DeBERTa V3 Base
| Property | Value |
|---|---|
| Parameters (Backbone) | 86M |
| Vocabulary Size | 128K tokens |
| License | MIT |
| Author | Microsoft |
| Paper | DeBERTaV3 Paper |
What is deberta-v3-base?
DeBERTa-v3-base is Microsoft's enhanced version of the DeBERTa architecture, incorporating ELECTRA-style pre-training with gradient-disentangled embedding sharing. The model consists of 12 layers with a hidden size of 768, featuring 86M backbone parameters and a 128K token vocabulary.
Implementation Details
This model builds upon the original DeBERTa architecture by introducing significant improvements in efficiency and performance. It utilizes disentangled attention and enhanced mask decoder mechanisms, trained on 160GB of data.
- Advanced ELECTRA-style pre-training methodology
- Gradient-disentangled embedding sharing for improved efficiency
- 12-layer architecture with 768 hidden size
- State-of-the-art performance on key NLU benchmarks
Core Capabilities
- Superior performance on SQuAD 2.0 (88.4/85.4 F1/EM)
- Outstanding MNLI results (90.6/90.7 m/mm accuracy)
- Efficient parameter utilization compared to previous models
- Advanced masked language modeling capabilities
Frequently Asked Questions
Q: What makes this model unique?
DeBERTa-v3-base stands out through its innovative combination of ELECTRA-style pre-training and gradient-disentangled embedding sharing, achieving superior performance with fewer parameters than its predecessors.
Q: What are the recommended use cases?
The model excels in natural language understanding tasks, particularly in question answering (SQuAD) and natural language inference (MNLI). It's ideal for applications requiring robust language understanding and high accuracy in text analysis.
