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: 0.0000
Accuracy: 1.0

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: Use 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: 25

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	338	0.0028	1.0
0.1312	2.0	676	0.0008	1.0
0.0011	3.0	1014	0.0004	1.0
0.0011	4.0	1352	0.0002	1.0
0.0004	5.0	1690	0.0001	1.0
0.0002	6.0	2028	0.0001	1.0
0.0002	7.0	2366	0.0001	1.0
0.0001	8.0	2704	0.0001	1.0
0.0001	9.0	3042	0.0000	1.0
0.0001	10.0	3380	0.0000	1.0
0.0001	11.0	3718	0.0000	1.0
0.0	12.0	4056	0.0000	1.0
0.0	13.0	4394	0.0000	1.0
0.0	14.0	4732	0.0000	1.0
0.0	15.0	5070	0.0000	1.0
0.0	16.0	5408	0.0000	1.0
0.0	17.0	5746	0.0000	1.0
0.0	18.0	6084	0.0000	1.0
0.0	19.0	6422	0.0000	1.0
0.0	20.0	6760	0.0000	1.0
0.0	21.0	7098	0.0000	1.0
0.0	22.0	7436	0.0000	1.0
0.0	23.0	7774	0.0000	1.0
0.0	24.0	8112	0.0000	1.0
0.0	25.0	8450	0.0000	1.0

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

vishnu567/intent_classfication_01

intent_classfication_01

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

vishnu567/
intent_classfication_01