Data Summary for microsoft_udop-large
This was data summary created by Microsoft on behalf of the developer and may contain mistakes
1. General information
1.0.1 Version of the Summary: 1.0
1.0.2 Last update: 2-Dec-2025
1.1 Model Developer Identification
1.1.1 Model Developer name and contact details: Microsoft Corporation at One Microsoft Way, Redmond, WA 98052. Tel: 425-882-8080
1.2 Model Identification
1.2.1 Versioned model name(s): udop-large, udop-large-512, udop-large-512-300k
1.2.2 Model release date: 26-Feb-2024
1.3 Overall training data size and characteristics
1.3.1 Size of dataset and characteristics
1.3.1.A Text training data size: 1 billion to 10 trillion tokens
1.3.1.B Text training data content: OCR text extracted from scanned documents, including diverse document types such as invoices, forms, letters, receipts, academic papers, and web-like pages with figures, tables, and varied layouts
1.3.1.C Image training data size: 1 million to 10 billion tokens
1.3.1.D Image training data content: Scanned document images, including diverse document types such as invoices, forms, letters, receipts, academic papers, and web-like pages with figures, tables, and varied layouts
1.3.1.E Audio training data size: Not applicable. Audio is not part of the training data
1.3.1.F Audio training data content: Not applicable
1.3.1.G Video training data size: Not applicable. Video data is not part of the training data
1.3.1.H Video training data content: Not applicable
1.3.1.I Other training data size: Not applicable
1.3.1.J Other training data content: Not applicable
1.3.2 Latest date of data acquisition/collection for model training: This information cannot be provided due to unavailability of the underlying data (e.g., loss, corruption, or other access limitations)
1.3.3 Is data collection ongoing to update the model with new data collection after deployment? No
1.3.4 Date the training dataset was first used to train the model:
1.3.5 Rationale or purpose of data selection: Large-scale scanned document images with OCR text and layout provide diverse real-world document structures to learn unified vision-text-layout representations. Incorporating multiple supervised datasets across classification, layout analysis, information extraction, question answering, and NLI supports the model’s intended use for universal document processing tasks and improves performance across varied domains
2. List of data sources
2.1 Publicly available datasets
2.1.1 Have you used publicly available datasets to train the model? Yes
2.2 Private non-publicly available datasets obtained from third parties
2.2.1 Datasets commercially licensed by rights holders or their representatives
2.2.1.A Have you concluded transactional commercial licensing agreement(s) with rights holder(s) or with their representatives? Not applicable
2.2.2 Private datasets obtained from other third-parties
2.2.2.A Have you obtained private datasets from third parties that are not licensed as described in Section 2.2.1, such as data obtained from providers of private databases, or data intermediaries? No
2.3 Personal Information
2.3.1 Was personal data used to train the model? Microsoft follows all relevant laws and regulations pertaining to personal information
2.4 Synthetic data
2.4.1 Was any synthetic AI-generated data used to train the model? No
3. Data processing aspects
3.1 Respect of reservation of rights from text and data mining exception or limitation
3.1.1 Does this dataset include any data protected by copyright, trademark, or patent? Microsoft follows all required regulations and laws for processing data protected by copyright, trademark, or patent
3.2 Other information
3.2.1 Does the dataset include information about consumer groups without revealing individual consumer identities? Microsoft follows all required regulations and laws for protecting consumer identities
3.2.2 Was the dataset cleaned or modified before model training? Yes