colpali
Maintainer: vidore
172
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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
colpali is a novel model architecture and training strategy based on Vision Language Models (VLMs) to efficiently index documents from their visual features. It is an extension of the PaliGemma-3B model that generates ColBERT-style multi-vector representations of text and images. Developed by vidore, ColPali was introduced in the paper ColPali: Efficient Document Retrieval with Vision Language Models.
Model inputs and outputs
Inputs
- Images and text documents
Outputs
- Ranked list of relevant documents for a given query
- Efficient document retrieval using ColBERT-style multi-vector representations
Capabilities
ColPali is designed to enable fast and accurate retrieval of documents based on their visual and textual content. By generating ColBERT-style representations, it can efficiently match queries to relevant passages, outperforming earlier BiPali models that only used text-based representations.
What can I use it for?
The ColPali model can be used for a variety of document retrieval and search tasks, such as finding relevant research papers, product information, or news articles based on a user's query. Its ability to leverage both visual and textual content makes it particularly useful for tasks that involve mixed media, like retrieving relevant documents for a given image.
Things to try
One interesting aspect of ColPali is its use of the PaliGemma-3B language model as a starting point. By finetuning this off-the-shelf model and incorporating ColBERT-style multi-vector representations, the researchers were able to create a powerful retrieval system. This suggests that similar techniques could be applied to other large language models to create specialized retrieval systems for different domains or use cases.
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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