logun-base

This model is a fine-tuned version of Itau-Unibanco/NorBERTo-base on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.4919

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: 5e-05
• train_batch_size: 2
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 16
• total_train_batch_size: 32
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 1.5
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
0.5470	0.0860	500	0.5439
0.5278	0.1719	1000	0.5265
0.5182	0.2579	1500	0.5230
0.5087	0.3439	2000	0.5111
0.5099	0.4299	2500	0.5067
0.5029	0.5158	3000	0.5020
0.4995	0.6018	3500	0.5057
0.4999	0.6878	4000	0.5016
0.4924	0.7737	4500	0.4992
0.4986	0.8597	5000	0.4918
0.4951	0.9457	5500	0.4952
0.4841	1.0316	6000	0.4954
0.5001	1.1176	6500	0.4933
0.4961	1.2036	7000	0.4953
0.4884	1.2895	7500	0.4918
0.4946	1.3755	8000	0.4919
0.4763	1.4615	8500	0.4874
0.4814	1.5000	8724	0.4919

Framework versions

• PEFT 0.20.0
• Transformers 5.16.1
• Pytorch 2.11.0+cu128
• Datasets 4.0.0
• Tokenizers 0.23.1