bge-multilingual-gemma2
Maintainer: BAAI
83
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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 bge-multilingual-gemma2 model is a large language model (LLM) based multilingual embedding model developed by BAAI. It is trained on a diverse range of languages and tasks, building on the google/gemma-2-9b model. The model demonstrates strong performance on multilingual benchmarks like MIRACL, MTEB-pl, and MTEB-fr, as well as major evaluations like MTEB, C-MTEB and AIR-Bench.
Model inputs and outputs
Inputs
- Text: The model accepts text input, which can be used for tasks like retrieval, classification, and clustering.
Outputs
- Text embeddings: The model outputs dense vector representations of the input text, which can be used for downstream applications.
Capabilities
The bge-multilingual-gemma2 model exhibits state-of-the-art performance on a variety of multilingual tasks. It is able to effectively process and represent text in a diverse range of languages, including English, Chinese, Japanese, Korean, and French, among others. The model's capabilities make it well-suited for applications that require cross-lingual understanding and interoperability.
What can I use it for?
The bge-multilingual-gemma2 model can be leveraged for a wide range of natural language processing tasks, such as:
- Multilingual text retrieval: Use the model's embeddings to find relevant passages or documents in different languages for a given query.
- Cross-lingual classification: Classify text in one language based on training data in another language.
- Multilingual semantic similarity: Identify semantically similar text across languages.
- Multilingual clustering: Group text documents in different languages based on their semantic content.
By taking advantage of the model's strong multilingual capabilities, you can build applications that seamlessly handle text in multiple languages, opening up new possibilities for global reach and user experiences.
Things to try
One interesting aspect of the bge-multilingual-gemma2 model is its ability to perform well without the need for explicit instruction during inference. While adding instruction to queries can provide a slight boost in retrieval performance, the model is able to generate useful embeddings even without the instruction, making it more convenient to use in certain scenarios. Experiment with using the model both with and without instruction to see which approach works best for your specific use case.
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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