mxbai-colbert-large-v1
Maintainer: mixedbread-ai
49
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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 mxbai-colbert-large-v1 model is the first English ColBERT model from Mixedbread, built upon their sentence embedding model mixedbread-ai/mxbai-embed-large-v1. ColBERT is an efficient and effective passage retrieval model that uses fine-grained contextual late interaction to score the similarity between a query and a passage. It encodes each passage into a matrix of token-level embeddings, allowing it to surpass the quality of single-vector representation models while scaling efficiently to large corpora.
Model inputs and outputs
Inputs
- Text: The model takes text as input, which can be queries or passages.
Outputs
- Ranking: The model outputs a ranking of passages for a given query, along with relevance scores for each passage.
Capabilities
The mxbai-colbert-large-v1 model can be used for efficient and accurate passage retrieval. It excels at finding relevant passages from large text collections, outperforming traditional keyword-based search and semantic search models in many cases.
What can I use it for?
You can use the mxbai-colbert-large-v1 model for a variety of text-based retrieval tasks, such as:
- Search engines: Integrate the model into a search engine to provide more relevant and accurate results.
- Question answering: Use the model to retrieve relevant passages for answering questions.
- Recommendation systems: Leverage the model's passage ranking capabilities to provide personalized recommendations.
Things to try
One interesting thing to try with the mxbai-colbert-large-v1 model is to combine it with other approaches, such as keyword-based search or semantic search. By using a hybrid approach that leverages the strengths of multiple techniques, you may be able to achieve even better retrieval performance.
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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