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SOTA
Defektenerkennung
Defect Detection On Codexglue Devign
Defect Detection On Codexglue Devign
Metriken
Accuracy
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Columns
Modellname
Accuracy
Paper Title
Repository
PLBART + GFSA
62.96
Graph Convolutions Enrich the Self-Attention in Transformers!
-
CodeT5-small + GFSA
63.69
Graph Convolutions Enrich the Self-Attention in Transformers!
-
CodeT5-small
63.25
Graph Convolutions Enrich the Self-Attention in Transformers!
-
RoBERTa + GFSA
64.39
Graph Convolutions Enrich the Self-Attention in Transformers!
-
PLBART
62.63
Graph Convolutions Enrich the Self-Attention in Transformers!
-
CodeT5
65.78
CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation
-
CodeT5-base
63.51
Graph Convolutions Enrich the Self-Attention in Transformers!
-
CodeBERT + GFSA
64.49
Graph Convolutions Enrich the Self-Attention in Transformers!
-
RoBERTa
62.88
Graph Convolutions Enrich the Self-Attention in Transformers!
-
CodeBERT
64.31
Graph Convolutions Enrich the Self-Attention in Transformers!
-
CodeBERT
62.08
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
-
CodeT5-base + GFSA
64.75
Graph Convolutions Enrich the Self-Attention in Transformers!
-
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Defect Detection On Codexglue Devign | SOTA | HyperAI