bert-srb-ner

This model was trained from scratch on the wikiann dataset. It achieves the following results on the evaluation set:

• Loss: 0.3561
• Precision: 0.8909
• Recall: 0.9082
• F1: 0.8995
• Accuracy: 0.9547

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: 32
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.3907	1.0	625	0.2316	0.8255	0.8314	0.8285	0.9259
0.2091	2.0	1250	0.1920	0.8598	0.8731	0.8664	0.9420
0.1562	3.0	1875	0.1833	0.8608	0.8820	0.8713	0.9441
0.0919	4.0	2500	0.1985	0.8712	0.8886	0.8798	0.9476
0.0625	5.0	3125	0.2195	0.8762	0.8923	0.8842	0.9485
0.0545	6.0	3750	0.2320	0.8706	0.9004	0.8852	0.9495
0.0403	7.0	4375	0.2459	0.8817	0.8957	0.8887	0.9505
0.0269	8.0	5000	0.2603	0.8813	0.9021	0.8916	0.9516
0.0193	9.0	5625	0.2916	0.8812	0.8949	0.8880	0.9500
0.0162	10.0	6250	0.2938	0.8814	0.9025	0.8918	0.9520
0.0134	11.0	6875	0.3330	0.8809	0.8961	0.8885	0.9497
0.0076	12.0	7500	0.3141	0.8840	0.9025	0.8932	0.9524
0.0069	13.0	8125	0.3292	0.8819	0.9065	0.8940	0.9535
0.0053	14.0	8750	0.3454	0.8844	0.9018	0.8930	0.9523
0.0038	15.0	9375	0.3519	0.8912	0.9061	0.8986	0.9539
0.0034	16.0	10000	0.3437	0.8894	0.9038	0.8965	0.9539
0.0024	17.0	10625	0.3518	0.8896	0.9072	0.8983	0.9543
0.0018	18.0	11250	0.3572	0.8877	0.9072	0.8973	0.9543
0.0015	19.0	11875	0.3554	0.8910	0.9081	0.8994	0.9549
0.0011	20.0	12500	0.3561	0.8909	0.9082	0.8995	0.9547

Framework versions

• Transformers 4.9.2
• Pytorch 1.9.0
• Datasets 1.11.0
• Tokenizers 0.10.1