multibert_testrun

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: 0.4300
• Precisions: 0.8488
• Recall: 0.7908
• F-measure: 0.8172
• Accuracy: 0.9404

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: 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
0.4196	1.0	269	0.3190	0.8426	0.7090	0.7230	0.9078
0.2111	2.0	538	0.2981	0.7730	0.7491	0.7551	0.9190
0.1275	3.0	807	0.2666	0.8158	0.7744	0.7915	0.9346
0.0868	4.0	1076	0.2929	0.8276	0.7891	0.8050	0.9349
0.0608	5.0	1345	0.3253	0.8370	0.7803	0.8043	0.9353
0.0353	6.0	1614	0.3723	0.8153	0.7999	0.8051	0.9360
0.0254	7.0	1883	0.4149	0.8266	0.7688	0.7934	0.9339
0.0203	8.0	2152	0.4399	0.8356	0.7755	0.8028	0.9357
0.0146	9.0	2421	0.4413	0.8295	0.7845	0.8045	0.9349
0.0108	10.0	2690	0.4300	0.8488	0.7908	0.8172	0.9404
0.0054	11.0	2959	0.4428	0.8317	0.7858	0.8062	0.9357
0.004	12.0	3228	0.4681	0.8403	0.7861	0.8095	0.9375
0.0019	13.0	3497	0.4725	0.8409	0.7901	0.8123	0.9386
0.0013	14.0	3766	0.4839	0.8437	0.7895	0.8137	0.9404

Framework versions

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