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

Loss: 0.1204
Precision: 0.9429
Recall: 0.9658
F1: 0.9542
Accuracy: 0.9764

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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.1823	1.0	1224	0.1059	0.9301	0.9628	0.9462	0.9731
0.1142	2.0	2448	0.1163	0.9203	0.9717	0.9453	0.9698
0.0812	3.0	3672	0.1000	0.9427	0.9705	0.9564	0.9773
0.0603	4.0	4896	0.0970	0.9424	0.9717	0.9568	0.9773
0.0516	5.0	6120	0.1018	0.9416	0.9720	0.9566	0.9772
0.0418	6.0	7344	0.1044	0.9446	0.9704	0.9574	0.9778
0.0361	7.0	8568	0.1070	0.9422	0.9725	0.9571	0.9775
0.0296	8.0	9792	0.1166	0.9438	0.9708	0.9571	0.9776
0.0242	9.0	11016	0.1174	0.9437	0.9671	0.9553	0.9767
0.0231	10.0	12240	0.1204	0.9429	0.9658	0.9542	0.9764

Framework versions

Transformers 4.45.1
Pytorch 2.4.0
Datasets 3.0.1
Tokenizers 0.20.0

juancortizgonz/bert-base-uncased-finetuned-ner

bert-base-uncased-finetuned-ner

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

juancortizgonz/
bert-base-uncased-finetuned-ner