newly_fine_tuned_bert

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: 0.2557
• F1: 0.7778
• Roc Auc: 0.8730
• Accuracy: 0.7778

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: 300

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Roc Auc	Accuracy
0.0276	39.5	790	0.1386	0.7778	0.8730	0.7778
0.0103	79.0	1580	0.1666	0.7778	0.8730	0.7778
0.0057	118.5	2370	0.2108	0.7778	0.8730	0.7778
0.0037	158.0	3160	0.2036	0.7778	0.8730	0.7778
0.0027	197.5	3950	0.2322	0.7778	0.8730	0.7778
0.0021	237.0	4740	0.2418	0.7778	0.8730	0.7778
0.0018	276.5	5530	0.2557	0.7778	0.8730	0.7778

Framework versions

• Transformers 4.45.2
• Pytorch 2.4.0+cu124
• Datasets 3.0.1
• Tokenizers 0.20.1