ModernBERT-base-ft-code-defect-detection-10e-4k

This model is a fine-tuned version of answerdotai/ModernBERT-base on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 2.0516
• Accuracy Score: 0.6369
• F1 Score: 0.6091
• Precision Score: 0.6159
• Recall Score: 0.6025

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: 8e-05
• train_batch_size: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Use adamw_torch with betas=(0.9,0.98) and epsilon=1e-06 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy Score	F1 Score	Precision Score	Recall Score
0.6768	1.0	342	0.6130	0.6358	0.5728	0.5315	0.6210
0.5902	2.0	684	0.5828	0.6654	0.5421	0.4311	0.7301
0.5346	3.0	1026	0.5995	0.6585	0.4744	0.3355	0.8096
0.4583	4.0	1368	0.6115	0.6812	0.6085	0.5394	0.6979
0.3722	5.0	1710	0.6749	0.6482	0.6197	0.6239	0.6156
0.2896	6.0	2052	0.8197	0.6490	0.6087	0.5944	0.6237
0.2234	7.0	2394	0.9451	0.6490	0.6019	0.5777	0.6282
0.1655	8.0	2736	1.1632	0.6354	0.6115	0.6247	0.5989
0.1151	9.0	3078	1.4168	0.6387	0.6063	0.6056	0.6070
0.0684	10.0	3420	2.0516	0.6369	0.6091	0.6159	0.6025

Framework versions

• Transformers 4.48.0.dev0
• Pytorch 2.5.1+cu121
• Datasets 3.2.0
• Tokenizers 0.21.0