multibert1110_lrate7.5b8

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: 0.6124
• Precisions: 0.8795
• Recall: 0.7898
• F-measure: 0.8228
• Accuracy: 0.9026

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: 8
• eval_batch_size: 8
• 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
0.6395	1.0	471	0.4532	0.8345	0.6789	0.6899	0.8619
0.3636	2.0	942	0.3956	0.8284	0.7491	0.7766	0.8853
0.2471	3.0	1413	0.5037	0.8053	0.6950	0.7258	0.8821
0.1785	4.0	1884	0.5098	0.8444	0.7522	0.7755	0.8936
0.1279	5.0	2355	0.5574	0.8751	0.7735	0.8077	0.8973
0.097	6.0	2826	0.6124	0.8795	0.7898	0.8228	0.9026
0.071	7.0	3297	0.5377	0.8621	0.7836	0.8157	0.9044
0.0494	8.0	3768	0.5842	0.8705	0.7725	0.8109	0.9029
0.0344	9.0	4239	0.6835	0.8705	0.7506	0.7912	0.9010
0.0276	10.0	4710	0.6916	0.8226	0.7864	0.7999	0.9048
0.0174	11.0	5181	0.7412	0.8646	0.7491	0.7905	0.8994
0.0112	12.0	5652	0.7701	0.8258	0.7647	0.7866	0.9018
0.0084	13.0	6123	0.7811	0.8331	0.7593	0.7899	0.9058
0.0063	14.0	6594	0.7682	0.8636	0.7763	0.8112	0.9064

Framework versions

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