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使用前须知
运行模型需要的设备、工具与依赖，以原作者说明为准。
是否允许商用、修改和分发，请查看模型许可证。
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