mxbai-rerank-large-v1
Maintainer: mixedbread-ai
69
✅
| 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 mxbai-rerank-large-v1 model is the largest in the family of powerful reranker models created by mixedbread ai. This model can be used to rerank a set of documents based on a given query. The model is part of a suite of three reranker models:
Model inputs and outputs
Inputs
- Query: A natural language query for which you want to rerank a set of documents.
- Documents: A list of text documents that you want to rerank based on the given query.
Outputs
- Relevance scores: The model outputs relevance scores for each document in the input list, indicating how well each document matches the given query.
Capabilities
The mxbai-rerank-large-v1 model can be used to improve the ranking of documents retrieved by a search engine or other text retrieval system. By taking a query and a set of candidate documents, the model can re-order the documents to surface the most relevant ones at the top of the list.
What can I use it for?
You can use the mxbai-rerank-large-v1 model to build robust search and retrieval systems. For example, you could use it to power the search functionality of a content-rich website, helping users quickly find the most relevant information. It could also be integrated into chatbots or virtual assistants to improve their ability to understand user queries and surface the most helpful responses.
Things to try
One interesting thing to try with the mxbai-rerank-large-v1 model is to experiment with different types of queries. While it is designed to work well with natural language queries, you could also try feeding it more structured or keyword-based queries to see how the reranking results differ. Additionally, you could try varying the size of the input document set to understand how the model's performance scales with the number of items it needs to rerank.
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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