CodeDetect-BERT-T2

This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 2.2616
• Accuracy: 0.9178
• F1: 0.4680
• Precision: 0.4960
• Recall: 0.4600

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: 128
• eval_batch_size: 128
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 5
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
2.1731	1.2798	5000	2.2130	0.9197	0.3964	0.4880	0.3890
1.8504	2.5595	10000	2.1255	0.9216	0.4423	0.5119	0.4239
1.5734	3.8393	15000	2.1121	0.9226	0.4766	0.5175	0.4584
1.4124	5.0	19535	2.2616	0.9178	0.4680	0.4960	0.4600

Framework versions

• Transformers 5.13.1
• Pytorch 2.11.0+cu128
• Datasets 4.0.0
• Tokenizers 0.22.2