layoutlm-document-qa
Maintainer: impira
857
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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 layoutlm-document-qa model is a fine-tuned version of the multi-modal LayoutLM model, created by the team at Impira. It has been fine-tuned for the task of question answering on documents, using both the SQuAD2.0 and DocVQA datasets.
Another similar model created by Impira is the layoutlm-invoices model, which is also a fine-tuned version of LayoutLM, but specifically for question answering on invoices and other documents.
Model inputs and outputs
Inputs
- Image: The model takes an image of a document as input.
- Question: The model also takes a natural language question about the document as input.
Outputs
- Answer: The model outputs the answer to the given question, along with a confidence score.
- Start and end positions: The model also outputs the start and end positions of the answer within the document.
Capabilities
The layoutlm-document-qa model is capable of answering questions about the content and layout of documents, even when the answer is non-consecutive or spans multiple locations in the document. This is in contrast to other question-answering models that can only extract consecutive tokens.
For example, the model can correctly identify the address in an invoice, even when it is split across multiple lines.
What can I use it for?
The layoutlm-document-qa model can be used for a variety of document-related tasks, such as:
- Automating the process of extracting information from invoices, receipts, and other business documents.
- Enhancing document search and retrieval systems by allowing users to ask natural language questions about document contents.
- Improving document understanding and comprehension for tasks like legal document analysis and medical record processing.
Things to try
One interesting aspect of the layoutlm-document-qa model is its ability to handle non-consecutive tokens in the answer. This can be particularly useful when dealing with documents that have complex layouts or formatting. You could try experimenting with different types of documents, such as forms, tables, or mixed-content pages, to see how the model performs.
Additionally, you could explore fine-tuning the model further on your own specialized document datasets to see if you can improve its performance on your specific use case.
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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