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Deberta Large Mnli Zero Cls

Developed by Narsil
DeBERTa is an enhanced BERT decoding model based on the disentangled attention mechanism, surpassing BERT and RoBERTa in multiple natural language understanding tasks by improving the attention mechanism and masked decoder.
Downloads 51.27k
Release Time : 3/2/2022

Model Overview

DeBERTa improves upon BERT and RoBERTa models through its disentangled attention mechanism and enhanced masked decoder, supporting various natural language understanding tasks.

Model Features

Disentangled Attention Mechanism
Improves traditional attention computation through a disentangled attention mechanism, enhancing model performance.
Enhanced Masked Decoder
Utilizes an enhanced masked decoder to further improve the model's performance in natural language understanding tasks.
High Performance
Outperforms models like BERT, RoBERTa, and XLNet in multiple natural language understanding tasks.

Model Capabilities

Text Classification
Question Answering Systems
Natural Language Inference
Semantic Similarity Calculation

Use Cases

Natural Language Processing
Text Classification
Used for tasks such as sentiment analysis and topic classification.
Achieves an accuracy of 97.2% on the SST-2 dataset.
Question Answering Systems
Used to build high-performance question answering systems.
Scores an F1 of 96.1 and EM of 91.4 on the SQuAD 1.1 dataset.
Natural Language Inference
Used to determine the logical relationship between two sentences.
Achieves an accuracy of 91.7/91.9 (matched/mismatched) on the MNLI dataset.
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