multilingual-e5-large

Maintainer: beautyyuyanli

Total Score

4.6K

Last updated 6/13/2024

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Model overview

The multilingual-e5-large is a multi-language text embedding model developed by beautyyuyanli. This model is similar to other large language models like qwen1.5-72b, llava-13b, qwen1.5-110b, uform-gen, and cog-a1111-ui, which aim to provide large-scale language understanding capabilities across multiple languages.

Model inputs and outputs

The multilingual-e5-large model takes text data as input and generates embeddings, which are numerical representations of the input text. The input text can be provided as a JSON list of strings, and the model also accepts parameters for batch size and whether to normalize the output embeddings.

Inputs

  • texts: Text to embed, formatted as a JSON list of strings (e.g. ["In the water, fish are swimming.", "Fish swim in the water.", "A book lies open on the table."])
  • batch_size: Batch size to use when processing text data (default is 32)
  • normalize_embeddings: Whether to normalize the output embeddings (default is true)

Outputs

  • An array of arrays, where each inner array represents the embedding for the corresponding input text.

Capabilities

The multilingual-e5-large model is capable of generating high-quality text embeddings for a wide range of languages, making it a useful tool for various natural language processing tasks such as text classification, semantic search, and data analysis.

What can I use it for?

The multilingual-e5-large model can be used in a variety of applications that require text embeddings, such as building multilingual search engines, recommendation systems, or language translation tools. By leveraging the model's ability to generate embeddings for multiple languages, developers can create more inclusive and accessible applications that serve a global audience.

Things to try

One interesting thing to try with the multilingual-e5-large model is to explore how the generated embeddings capture the semantic relationships between words and phrases across different languages. You could experiment with using the embeddings for cross-lingual text similarity or clustering tasks, which could provide valuable insights into the model's language understanding capabilities.



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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