multibert1110_lrate7.5b4

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: 0.7163
• Precisions: 0.8864
• Recall: 0.8013
• F-measure: 0.8374
• Accuracy: 0.9059

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: 7.5e-05
• train_batch_size: 4
• eval_batch_size: 4
• 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
0.7304	1.0	942	0.4905	0.8049	0.6436	0.6549	0.8554
0.4336	2.0	1884	0.6035	0.8585	0.6334	0.6863	0.8477
0.3238	3.0	2826	0.5094	0.8668	0.7014	0.7232	0.8882
0.249	4.0	3768	0.5951	0.8798	0.7110	0.7609	0.8770
0.191	5.0	4710	0.4988	0.8304	0.7761	0.7816	0.8975
0.1513	6.0	5652	0.5998	0.8351	0.7917	0.8062	0.8962
0.1088	7.0	6594	0.5874	0.8427	0.7953	0.8158	0.9003
0.0914	8.0	7536	0.5529	0.8580	0.7885	0.8087	0.9069
0.0682	9.0	8478	0.6882	0.8371	0.7773	0.8024	0.8958
0.0487	10.0	9420	0.7163	0.8864	0.8013	0.8374	0.9059
0.0319	11.0	10362	0.7020	0.8724	0.7867	0.8235	0.9007
0.0305	12.0	11304	0.6886	0.8689	0.8002	0.8311	0.9079
0.0184	13.0	12246	0.6994	0.8680	0.8089	0.8357	0.9085
0.0138	14.0	13188	0.7183	0.8677	0.8105	0.8362	0.9093

Framework versions

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