bert_results

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

• Loss: 0.1916
• Precision: 0.8920
• Recall: 0.8986
• F1: 0.8953

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: 8
• eval_batch_size: 8
• 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: linear
• num_epochs: 2
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1
0.1686	1.0	12500	0.1961	0.8800	0.8837	0.8818
0.1048	2.0	25000	0.1901	0.8931	0.8987	0.8959

Framework versions

• Transformers 4.46.3
• Pytorch 2.14.1+cu130
• Datasets 3.1.0
• Tokenizers 0.20.3