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Deberta Base Mnli

Developed by microsoft
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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