This model is a fine-tuned version of distilbert/distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.4307
Accuracy: 0.9303

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: 1e-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
lr_scheduler_warmup_steps: 1000
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.7756	1.0	388	0.5982	0.84
0.4327	2.0	776	0.4598	0.8658
0.4457	3.0	1164	0.4576	0.8684
0.3002	4.0	1552	0.3834	0.9039
0.3735	5.0	1940	0.3693	0.8968
0.3499	6.0	2328	0.4273	0.8961
0.3335	7.0	2716	0.3690	0.9155
0.2379	8.0	3104	0.4456	0.8987
0.039	9.0	3492	0.4262	0.9168
0.1206	10.0	3880	0.4058	0.9194
0.0602	11.0	4268	0.4234	0.9252
0.0411	12.0	4656	0.4621	0.9226
0.0667	13.0	5044	0.4307	0.9303

Framework versions

Transformers 4.42.4
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1

sara-m98/DISTILBERT_BIRADS_ECO_MAMO_3_FINAL

DISTILBERT_SARA_BIRADS_ECO_MAMO_3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

sara-m98/
DISTILBERT_BIRADS_ECO_MAMO_3_FINAL