gte-multilingual-base
Maintainer: Alibaba-NLP
84
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The gte-multilingual-base model is the latest in the GTE (General Text Embedding) family of models from Alibaba-NLP. It achieves state-of-the-art results in multilingual retrieval tasks and multi-task representation model evaluations compared to models of similar size. Unlike previous GTE models based on decode-only LLM architecture (e.g., gte-qwen2-1.5b-instruct), this encoder-only transformers model has lower hardware requirements for inference, offering a 10x increase in speed. It supports text lengths up to 8192 tokens and over 70 languages.
Model inputs and outputs
The gte-multilingual-base model takes in text as input and outputs dense embeddings. It can also generate sparse vectors in addition to the dense representations. The elastic dense embedding output helps reduce storage costs and improve execution efficiency while maintaining effectiveness on downstream tasks.
Inputs
- Text sequences up to 8192 tokens in length
Outputs
- Dense vector embeddings of size 768
- Sparse vector embeddings
Capabilities
The gte-multilingual-base model excels at multilingual text retrieval and representation tasks. It achieves state-of-the-art performance on the MTEB benchmark compared to models of similar size. The model's ability to handle long-form text up to 8192 tokens makes it suitable for applications that require processing lengthy documents or passages.
What can I use it for?
The gte-multilingual-base model is well-suited for a variety of text-based applications that require effective cross-lingual representations, such as:
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Multilingual information retrieval: The model's high performance on multilingual retrieval tasks makes it useful for building search engines or recommender systems that need to handle queries and documents in multiple languages.
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Semantic text similarity: The model's dense embeddings can be used to measure the semantic similarity between text, enabling applications like paraphrase detection, document clustering, or content-based recommendation.
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Text reranking: The model's effectiveness on reranking tasks makes it applicable for improving the ranking of search results or other text-based content.
Things to try
One interesting aspect of the gte-multilingual-base model is its ability to generate sparse vector embeddings in addition to the dense representations. Sparse vectors can be more efficient to store and transmit, which could be beneficial for applications with storage or bandwidth constraints. Exploring the use of the sparse embeddings and comparing their performance to the dense ones could yield valuable insights.
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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