multibert_1210seed7

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: 0.5019
• Precisions: 0.8874
• Recall: 0.7790
• F-measure: 0.8105
• Accuracy: 0.9107

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: 7
• 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
0.6032	1.0	236	0.4733	0.8608	0.6507	0.6853	0.8645
0.3527	2.0	472	0.3790	0.8098	0.7259	0.7383	0.8826
0.2198	3.0	708	0.4191	0.8209	0.7632	0.7816	0.8936
0.1359	4.0	944	0.4433	0.8430	0.7344	0.7590	0.8924
0.0862	5.0	1180	0.5207	0.8067	0.7697	0.7838	0.8947
0.0637	6.0	1416	0.5019	0.8874	0.7790	0.8105	0.9107
0.0454	7.0	1652	0.5048	0.8049	0.8135	0.8070	0.9058
0.0318	8.0	1888	0.5969	0.8135	0.7710	0.7845	0.9003
0.024	9.0	2124	0.6388	0.8295	0.7999	0.8057	0.9048
0.0138	10.0	2360	0.6448	0.8304	0.7727	0.7949	0.9033
0.0084	11.0	2596	0.6589	0.8216	0.7756	0.7936	0.9017
0.0091	12.0	2832	0.6471	0.8340	0.7683	0.7952	0.9045
0.005	13.0	3068	0.6817	0.8600	0.7662	0.8034	0.9073
0.0045	14.0	3304	0.6774	0.8397	0.7680	0.7976	0.9077

Framework versions

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