finetuning-bert-text-classification

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: 0.2838
• Accuracy: 0.9114
• F1: 0.9114

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: 3e-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
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.2817	1.0	219	0.3042	0.9	0.8994
0.2264	2.0	438	0.2745	0.9186	0.9183
0.1237	3.0	657	0.2838	0.9114	0.9114

Framework versions

• Transformers 4.57.3
• Pytorch 2.9.1+cu128
• Datasets 4.0.0
• Tokenizers 0.22.1