Este es modelo resultado de un finetuning de google-bert/bert-base-cased sobre el conll2002 dataset. Los siguientes son los resultados sobre el conjunto de evaluación:

Training Loss: 0.000900
Validation Loss: 0.306902
Precision: 0.805356
Recall: 0.822381
F1: 0.813779
Accuracy: 0.969573

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
num_epochs: 8
weight_decay: 0.001

Training results

Epoch	Training Loss	Validation Loss	Precision	Recall	F1	Accuracy
1.0	0.0045	0.263534	0.787187	0.815947	0.801309	0.968117
2.0	0.0054	0.261010	0.776933	0.798713	0.787673	0.966914
3.0	0.0031	0.288264	0.787994	0.811351	0.799502	0.967351
4.0	0.0030	0.261651	0.799186	0.812040	0.805562	0.969476
5.0	0.0023	0.281675	0.792880	0.813649	0.803130	0.968544
6.0	0.0014	0.285965	0.790842	0.817555	0.803977	0.969311
7.0	0.0009	0.320790	0.795583	0.811121	0.803277	0.968049
8.0	0.0009	0.306902	0.805356	0.822381	0.813779	0.969573

PLN-T4-J-D-W/bert-finetuned-ner-1

bert-finetuned-ner-1

Training hyperparameters

Training results

PLN-T4-J-D-W/
bert-finetuned-ner-1