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

• Loss: 0.3746
• Topology Accuracy: 0.9851
• Service Accuracy: 0.9435
• Combined Accuracy: 0.9643

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: 5e-05
• train_batch_size: 16
• eval_batch_size: 16
• 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: 50
• num_epochs: 15
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Topology Accuracy	Service Accuracy	Combined Accuracy
1.016	1.0	64	0.9725	0.7411	0.6220	0.6815
0.7234	2.0	128	0.6385	0.9643	0.6935	0.8289
0.6038	3.0	192	0.5826	0.9345	0.7440	0.8393
0.5014	4.0	256	0.5192	0.9583	0.7738	0.8661
0.3959	5.0	320	0.4845	0.9732	0.7768	0.875
0.4165	6.0	384	0.4579	0.9762	0.8601	0.9182
0.3699	7.0	448	0.4156	0.9851	0.9286	0.9568
0.3272	8.0	512	0.3777	0.9851	0.9524	0.9688
0.3091	9.0	576	0.3714	0.9851	0.9464	0.9658
0.3092	10.0	640	0.3814	0.9821	0.9464	0.9643
0.3221	11.0	704	0.3811	0.9821	0.9405	0.9613
0.3033	12.0	768	0.3724	0.9851	0.9405	0.9628
0.304	13.0	832	0.3741	0.9881	0.9435	0.9658
0.3051	14.0	896	0.3743	0.9851	0.9435	0.9643
0.3039	15.0	960	0.3746	0.9851	0.9435	0.9643

Framework versions

• Transformers 4.56.1
• Pytorch 2.8.0+cu126
• Datasets 4.0.0
• Tokenizers 0.22.0