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: 0.1090
Accuracy: 0.9875

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: 11

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	10	0.8290	0.625
No log	2.0	20	0.6544	0.8187
No log	3.0	30	0.4782	0.8688
No log	4.0	40	0.3593	0.9062
No log	5.0	50	0.2612	0.9313
No log	6.0	60	0.2043	0.9313
No log	7.0	70	0.1709	0.9375
No log	8.0	80	0.1448	0.95
No log	9.0	90	0.1236	0.9688
No log	10.0	100	0.1129	0.9875
No log	11.0	110	0.1090	0.9875

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

lenate/my_awesome_model_priority_3

my_awesome_model_priority_3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

lenate/
my_awesome_model_priority_3