This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0492
Accuracy: 0.9938

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: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	10	0.7954	0.625
No log	2.0	20	0.6318	0.7812
No log	3.0	30	0.4738	0.8625
No log	4.0	40	0.3387	0.9062
No log	5.0	50	0.2444	0.925
No log	6.0	60	0.1899	0.9313
No log	7.0	70	0.1490	0.9313
No log	8.0	80	0.1216	0.9688
No log	9.0	90	0.0995	0.9812
No log	10.0	100	0.0832	0.9875
No log	11.0	110	0.0669	0.9938
No log	12.0	120	0.0598	0.9938
No log	13.0	130	0.0530	0.9938
No log	14.0	140	0.0502	0.9938
No log	15.0	150	0.0492	0.9938

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

lenate/my_awesome_model_priority_2

my_awesome_model_priority_2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

lenate/
my_awesome_model_priority_2