multibert1010_lrate7.5b32

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: 0.5515
• Precisions: 0.8551
• Recall: 0.8069
• F-measure: 0.8283
• Accuracy: 0.9171

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: 32
• eval_batch_size: 32
• 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
0.6054	1.0	118	0.4021	0.8661	0.6558	0.6767	0.8698
0.316	2.0	236	0.4039	0.8167	0.6935	0.7317	0.8800
0.1896	3.0	354	0.3480	0.8183	0.7792	0.7780	0.9003
0.1318	4.0	472	0.3930	0.8529	0.7703	0.7983	0.8965
0.0846	5.0	590	0.4027	0.8348	0.8010	0.8141	0.9047
0.0652	6.0	708	0.4824	0.8298	0.7555	0.7855	0.9002
0.0398	7.0	826	0.5446	0.8697	0.7766	0.8110	0.9017
0.0335	8.0	944	0.4761	0.8402	0.8013	0.8192	0.9054
0.0228	9.0	1062	0.5232	0.8547	0.7921	0.8156	0.9085
0.0181	10.0	1180	0.5477	0.8560	0.7968	0.8226	0.9133
0.0106	11.0	1298	0.5207	0.8370	0.8050	0.8199	0.9142
0.0075	12.0	1416	0.5381	0.8469	0.8025	0.8229	0.9156
0.0038	13.0	1534	0.5573	0.8538	0.8061	0.8269	0.9165
0.0047	14.0	1652	0.5515	0.8551	0.8069	0.8283	0.9171

Framework versions

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