This model is a fine-tuned version of microsoft/codebert-base on a Code Clone Benchmark dataset. See this github repository for more information. It achieves the following results on the evaluation set:

Loss: 0.3452
Accuracy: 0.9525
Precision: 0.9544
Recall: 0.9496
F1: 0.9520

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.3416	0.49	33	0.1724	0.9417	0.9828	0.9048	0.9421
0.221	0.97	66	0.2768	0.925	1.0	0.8571	0.9231
0.0929	1.46	99	0.2469	0.9583	1.0	0.9206	0.9587
0.1696	1.94	132	0.2142	0.95	0.9524	0.9524	0.9524
0.0818	2.43	165	0.4142	0.925	1.0	0.8571	0.9231
0.0676	2.91	198	0.3539	0.9333	0.9508	0.9206	0.9355

Framework versions

Transformers 4.39.3
Pytorch 2.1.2
Datasets 2.18.0
Tokenizers 0.15.2

4luc/codebert-code-clone-detector

codebert-code-clone-detector

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

4luc/
codebert-code-clone-detector