multibert1110_lrate2.5b16

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: 0.5408
• Precisions: 0.8751
• Recall: 0.8102
• F-measure: 0.8365
• Accuracy: 0.9131

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: 2.5e-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
0.6226	1.0	236	0.3957	0.8372	0.6871	0.6960	0.8714
0.3373	2.0	472	0.3830	0.8460	0.7204	0.7485	0.8810
0.2071	3.0	708	0.3464	0.8572	0.7790	0.7966	0.8985
0.1384	4.0	944	0.4046	0.8653	0.7863	0.8128	0.9041
0.0935	5.0	1180	0.4299	0.8559	0.7976	0.8209	0.9044
0.0708	6.0	1416	0.4899	0.8709	0.7972	0.8269	0.9096
0.0504	7.0	1652	0.4837	0.8578	0.8030	0.8254	0.9039
0.0361	8.0	1888	0.5098	0.8448	0.7970	0.8173	0.9056
0.0259	9.0	2124	0.5260	0.8622	0.7992	0.8241	0.9090
0.0214	10.0	2360	0.5394	0.8676	0.8051	0.8316	0.9107
0.0149	11.0	2596	0.5408	0.8751	0.8102	0.8365	0.9131
0.0095	12.0	2832	0.5725	0.8709	0.8056	0.8321	0.9115
0.0092	13.0	3068	0.5650	0.8658	0.8099	0.8326	0.9119
0.0073	14.0	3304	0.5734	0.8637	0.8101	0.8317	0.9122

Framework versions

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