bge-base-en

Maintainer: BAAI

Total Score

53

Last updated 6/17/2024

📈

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 bge-base-en is a text embedding model developed by the BAAI (Beijing Academy of Artificial Intelligence) that can map any text to a low-dimensional dense vector. It is part of the BAAI/bge-base-en model series, which also includes larger and smaller scale versions such as BAAI/bge-large-en and BAAI/bge-small-en. These models were trained using contrastive learning on a massive text corpus and demonstrate state-of-the-art performance on text embedding benchmarks like MTEB and C-MTEB.

The bge-base-en model is a base-scale version that achieves similar performance to the larger bge-large-en model, making it a good option for applications with limited compute resources. All models in the BAAI/bge series have been recommended to use the newest v1.5 versions, which have an improved similarity distribution compared to earlier versions.

Model inputs and outputs

Inputs

  • Text: The bge-base-en model can take any text as input, such as a sentence, paragraph, or document.
  • Instruction (optional): For text retrieval tasks, the input text can optionally be prefixed with an instruction to improve performance, such as "Represent this sentence for searching relevant passages:".

Outputs

  • Embedding vector: The model outputs a fixed-size dense vector representation of the input text, which can be used for downstream tasks like retrieval, classification, clustering, or semantic search.

Capabilities

The bge-base-en model is a powerful text embedding model that can capture the semantic meaning of input text in a compact vector representation. It has been shown to excel at a variety of NLP tasks, achieving top performance on the MTEB and C-MTEB benchmarks.

Some key capabilities of the model include:

  • Retrieval: The embedding vectors can be used to efficiently search large text corpora to find relevant documents or passages for a given query.
  • Classification: The embeddings can be leveraged as features for training classifiers on text data.
  • Clustering: The vector representations allow for effective grouping of similar text items.
  • Semantic search: The model can identify semantically related texts based on the proximity of their embedding vectors.

What can I use it for?

The bge-base-en model is a highly versatile tool that can be applied to a wide range of NLP applications. Some potential use cases include:

  • Intelligent search: Integrating the model into search engines or knowledge bases to enable more accurate and semantically-aware retrieval of information.
  • Recommender systems: Using the text embeddings to identify related content or products for recommendation.
  • Content analysis: Leveraging the model's ability to capture semantic meaning for tasks like topic modeling, sentiment analysis, or text summarization.
  • Multimodal applications: Combining the text embeddings with visual or audio representations for applications like image/video captioning or multimedia search.

Things to try

One interesting aspect of the bge-base-en model is its ability to generate high-quality embeddings without requiring an instruction prefix, while still maintaining strong retrieval performance. This makes the model convenient to use in many scenarios where adding an instruction may not be practical.

Another thing to explore is fine-tuning the model on your own data using the provided examples. By incorporating domain-specific knowledge, you can further improve the model's performance on tasks relevant to your application. The FlagEmbedding library provides guidance on how to effectively fine-tune the bge models.

Finally, you can experiment with using the bge-base-en model in combination with the larger bge-large-en model or the bge-reranker models to further enhance retrieval performance. The reranker models can be used to re-rank the top results from the embedding model, providing a more accurate relevance score.



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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