jina-embeddings-v2-base-zh

Maintainer: jinaai

Total Score

121

Last updated 5/27/2024

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PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model Overview

The jina-embeddings-v2-base-zh model is a Chinese/English bilingual text embedding model developed by Jina AI. It is based on a BERT architecture (JinaBERT) that supports the symmetric bidirectional variant of ALiBi to allow longer sequence lengths of up to 8192 tokens. Compared to other Jina embedding models, jina-embeddings-v2-base-zh is a 161 million parameter model trained specifically on mixed Chinese-English input to provide high performance in both mono-lingual and cross-lingual applications.

Similar Jina AI embedding models include [object Object], [object Object], [object Object], and an upcoming jina-embeddings-v2-base-es model for Spanish-English bilingual embeddings.

Model Inputs and Outputs

Inputs

  • Text sequence: The model takes in text sequences of up to 8192 tokens, supporting both Chinese and English, as well as a mix of the two.

Outputs

  • Text embeddings: The model outputs 768-dimensional embedding vectors that capture the semantic meaning of the input text. These can be used for a variety of downstream tasks like information retrieval, text similarity, and multilingual applications.

Capabilities

The jina-embeddings-v2-base-zh model has been designed to excel at both mono-lingual and cross-lingual tasks involving Chinese and English text. Its long sequence length support of up to 8192 tokens makes it useful for applications that need to process long-form content, such as document retrieval, semantic textual similarity, and text reranking.

What Can I Use It For?

The jina-embeddings-v2-base-zh model can be used for a wide range of natural language processing tasks that require high-quality text embeddings, especially those involving a mix of Chinese and English text. Some potential use cases include:

  • Information Retrieval: Use the embeddings for semantic search and retrieval of Chinese or English documents, or documents containing a mix of both languages.
  • Text Similarity: Compute the similarity between Chinese, English, or bilingual text passages to detect paraphrases, identify related content, or perform clustering.
  • Multilingual Applications: Leverage the model's cross-lingual capabilities to build applications that seamlessly handle Chinese and English input, such as chatbots or question-answering systems.

Things to Try

An interesting aspect of the jina-embeddings-v2-base-zh model is its ability to handle long input sequences of up to 8192 tokens. This makes it well-suited for tasks involving lengthy documents or multi-paragraph inputs. You could experiment with using the model for tasks like:

  • Long-form text summarization, where the model's ability to capture semantic meaning in long passages could improve the quality of generated summaries.
  • Cross-lingual document retrieval, where the model's bilingual capabilities and long sequence support could help surface relevant content even when the query and target documents are in different languages.
  • Multilingual dialog systems, where the model's embeddings could be used to maintain context and coherence across language switches within a conversation.

By exploring the model's unique features, you can uncover novel applications that leverage its strengths in handling long, multilingual text inputs.



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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