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: 0.4995
Accuracy: 0.7833

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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	355	0.5751	0.7424
0.6685	2.0	710	0.4995	0.7833
0.4105	3.0	1065	0.5366	0.7826
0.4105	4.0	1420	0.6784	0.7784
0.2443	5.0	1775	0.7940	0.7812
0.1393	6.0	2130	0.9600	0.7826
0.1393	7.0	2485	1.0671	0.7763
0.0921	8.0	2840	1.1389	0.7812
0.0456	9.0	3195	1.2180	0.7826
0.0297	10.0	3550	1.2229	0.7791

Framework versions

Transformers 4.40.0
Pytorch 2.2.1+cu121
Datasets 2.19.0
Tokenizers 0.19.1

KalaiselvanD/albert_model__25_1

albert_model__25_1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

KalaiselvanD/
albert_model__25_1