Type Prediction On Manytypes4Typescript
Métriques
Average Accuracy
Average F1
Average Precision
Average Recall
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | Average Accuracy | Average F1 | Average Precision | Average Recall | Paper Title | Repository |
|---|---|---|---|---|---|---|
| PolyGot | 61.29 | 58.86 | 58.81 | 58.91 | Multilingual training for Software Engineering | - |
| CodeBERTa | 59.81 | 56.71 | 56.57 | 56.85 | - | - |
| RoBERTa | 59.84 | 57.54 | 57.45 | 57.62 | RoBERTa: A Robustly Optimized BERT Pretraining Approach | - |
| BERT | 57.52 | 54.10 | 54.18 | 54.02 | BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding | - |
| GraphCodeBERT-MT4TS | 63.42 | - | - | - | ManyTypes4TypeScript: A Comprehensive TypeScript Dataset for Sequence-Based Type Inference | |
| GraphCodeBERT | 62.51 | 60.57 | 60.06 | 61.08 | GraphCodeBERT: Pre-training Code Representations with Data Flow | - |
| CodeTIDAL5 | 71.27 | - | - | - | Learning Type Inference for Enhanced Dataflow Analysis | - |
| GraphPolyGot | 61.00 | 58.63 | 58.36 | 58.91 | Multilingual training for Software Engineering | - |
| CodeBERT | 61.72 | 59.57 | 59.34 | 59.80 | CodeBERT: A Pre-Trained Model for Programming and Natural Languages | - |
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