Nougat-base
| Property | Value |
|---|---|
| Parameter Count | 349M |
| License | CC-BY-NC-4.0 |
| Paper | View Paper |
| Architecture | Vision-encoder-decoder (Swin Transformer + mBART) |
What is nougat-base?
Nougat-base is a sophisticated AI model designed to bridge the gap between PDF documents and machine-readable text. Developed by Facebook Research, this model specializes in converting academic PDFs into a clean, structured markdown format. It leverages a hybrid architecture combining visual understanding with text generation capabilities.
Implementation Details
The model employs a two-stage architecture: a Swin Transformer for visual encoding and an mBART model for text decoding. This combination allows it to process PDF documents at the pixel level and generate corresponding markdown output through autoregressive prediction.
- Vision Encoder: Utilizes Swin Transformer architecture for processing document images
- Text Decoder: Implements mBART for generating structured markdown output
- Training Focus: Specialized in academic document understanding
Core Capabilities
- PDF-to-markdown conversion with high accuracy
- Academic document structure understanding
- Support for complex scientific content
- Automated document formatting
Frequently Asked Questions
Q: What makes this model unique?
Nougat-base stands out for its specialized focus on academic document processing and its ability to maintain document structure while converting from PDF to markdown format. The combination of visual and textual processing makes it particularly effective for handling complex scientific documents.
Q: What are the recommended use cases?
The model is ideal for researchers and academics who need to convert scientific papers from PDF to markdown format, making content more accessible and easier to process programmatically. It's particularly useful for building digital libraries and document processing pipelines.
