multibert_1210seed25

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: 0.4453
• Precisions: 0.8647
• Recall: 0.8314
• F-measure: 0.8459
• Accuracy: 0.9141

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: 25
• 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
0.6013	1.0	236	0.4080	0.8974	0.6857	0.7273	0.8736
0.319	2.0	472	0.3621	0.8338	0.7306	0.7317	0.8875
0.1929	3.0	708	0.3823	0.8020	0.7680	0.7761	0.9022
0.1389	4.0	944	0.4353	0.8400	0.7742	0.7990	0.9003
0.0958	5.0	1180	0.4348	0.8726	0.7547	0.7971	0.9011
0.0676	6.0	1416	0.4453	0.8647	0.8314	0.8459	0.9141
0.0506	7.0	1652	0.5222	0.8555	0.8013	0.8253	0.9100
0.0315	8.0	1888	0.5192	0.8700	0.7873	0.8187	0.9108
0.0229	9.0	2124	0.5977	0.8402	0.7839	0.8079	0.9062
0.0149	10.0	2360	0.6061	0.8622	0.8069	0.8305	0.9131
0.0122	11.0	2596	0.5894	0.8419	0.7702	0.7983	0.9085
0.0065	12.0	2832	0.6120	0.8514	0.7700	0.8021	0.9089
0.0039	13.0	3068	0.6434	0.8437	0.7646	0.7965	0.9055
0.003	14.0	3304	0.6391	0.8403	0.7670	0.7973	0.9062

Framework versions

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