Maintainer: nateraw

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Last updated 6/13/2024
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Model overview

The bge-large-en-v1.5 is a text embedding model created by BAAI (Beijing Academy of Artificial Intelligence). It is designed to generate high-quality embeddings for text sequences in English. This model builds upon BAAI's previous work on the bge-reranker-base and multilingual-e5-large models, which have shown strong performance on various language tasks. The bge-large-en-v1.5 model offers enhanced capabilities and is well-suited for a range of natural language processing applications.

Model inputs and outputs

The bge-large-en-v1.5 model takes text sequences as input and generates corresponding embeddings. Users can provide the text either as a path to a file containing JSONL data with a 'text' field, or as a JSON list of strings. The model also accepts a batch size parameter to control the processing of the input data. Additionally, users can choose to normalize the output embeddings and convert the results to a NumPy format.


  • Path: Path to a file containing text as JSONL with a 'text' field or a valid JSON string list.
  • Texts: Text to be embedded, formatted as a JSON list of strings.
  • Batch Size: Batch size to use when processing the text data.
  • Convert To Numpy: Option to return the output as a NumPy file instead of JSON.
  • Normalize Embeddings: Option to normalize the generated embeddings.


  • The model outputs the text embeddings, which can be returned either as a JSON array or as a NumPy file, depending on the user's preference.


The bge-large-en-v1.5 model is capable of generating high-quality text embeddings that capture the semantic and contextual meaning of the input text. These embeddings can be utilized in a wide range of natural language processing tasks, such as text classification, semantic search, and content recommendation. The model's performance has been demonstrated in various benchmarks and real-world applications.

What can I use it for?

The bge-large-en-v1.5 model can be a valuable tool for developers and researchers working on natural language processing projects. The text embeddings generated by the model can be used as input features for downstream machine learning models, enabling more accurate and efficient text-based applications. For example, the embeddings could be used in sentiment analysis, topic modeling, or to power personalized content recommendations.

Things to try

To get the most out of the bge-large-en-v1.5 model, you can experiment with different input text formats, batch sizes, and normalization options to find the configuration that works best for your specific use case. You can also explore how the model's performance compares to other similar models, such as the bge-reranker-base and multilingual-e5-large models, to determine the most suitable approach for your needs.

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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