bert-base-uncased-disfluency-filled-pauses-detection-test

This model is a fine-tuned version of google-bert/bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0026
• Model Preparation Time: 0.0056
• Accuracy: 0.9994
• Precision: 0.9936
• Recall: 0.9968
• F1: 0.9952

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: 1e-05
• train_batch_size: 8
• eval_batch_size: 8
• 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
• lr_scheduler_warmup_steps: 50
• num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Model Preparation Time	Accuracy	Precision	Recall	F1
0.041	1.0	500	0.0022	0.0056	0.9995	0.9957	0.9968	0.9962
0.0035	2.0	1000	0.0026	0.0056	0.9994	0.9936	0.9968	0.9952
0.0024	3.0	1500	0.0024	0.0056	0.9993	0.9914	0.9968	0.9941
0.0018	4.0	2000	0.0023	0.0056	0.9994	0.9925	0.9968	0.9946
0.0012	5.0	2500	0.0026	0.0056	0.9994	0.9936	0.9968	0.9952

Framework versions

• Transformers 4.51.3
• Pytorch 2.6.0+cu124
• Datasets 3.5.0
• Tokenizers 0.21.1