Impira
Models by this creator
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layoutlm-document-qa
857
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.
Updated 5/28/2024
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layoutlm-invoices
139
The layoutlm-invoices model is a fine-tuned version of the multi-modal LayoutLM model for the task of question answering on invoices and other documents. It has been fine-tuned on a proprietary dataset of invoices as well as both SQuAD2.0 and DocVQA for general comprehension. Unlike other QA models that can only extract consecutive tokens, this model can predict longer-range, non-consecutive sequences with an additional classifier head. This allows it to correctly identify multi-line addresses and other non-contiguous answers. Model inputs and outputs Inputs Text and image data**: The layoutlm-invoices model takes both text and image data as inputs, allowing it to understand the layout and visual context of documents like invoices. Outputs Question-answering**: The primary output of the layoutlm-invoices model is an answer to a given question about the input document. It can extract both consecutive and non-consecutive token sequences as answers. Capabilities The layoutlm-invoices model excels at understanding the layout and content of documents like invoices, and can answer questions that require comprehending the visual and textual information. Its ability to extract non-consecutive token sequences as answers sets it apart from other QA models, making it better suited for tasks where the relevant information is spread across multiple locations in the document. What can I use it for? The layoutlm-invoices model is well-suited for automating document understanding tasks, such as extracting key information from invoices, receipts, and other business documents. It can be used to build intelligent document processing systems that can quickly and accurately answer questions about the content and layout of these documents. This can help streamline workflows, reduce manual effort, and improve the efficiency of document-heavy business processes. Things to try One interesting aspect of the layoutlm-invoices model is its ability to handle non-consecutive token sequences as answers. This can be particularly useful for extracting information like addresses or other multi-part entities from documents. Try experimenting with questions that require understanding the visual and spatial layout of the document, and see how the model performs compared to more traditional QA models.
Updated 5/28/2024