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: 0.5635
Accuracy: 0.8159

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: 128
eval_batch_size: 128
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
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.9248	1.0	29	0.7330	0.6533
0.6779	2.0	58	0.5587	0.7748
0.5661	3.0	87	0.5331	0.7891
0.4916	4.0	116	0.5064	0.7989
0.4224	5.0	145	0.4861	0.8088
0.3789	6.0	174	0.4965	0.8097
0.3159	7.0	203	0.5085	0.8061
0.29	8.0	232	0.5049	0.8123
0.2548	9.0	261	0.5158	0.8213
0.2228	10.0	290	0.5447	0.8222
0.2145	11.0	319	0.5223	0.8248
0.1785	12.0	348	0.5511	0.8168
0.185	13.0	377	0.5806	0.8105
0.1643	14.0	406	0.5619	0.8114
0.155	15.0	435	0.5635	0.8159

Framework versions

Transformers 4.53.2
Pytorch 2.6.0+cu124
Datasets 3.2.0
Tokenizers 0.21.2

trung061204/absa-restaurants-distilbert

absa-restaurants-distilbert

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

trung061204/
absa-restaurants-distilbert