multibert_1210seed24

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: 0.6397
• Precisions: 0.8875
• Recall: 0.7915
• F-measure: 0.8255
• Accuracy: 0.9112

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: 24
• 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
0.5949	1.0	236	0.4396	0.8425	0.6484	0.6768	0.8569
0.3352	2.0	472	0.4132	0.7836	0.7344	0.7453	0.8862
0.2148	3.0	708	0.3528	0.8396	0.7759	0.8020	0.8985
0.1389	4.0	944	0.4093	0.8386	0.7431	0.7775	0.8931
0.099	5.0	1180	0.4169	0.8501	0.7998	0.8200	0.9022
0.078	6.0	1416	0.4629	0.7912	0.7756	0.7815	0.8900
0.0536	7.0	1652	0.4658	0.8394	0.8096	0.8235	0.9098
0.0316	8.0	1888	0.5609	0.8440	0.7790	0.8044	0.9019
0.0217	9.0	2124	0.5870	0.8686	0.7814	0.8128	0.9055
0.0126	10.0	2360	0.5636	0.8613	0.7997	0.8255	0.9059
0.0115	11.0	2596	0.5978	0.8721	0.7964	0.8232	0.9093
0.0082	12.0	2832	0.6072	0.8645	0.7904	0.8184	0.9098
0.0042	13.0	3068	0.6332	0.8801	0.7903	0.8230	0.9104
0.0033	14.0	3304	0.6397	0.8875	0.7915	0.8255	0.9112

Framework versions

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