Leverage the power of the mPLUG-Owl document understanding model to ask questions about your documents
This article will discuss the Alibaba document understanding model, recently released with model weights and datasets. It is a powerful model capable of performing various tasks such as document question answering, extracting information, and document embedding, making it a helpful tool when working with documents. This article will implement the model locally and test it out on different tasks to give an opinion on its performance and usefulness.
· Motivation
· Tasks
· Running the model locally
· Testing of the model
∘ Data
∘ Testing the first, leftmost receipt:
∘ Testing the second, rightmost receipt:
∘ Testing the first, leftmost lecture note:
∘ Testing the second, rightmost lecture note
· My thoughts on the model
· Conclusion
My motivation for this article is to test out the latest machine-learning models that are publicly available. This model caught my attention since I have worked and am still working on machine learning applied to documents. I have also previously written an article on my work with a similar model called Donut that does OCR-free document understanding. I think the concept of having a document and asking visual and textual questions about it is awesome, so I spend time working with documents, understanding models, and testing their performance. This article is the second article in my series on testing out the latest machine-learning models, and you can read my first article on time series forecasting with Chronos below: