bert-finetuning-italian

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

• Loss: 0.5598
• Model Preparation Time: 0.0031
• Accuracy: 0.7748
• F1 Macro: 0.7797
• Precision Macro: 0.7863
• Recall Macro: 0.7775

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: 2.1612703354421325e-05
• train_batch_size: 16
• eval_batch_size: 16
• 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: cosine_with_restarts
• lr_scheduler_warmup_ratio: 0.051758482154894515
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Model Preparation Time	Accuracy	F1 Macro	Precision Macro	Recall Macro
1.0928	1.0	735	0.7222	0.0031	0.6878	0.6933	0.7207	0.6978
0.6827	2.0	1470	0.6133	0.0031	0.7381	0.7456	0.7532	0.7479
0.473	3.0	2205	0.6064	0.0031	0.7544	0.7638	0.7708	0.7602
0.4259	4.0	2940	0.7100	0.0031	0.7469	0.7578	0.7640	0.7534
0.3187	5.0	3675	0.7481	0.0031	0.7524	0.7586	0.7631	0.7587

Framework versions

• Transformers 4.49.0
• Pytorch 2.5.1+cu124
• Datasets 3.3.2
• Tokenizers 0.21.0