bert-base-uncased-filled-pauses-disfluency-detection-beta-v1 – AI Model by hafidev | AlphaNeural AI

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hafidev/
bert-base-uncased-filled-pauses-disfluency-detection-beta-v1

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token-classification

generated_from_trainer

google-bert/bert-base-uncased

autotrain_compatible

endpoints_compatible

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bert-base-uncased-filled-pauses-disfluency-detection-beta-v1

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: 0.0010
Model Preparation Time: 0.0033
Accuracy: 0.9997
Precision: 0.9941
Recall: 1.0
F1: 0.9970

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: 16
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
lr_scheduler_warmup_steps: 50
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Model Preparation Time	Accuracy	Precision	Recall	F1
0.0786	1.0	227	0.0020	0.0033	0.9996	0.9926	1.0	0.9963
0.0019	2.0	454	0.0014	0.0033	0.9997	0.9941	1.0	0.9970
0.0015	3.0	681	0.0013	0.0033	0.9997	0.9941	1.0	0.9970
0.0012	4.0	908	0.0012	0.0033	0.9997	0.9941	1.0	0.9970
0.0011	5.0	1135	0.0010	0.0033	0.9997	0.9941	1.0	0.9970

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1