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: 0.2024
Precision: 0.8316
Recall: 0.5374
F1: 0.6529
Accuracy: 0.9405

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	Precision	Recall	F1	Accuracy
No log	1.0	4	1.0541	0.0	0.0	0.0	0.8445
No log	2.0	8	0.6374	0.0	0.0	0.0	0.8445
No log	3.0	12	0.5150	0.0	0.0	0.0	0.8445
No log	4.0	16	0.4235	0.0	0.0	0.0	0.8445
No log	5.0	20	0.3564	0.5	0.0850	0.1453	0.8667
No log	6.0	24	0.3024	0.5	0.0850	0.1453	0.8667
No log	7.0	28	0.2609	0.6835	0.1837	0.2895	0.8796
No log	8.0	32	0.2299	0.8264	0.4048	0.5434	0.9085
No log	9.0	36	0.2104	0.7826	0.4898	0.6025	0.9280
No log	10.0	40	0.2024	0.8316	0.5374	0.6529	0.9405

Framework versions

Transformers 4.40.2
Pytorch 2.2.1+cu121
Datasets 2.19.1
Tokenizers 0.19.1

raoulmago/codice_fiscale

codice_fiscale

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

raoulmago/
codice_fiscale