gte-large-en-v1.5

Maintainer: Alibaba-NLP

Total Score

80

Last updated 5/30/2024

🛠️

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 gte-large-en-v1.5 is a state-of-the-art text embedding model developed by Alibaba-NLP. It is part of the GTE (General Text Embeddings) model series, which are based on the BERT framework and trained on a large-scale corpus of relevant text pairs. This enables the GTE models to perform well on a variety of downstream tasks like information retrieval, semantic textual similarity, and text reranking.

The gte-large-en-v1.5 model in particular achieves high scores on the MTEB benchmark, outperforming other popular text embedding models in the same size category. It also performs competitively on the LoCo long-context retrieval tests. Alibaba-NLP has also released other GTE models, including the gte-large-zh for Chinese text and the gte-small and gte-base for English.

Model Inputs and Outputs

The gte-large-en-v1.5 model takes in text inputs and generates dense vector representations, also known as text embeddings. These embeddings can capture the semantic meaning of the input text, allowing them to be used in a variety of downstream NLP tasks.

Inputs

  • Text data, up to 8192 tokens in length

Outputs

  • 1024-dimensional text embeddings for each input

Capabilities

The gte-large-en-v1.5 model is particularly adept at tasks that involve understanding the semantic relationship between text, such as information retrieval, text ranking, and semantic textual similarity. For example, it can be used to find relevant documents for a given query, or to identify similar paragraphs or sentences across a corpus.

What Can I Use It For?

The gte-large-en-v1.5 model can be a powerful tool for a variety of NLP applications. Some potential use cases include:

  • Information retrieval: Use the model to find the most relevant documents or web pages for a given query.
  • Semantic search: Leverage the model's ability to understand text semantics to build advanced search engines.
  • Text ranking: Apply the model to rank and order text data, such as search results or recommendation lists.
  • Text summarization: Combine the model with other techniques to generate concise summaries of longer text.

Things to Try

One key advantage of the gte-large-en-v1.5 model is its ability to handle long-form text inputs, up to 8192 tokens. This makes it well-suited for tasks that involve analyzing and processing lengthy documents or passages. Try experimenting with the model on tasks that require understanding the overall meaning and context of longer text, rather than just individual sentences or short snippets.

You can also explore how the gte-large-en-v1.5 model compares to other text embedding models, such as the gte-small or gte-base, in terms of performance on your specific use cases. The tradeoffs between model size, speed, and accuracy may vary depending on your requirements.



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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