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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: 0.5292
• Accuracy: 0.8578
• F1: 0.9017

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: 0.0001
• train_batch_size: 32
• eval_batch_size: 16
• seed: 42
• optimizer: Use OptimizerNames.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	F1
0.4819	0.8696	100	0.4468	0.8064	0.8703
0.2429	1.7391	200	0.4559	0.8407	0.8915
0.1305	2.6087	300	0.5292	0.8578	0.9017

Framework versions

• Transformers 4.57.3
• Pytorch 2.5.1+cu121
• Datasets 4.4.1
• Tokenizers 0.22.1