LILT-XLM-RoBERTa-Base
| Property | Value |
|---|---|
| Parameter Count | 284M |
| License | MIT |
| Languages Supported | 94 |
| Framework | PyTorch |
| Model Type | Feature Extraction |
What is lilt-xlm-roberta-base?
LILT-XLM-RoBERTa-Base is an innovative multilingual document understanding model that combines the Language-Independent Layout Transformer (LILT) with XLM-RoBERTa architecture. This combination creates a powerful LayoutLM-like model capable of processing documents in 94 different languages, making it a versatile solution for global document analysis tasks.
Implementation Details
The model leverages the multilingual capabilities of XLM-RoBERTa, which was trained on 100 languages, and integrates it with LILT's layout understanding capabilities. It uses safetensors for efficient model weight storage and supports both I64 and F32 tensor types.
- Built on XLM-RoBERTa architecture
- Implements Language-Independent Layout Transformer methodology
- Supports PyTorch framework
- Available through Inference Endpoints
Core Capabilities
- Multilingual document understanding across 94 languages
- Layout-aware text processing
- Feature extraction for downstream tasks
- Cross-lingual document analysis
- Support for both left-to-right and right-to-left languages
Frequently Asked Questions
Q: What makes this model unique?
This model uniquely combines layout understanding with multilingual capabilities, making it one of the few models that can process document layouts across 94 different languages, including low-resource languages.
Q: What are the recommended use cases?
The model is ideal for document understanding tasks requiring layout awareness across multiple languages, such as multilingual form processing, document classification, and information extraction from structured documents in various languages.
