mPLUG-DocOwl 1.5
M
Mplug DocOwl 1.5
Overview :
mPLUG-DocOwl 1.5 is a unified structural learning model dedicated to OCR-free document understanding, achieving direct comprehension of documents through deep learning technologies without the need for traditional Optical Character Recognition (OCR). The model can handle various types of images, including documents, web pages, tables, and charts, supporting structural-aware document parsing, multi-granularity text recognition and localization, as well as question-and-answer capabilities. The development of mPLUG-DocOwl 1.5 is driven by the demand for automated and intelligent document understanding, aiming to enhance the efficiency and accuracy of document processing. Its open-source nature also facilitates further research and application in both academia and industry.
Target Users :
The primary target audience consists of enterprises and research institutions that require automated document processing, such as in automated office solutions, document digitization, and intelligent customer service. With its high-precision document parsing and comprehension capabilities, mPLUG-DocOwl 1.5 significantly enhances the efficiency and quality of document handling while reducing the costs associated with manual intervention.
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Use Cases
Businesses can apply mPLUG-DocOwl 1.5 for automated reviews of contract documents, quickly extracting key information.
Educational institutions can use this model to automate the analysis of teaching materials, enhancing the efficiency of resource utilization.
Government agencies can utilize mPLUG-DocOwl 1.5 to process large volumes of public documents, thereby improving public service delivery.
Features
Supports structural-aware document parsing, capable of identifying and understanding structured information within documents.
Facilitates conversion of tables to Markdown and charts to Markdown, promoting reusability of document content.
Offers multi-granularity text recognition and localization, improving the accuracy of document content extraction.
Supports answering both simple phrases and detailed explanatory questions, enhancing the model's interactivity and application range.
Open-source model providing training data, source code, and online demos for ease of use and further development by researchers and developers.
Offers several model versions tailored for different application scenarios, including DocOwl1.5-stage1, DocOwl1.5, DocOwl1.5-Chat, and DocOwl1.5-Omni.
How to Use
1. Set up a Python environment and install necessary dependencies, such as transformers and torch.
2. Download and extract the training datasets provided for mPLUG-DocOwl 1.5, including DocStruct4M and DocReason25K.
3. Choose the appropriate model version based on specific needs, such as DocOwl1.5-stage1 or DocOwl1.5-Chat.
4. Utilize the provided code samples to conduct inference tests on the model, verifying its functionality and performance.
5. If further training or fine-tuning is needed, prepare the training data as per the provided guidelines and run the training script.
6. For users looking to deploy the model, refer to the supplied local demo code to set up your application service.
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