bge-reranker-v2-m3
Maintainer: yxzwayne
57
🛠️
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
bge-reranker-v2-m3 is the newest balance-striking reranker model from BAAI. It outputs rank scores for query-doc pairs and has FP16 inference enabled. This model can be compared to similar query-document ranking models like qwen1.5-110b and reliberate-v3.
Model inputs and outputs
The bge-reranker-v2-m3 model takes a JSON string as input, which can be a list containing one query and one passage pair, or a list of such pairs. The output is an array.
Inputs
- Input List: A JSON string containing one or more query-passage pairs.
Outputs
- Output: An array containing the output of the model.
Capabilities
The bge-reranker-v2-m3 model can be used to rank query-document pairs, which is useful for a variety of applications such as search, question answering, and information retrieval.
What can I use it for?
The bge-reranker-v2-m3 model can be used for a variety of applications that involve ranking text-based content, such as web search, recommendation systems, and content moderation. For example, you could use this model to improve the relevance of search results on your website or to automatically filter out low-quality or inappropriate content.
Things to try
One interesting thing to try with the bge-reranker-v2-m3 model is to experiment with different types of query-document pairs and observe how the model's ranking scores change. You could also try combining this model with other natural language processing models, such as real-esrgan or absolutereality-v1.8.1, to create more sophisticated content ranking and recommendation systems.
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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🛠️
bge-reranker-v2-m3
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The bge-reranker-v2-m3 model is a lightweight reranker model from BAAI that possesses strong multilingual capabilities. It is built on top of the bge-m3 base model, which is a versatile AI model that can simultaneously perform dense retrieval, multi-vector retrieval, and sparse retrieval. The bge-reranker-v2-m3 model is easy to deploy and provides fast inference, making it suitable for a variety of multilingual contexts. Model inputs and outputs The bge-reranker-v2-m3 model takes as input a query and a passage, and outputs a relevance score that indicates how relevant the passage is to the query. The relevance score is not bounded to a specific range, as the model is optimized based on cross-entropy loss. This allows for more fine-grained ranking of passages compared to models that output similarity scores bounded between 0 and 1. Inputs Query**: The text of the query to be evaluated Passage**: The text of the passage to be evaluated for relevance to the query Outputs Relevance score**: A float value representing the relevance of the passage to the query, with higher scores indicating more relevance. Capabilities The bge-reranker-v2-m3 model is designed to be a powerful and efficient reranker for multilingual contexts. It can be used to rerank the top-k documents retrieved by an embedding model, such as the bge-m3 model, to further improve the relevance of the final results. What can I use it for? The bge-reranker-v2-m3 model is well-suited for a variety of multilingual information retrieval and question-answering tasks. It can be used to rerank results from a search engine, to filter and sort documents for research or analysis, or to improve the relevance of responses in a multilingual chatbot or virtual assistant. Its fast inference and strong multilingual capabilities make it a versatile tool for building language-agnostic applications. Things to try One interesting aspect of the bge-reranker-v2-m3 model is its ability to output relevance scores that are not bounded between 0 and 1. This allows for more nuanced ranking of passages, which could be particularly useful in applications where small differences in relevance are important. Developers could experiment with using these unbounded scores to improve the precision of their retrieval systems, or to surface more contextually relevant information to users. Another interesting thing to try would be to combine the bge-reranker-v2-m3 model with the bge-m3 model in a hybrid retrieval pipeline. By using the bge-m3 model for initial dense retrieval and the bge-reranker-v2-m3 model for reranking, you could potentially achieve higher accuracy and better performance across a range of multilingual use cases.
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