This model is a fine-tuned version of google-bert/bert-base-uncased on the conll2003 dataset. It achieves the following results on the evaluation set:

Loss: 0.1387
Precision: 0.8925
Recall: 0.9102
F1: 0.9013
Accuracy: 0.9800

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: 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: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.1773	1.0	878	0.1028	0.8910	0.8947	0.8928	0.9781
0.036	2.0	1756	0.1125	0.8901	0.9132	0.9015	0.9793
0.0194	3.0	2634	0.1202	0.8948	0.9093	0.9020	0.9800
0.0112	4.0	3512	0.1346	0.8889	0.9136	0.9011	0.9794
0.0081	5.0	4390	0.1387	0.8925	0.9102	0.9013	0.9800

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu121
Datasets 2.21.0
Tokenizers 0.19.1

eddiegulay/g-bert-NER

g-bert-NER

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

eddiegulay/
g-bert-NER