embeddings-gte-base

Maintainer: mark3labs

Total Score

867

Last updated 5/17/2024

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

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Model overview

The embeddings-gte-base model is a General Text Embeddings (GTE) model developed by Alibaba DAMO Academy. It is based on the BERT framework and is part of a family of GTE models that also include the GTE-large and GTE-small versions. The GTE models are trained on a large-scale corpus of relevant text pairs, enabling them to be applied to various downstream tasks like information retrieval, semantic textual similarity, and text reranking.

Compared to other popular text embedding models like bge-large-en-v1.5 and gte-large-zh, the embeddings-gte-base model offers a balance between performance and model size, with a dimension of 768 and a model size of 0.22GB.

Model inputs and outputs

Inputs

  • text: A string containing the text to be embedded.

Outputs

  • text: The input text string.
  • vectors: An array of floating-point numbers representing the text embedding.

Capabilities

The embeddings-gte-base model is capable of generating high-quality text embeddings that can be used for a variety of natural language processing tasks. Based on the provided metrics, the model performs well on a range of benchmarks, including information retrieval, semantic textual similarity, and text reranking.

What can I use it for?

The embeddings-gte-base model can be used for a variety of applications that require text embedding, such as:

  • Information retrieval: The model can be used to embed queries and documents, enabling efficient retrieval of relevant information.
  • Semantic textual similarity: The model can be used to compute the similarity between text segments, which is useful for applications like document clustering and recommendation.
  • Text reranking: The model can be used to rerank the results of a search query, improving the relevance of the top results.

Things to try

One interesting thing to try with the embeddings-gte-base model is to explore how it performs on different types of text data, such as short queries, long-form articles, or specialized domain-specific content. By analyzing the model's performance across various use cases, you can gain insights into its strengths and limitations, and potentially identify opportunities for further model refinement or customization.



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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