codebert-code-clone-detector

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