multibert_seed36_1311

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: 0.4419
• Precisions: 0.8943
• Recall: 0.8153
• F-measure: 0.8493
• Accuracy: 0.9385

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: 16
• eval_batch_size: 16
• seed: 36
• 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
0.4359	1.0	236	0.3021	0.8474	0.6948	0.7163	0.9077
0.2293	2.0	472	0.2484	0.8612	0.7522	0.7842	0.9258
0.1373	3.0	708	0.3033	0.7969	0.7892	0.7776	0.9250
0.0881	4.0	944	0.3218	0.8153	0.8103	0.8094	0.9299
0.0612	5.0	1180	0.3208	0.8357	0.8151	0.8225	0.9315
0.0378	6.0	1416	0.3553	0.8919	0.8173	0.8493	0.9405
0.0283	7.0	1652	0.4053	0.8575	0.8070	0.8270	0.9364
0.0229	8.0	1888	0.3789	0.8639	0.8236	0.8398	0.9354
0.0149	9.0	2124	0.4101	0.8856	0.8070	0.8387	0.9376
0.0073	10.0	2360	0.4419	0.8943	0.8153	0.8493	0.9385
0.0036	11.0	2596	0.4621	0.8882	0.8045	0.8392	0.9371
0.0045	12.0	2832	0.4494	0.8913	0.8093	0.8440	0.9383
0.0034	13.0	3068	0.4420	0.8795	0.8152	0.8422	0.9395
0.0014	14.0	3304	0.4494	0.8838	0.8100	0.8404	0.9390

Framework versions

• Transformers 4.35.0
• Pytorch 2.1.0+cu118
• Datasets 2.14.6
• Tokenizers 0.14.1