multibert1110_lrate5b16

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

• Loss: 0.5618
• Precisions: 0.8632
• Recall: 0.8248
• F-measure: 0.8416
• Accuracy: 0.9160

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: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 14

Training results

Training Loss	Epoch	Step	Validation Loss	Precisions	Recall	F-measure	Accuracy
0.5858	1.0	236	0.3670	0.8368	0.6841	0.7038	0.8774
0.302	2.0	472	0.3603	0.8064	0.7589	0.7780	0.8931
0.1746	3.0	708	0.3442	0.8616	0.7693	0.7773	0.9026
0.118	4.0	944	0.4355	0.8683	0.7908	0.8197	0.9039
0.0822	5.0	1180	0.4320	0.8775	0.8042	0.8343	0.9094
0.0597	6.0	1416	0.4654	0.8722	0.8075	0.8298	0.9089
0.0363	7.0	1652	0.5211	0.8768	0.7803	0.8192	0.9054
0.0258	8.0	1888	0.4996	0.8631	0.8111	0.8306	0.9133
0.0165	9.0	2124	0.6172	0.8984	0.7691	0.8095	0.9073
0.0135	10.0	2360	0.5919	0.8912	0.7948	0.8312	0.9130
0.0111	11.0	2596	0.5726	0.8704	0.8003	0.8280	0.9143
0.0079	12.0	2832	0.5618	0.8632	0.8248	0.8416	0.9160
0.0047	13.0	3068	0.5917	0.8674	0.7977	0.8269	0.9149
0.0042	14.0	3304	0.5886	0.8685	0.8014	0.8292	0.9161

Framework versions

• Transformers 4.34.0
• Pytorch 2.0.1+cu118
• Datasets 2.14.5
• Tokenizers 0.14.1