BERTInvoiceCzechR

This model is a fine-tuned version of google-bert/bert-base-multilingual-cased on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.3291
• Precision: 0.5188
• Recall: 0.6917
• F1: 0.5929
• Accuracy: 0.9335

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: 2
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 0.1
• num_epochs: 10
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	87	0.3944	0.1965	0.2233	0.2091	0.8997
No log	2.0	174	0.2951	0.4152	0.4517	0.4327	0.9241
No log	3.0	261	0.2896	0.4790	0.5810	0.5251	0.9314
No log	4.0	348	0.3295	0.4549	0.6443	0.5333	0.9226
No log	5.0	435	0.3249	0.4908	0.6866	0.5724	0.9281
0.3757	6.0	522	0.3615	0.4646	0.6827	0.5529	0.9216
0.3757	7.0	609	0.3376	0.4913	0.6579	0.5625	0.9299
0.3757	8.0	696	0.3290	0.5194	0.6924	0.5935	0.9336
0.3757	9.0	783	0.3604	0.4906	0.6858	0.5720	0.9279
0.3757	10.0	870	0.3515	0.5011	0.6944	0.5821	0.9296

Framework versions

• Transformers 5.0.0
• Pytorch 2.10.0+cu128
• Datasets 4.0.0
• Tokenizers 0.22.2