flan-t5-base-VG-factual-sg
| Property | Value |
|---|---|
| Parameter Count | 248M parameters |
| Model Type | Text-to-Text Generation |
| Architecture | FLAN-T5 Base |
| Tensor Format | F32 |
| Paper | FACTUAL: A Benchmark for Faithful and Consistent Textual Scene Graph Parsing |
What is flan-t5-base-VG-factual-sg?
This is a specialized variant of the FLAN-T5 base model, specifically designed for scene graph parsing tasks. The model undergoes a two-stage training process: first pre-training on the Visual Genome (VG) scene graph parsing dataset, followed by fine-tuning on the FACTUAL scene graph parsing dataset. This approach combines the robust foundation of FLAN-T5 with specialized training for improved scene graph understanding.
Implementation Details
The model builds upon the FLAN-T5 base architecture, incorporating 248M parameters and utilizing F32 tensor format for precise computations. It's implemented using PyTorch and supports text-generation-inference endpoints, making it suitable for production deployment.
- Two-stage training pipeline with VG and FACTUAL datasets
- Compatible with text-generation-inference systems
- Implemented using PyTorch and Transformers library
- Supports Safetensors format for efficient model loading
Core Capabilities
- Scene graph parsing from textual descriptions
- Faithful and consistent relationship extraction
- Structured representation of scene elements
- Integration with text-to-text generation pipelines
Frequently Asked Questions
Q: What makes this model unique?
This model's uniqueness lies in its specialized training approach, combining VG and FACTUAL datasets for enhanced scene graph parsing capabilities, while maintaining the versatility of the FLAN-T5 architecture.
Q: What are the recommended use cases?
The model is particularly suited for applications requiring structured scene understanding, including visual description analysis, relationship extraction from text, and scene graph generation for downstream visual tasks.
