NER_ehealth_Spanish_mBERT_fine_tuned

This model is a fine-tuned version of bert-base-multilingual-cased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.6563
• Precision: 0.8094
• Recall: 0.8330
• F1: 0.8210
• Accuracy: 0.9051

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: 2e-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: 12

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	100	0.5335	0.8018	0.8307	0.8160	0.9047
No log	2.0	200	0.5034	0.8110	0.8253	0.8181	0.9067
No log	3.0	300	0.5632	0.7932	0.8230	0.8078	0.9038
No log	4.0	400	0.5904	0.8004	0.8299	0.8149	0.9027
0.017	5.0	500	0.5958	0.7993	0.8330	0.8158	0.9071
0.017	6.0	600	0.6168	0.7980	0.8352	0.8162	0.9022
0.017	7.0	700	0.6219	0.8079	0.8314	0.8195	0.9062
0.017	8.0	800	0.6441	0.8046	0.8299	0.8171	0.9038
0.017	9.0	900	0.6338	0.8086	0.8253	0.8168	0.9051
0.0066	10.0	1000	0.6482	0.8021	0.8261	0.8139	0.9029
0.0066	11.0	1100	0.6578	0.8039	0.8291	0.8163	0.9038
0.0066	12.0	1200	0.6563	0.8094	0.8330	0.8210	0.9051

Framework versions

• Transformers 4.21.2
• Pytorch 1.12.1+cu113
• Datasets 2.4.0
• Tokenizers 0.12.1