bert-base-uncased-finetuned-rte-run_3

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: 1.3146
• Accuracy: 0.6245

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: 0.0002
• train_batch_size: 64
• eval_batch_size: 64
• 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: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	39	0.6537	0.6065
No log	2.0	78	0.6883	0.5596
No log	3.0	117	0.8331	0.6065
No log	4.0	156	0.6815	0.6209
No log	5.0	195	1.1783	0.5740
No log	6.0	234	1.0222	0.5921
No log	7.0	273	1.0504	0.5957
No log	8.0	312	1.0285	0.5921
No log	9.0	351	1.1737	0.6029
No log	10.0	390	1.3244	0.6065
No log	11.0	429	1.0123	0.6137
No log	12.0	468	1.3146	0.6245
0.332	13.0	507	1.4058	0.6065
0.332	14.0	546	1.5425	0.6245
0.332	15.0	585	1.4506	0.6245

Framework versions

• Transformers 4.50.3
• Pytorch 2.6.0+cu124
• Datasets 3.5.0
• Tokenizers 0.21.1