bert-finetuned-sla

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

• Loss: 0.3274
• F1: 0.6555
• Roc Auc: 0.7660
• Accuracy: 0.5294

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: 4
• eval_batch_size: 4
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Roc Auc	Accuracy
No log	1.0	30	0.4994	0.0	0.5	0.0
No log	2.0	60	0.4408	0.0	0.5	0.0
No log	3.0	90	0.3761	0.4444	0.6462	0.1961
No log	4.0	120	0.3438	0.6496	0.7604	0.4706
No log	5.0	150	0.3274	0.6555	0.7660	0.5294
No log	6.0	180	0.3093	0.6557	0.7699	0.4706
No log	7.0	210	0.3083	0.6560	0.7738	0.5098
No log	8.0	240	0.3030	0.6457	0.7703	0.4706
No log	9.0	270	0.3096	0.6667	0.7811	0.4902
No log	10.0	300	0.2976	0.6718	0.7907	0.5098
No log	11.0	330	0.2986	0.6769	0.7924	0.5294
No log	12.0	360	0.3046	0.6562	0.7777	0.5098
No log	13.0	390	0.2988	0.6870	0.7997	0.4902
No log	14.0	420	0.3026	0.6769	0.7924	0.5098
No log	15.0	450	0.3005	0.6870	0.7997	0.5098
No log	16.0	480	0.3012	0.6822	0.7941	0.5098
0.2216	17.0	510	0.3013	0.6977	0.8032	0.5294
0.2216	18.0	540	0.3033	0.6977	0.8032	0.5294
0.2216	19.0	570	0.3024	0.6977	0.8032	0.5294
0.2216	20.0	600	0.3027	0.6923	0.8015	0.5098

Framework versions

• Transformers 4.26.0
• Pytorch 1.13.1+cu116
• Datasets 2.9.0
• Tokenizers 0.13.2