Deberta Base Mnli
Enhanced BERT decoding model based on disentangled attention mechanism, fine-tuned on MNLI task
Downloads 96.92k
Release Time : 3/2/2022
Model Overview
DeBERTa improves upon BERT and RoBERTa models through disentangled attention mechanism and enhanced masked decoder, delivering superior performance on natural language understanding tasks.
Model Features
Disentangled Attention Mechanism
Enhances model comprehension by separating content and positional information processing
Enhanced Masked Decoder
Improved masking mechanism strengthens the model's contextual capture capability
Surpasses BERT/RoBERTa
Outperforms base versions of BERT and RoBERTa on most NLU tasks
Model Capabilities
Natural Language Understanding
Textual Entailment Recognition
Semantic Similarity Judgment
Use Cases
Text Analysis
Textual Entailment Judgment
Determine if an entailment relationship exists between two texts
Achieves 88.8% accuracy on MNLI dataset
Semantic Similarity Analysis
Analyze the semantic similarity between two texts
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