bert-news-classifier

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: 0.5859
• Accuracy: 0.8471
• Precision: 0.8492
• Recall: 0.8471
• F1-score: 0.8463

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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1-score
1.8874	1.0	788	1.1642	0.5871	0.6028	0.5871	0.5692
0.8034	2.0	1576	0.6257	0.8214	0.8249	0.8214	0.8195
0.5749	3.0	2364	0.5859	0.8471	0.8492	0.8471	0.8463

Framework versions

• Transformers 4.49.0
• Pytorch 2.6.0+cpu
• Datasets 3.3.2
• Tokenizers 0.21.0