results

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: 0.5096
• Accuracy: 0.848
• F1: 0.8468
• Precision: 0.84
• Recall: 0.8537

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: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.45	1.0	250	0.4892	0.816	0.8375	0.7406	0.9634
0.253	2.0	500	0.5096	0.848	0.8468	0.84	0.8537

Framework versions

• Transformers 4.44.2
• Pytorch 2.10.0+cu128
• Datasets 2.19.0
• Tokenizers 0.19.1