Number of objects | 5262 | Number of dataset objects | 233 |
Relationship types | 48 | Number of algorithm objects | 1448 |
Number of triples | 14774 | Number of interactions | 1485 |
Average number of descriptive words | 50.5 | Sparsity | 0.00440 |
operating system | Ubuntu 20.04.5 LTS |
memory | 64G |
graphics card | NVIDIA A100 40GB |
development language | Python 3.8 |
Deep learning platform | Pytorch 2.0.0 |
source language | English |
target language | Python |
quantity | 121 |
Average number of words in the source language | 52 |
Maximum number of words in the source language | 69 |
Average number of words in the target language | 1365 |
Maximum number of words in the target language | 1593 |
1 | CodeT5 | 770M | 12.62 | 7.62 | 3.02 | 5.29 |
2 | CodeT5-EKG | 770M | 23.93 | 13.52 | 4.62 | 10.02 |
3 | CodeT5 | 2B | 32.83 | 20.04 | 6.43 | 14.32 |
4 | CodeT5-EKG | 2B | 47.94 | 24.30 | 9.22 | 17.60 |
5 | CodeT5 | 6B | 46.27 | 32.96 | 14.21 | 25.68 |
6 | CodeT5-EKG | 6B | 51.12 | 35.58 | 16.11 | 27.54 |
Ruby | 2,119,741 |
JavaScript | 5,856,984 |
Go | 1,501,673 |
Python | 3,418,376 |
Java | 10,851,759 |
PHP | 4,386,876 |
C | 4,187,467 |
C++ | 2,951,945 |
C# | 4,119,796 |
Model | AUC | Precision | Recall | F1-score |
---|---|---|---|---|
KGNN-LS | 80.01 | 71.63 | 76.10 | 73.80 |
KGCN | 71.62 | 62.78 | 64.38 | 63.57 |
RippleNet | 82.55 | 69.43 | 86.91 | 77.19 |
TCF | 82.16 | 78.24 | 82.81 | 80.46 |
AD-EKG | 88.20 | 83.80 | 86.82 | 85.28 |
1 | CodeT5-EKG | 770M | 23.93 | 13.52 | 4.62 | 10.02 |
2 | CodeT5-EKG | 2B | 47.94 | 24.30 | 9.22 | 17.60 |
3 | CodeT5-EKG | 6B | 51.12 | 35.58 | 16.11 | 27.54 |
4 | CodeGen-Mono | 2B | 34.08 | 20.23 | 6.52 | 14.94 |
5 | GPT-Neo | 2.7B | 19.82 | 12.57 | 2.79 | 11.28 |
6 | InstructCodeT5 | 16B | 43.71 | 25.00 | 9.63 | 21.06 |
operating system | Windows 11 |
RAM | 16G |
Graphics card | NVIDIA GeForce RTX 3070 8G |
development language | Python 3.7.8 |
Deep learning platform | TensorFlow 2.2.0 |