mbert-shared-task

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

• Loss: 0.5703
• Precision: 0.7026
• Recall: 0.7092
• Accuracy: 0.7806
• F1: 0.7057

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: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Use adamw_torch 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: 286
• num_epochs: 10
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	Accuracy	F1
0.6972	1.0	96	0.6793	0.5609	0.5590	0.4045	0.4044
0.6298	2.0	192	0.6673	0.6160	0.6396	0.5142	0.5105
0.5252	3.0	288	0.5703	0.7026	0.7092	0.7806	0.7057
0.4483	4.0	384	0.6299	0.7020	0.6849	0.7835	0.6923
0.3792	5.0	480	0.8007	0.6669	0.6866	0.7463	0.6744
0.2724	6.0	576	0.7452	0.6755	0.6840	0.7591	0.6794
0.1949	7.0	672	1.1097	0.6741	0.6672	0.7630	0.6705
0.1418	8.0	768	1.2151	0.6711	0.6702	0.7591	0.6706
0.109	9.0	864	1.4251	0.6655	0.6691	0.7532	0.6672
0.0962	10.0	960	1.5793	0.6710	0.6632	0.7610	0.6668

Framework versions

• Transformers 4.47.0
• Pytorch 2.5.1+cu121
• Datasets 3.3.1
• Tokenizers 0.21.0