RelSC is a new graph regression benchmark from program graphs with execution time labels, released in homogeneous (RelSC-H) and multi-relational (RelSC-M) variants to study representation effects.
Codebert: A pre-trained model for programming and natural languages
2 Pith papers cite this work. Polarity classification is still indexing.
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SmartIntentV2 uses a pre-trained BERT model on smart contracts to achieve an F1 score of 0.9279 for detecting malicious intents, outperforming previous models and GPT-4.1.
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A Benchmark Dataset for Graph Regression with Homogeneous and Multi-Relational Variants
RelSC is a new graph regression benchmark from program graphs with execution time labels, released in homogeneous (RelSC-H) and multi-relational (RelSC-M) variants to study representation effects.
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Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models
SmartIntentV2 uses a pre-trained BERT model on smart contracts to achieve an F1 score of 0.9279 for detecting malicious intents, outperforming previous models and GPT-4.1.