LayoutLMV2 Fine-tuned FUNSD
| Property | Value |
|---|---|
| Base Model | microsoft/layoutlmv2-base-uncased |
| Framework | PyTorch 1.8.0 |
| Hugging Face | Model Repository |
What is layoutlmv2-finetuned-funsd?
This is a specialized version of Microsoft's LayoutLMV2 model, fine-tuned specifically for the FUNSD (Form Understanding in Noisy Scanned Documents) dataset. The model combines text, layout, and visual information to understand and extract information from document images.
Implementation Details
The model was trained using carefully selected hyperparameters including a learning rate of 5e-05, batch sizes of 8 for both training and evaluation, and implements Native AMP for mixed precision training. The training process ran for 1000 steps using the Adam optimizer with betas=(0.9,0.999) and epsilon=1e-08.
- Utilizes linear learning rate scheduler
- Implements seed 42 for reproducibility
- Built on Transformers 4.9.0.dev0
- Uses Datasets 1.9.0 and Tokenizers 0.10.3
Core Capabilities
- Document layout analysis
- Form understanding and information extraction
- Text and visual feature integration
- Handling of scanned document processing
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized fine-tuning on the FUNSD dataset, making it particularly effective for form understanding tasks. It leverages the powerful LayoutLMV2 architecture while being optimized for real-world document processing applications.
Q: What are the recommended use cases?
The model is best suited for tasks involving form understanding, document layout analysis, and information extraction from scanned documents. It's particularly useful for processing structured documents where both textual content and spatial layout are important.
