metadata

license: mit
base_model: indolem/indobert-base-uncased
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: nerugm-base-3
    results: []

nerugm-base-3

This model is a fine-tuned version of indolem/indobert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.2918
Precision: 0.7974
Recall: 0.8847
F1: 0.8388
Accuracy: 0.9619

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: 5e-05
train_batch_size: 16
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20.0

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.3817	1.0	106	0.1442	0.7266	0.8732	0.7932	0.9515
0.1266	2.0	212	0.1385	0.7381	0.8934	0.8083	0.9551
0.087	3.0	318	0.1367	0.7512	0.8790	0.8101	0.9568
0.0528	4.0	424	0.1468	0.7732	0.8646	0.8163	0.9595
0.0424	5.0	530	0.1664	0.7899	0.8559	0.8216	0.9607
0.0275	6.0	636	0.2044	0.7714	0.8847	0.8242	0.9583
0.019	7.0	742	0.2377	0.7410	0.8905	0.8089	0.9554
0.0145	8.0	848	0.2432	0.7758	0.8876	0.8280	0.9588
0.0102	9.0	954	0.2287	0.8109	0.9020	0.8540	0.9641
0.0067	10.0	1060	0.2430	0.8026	0.8905	0.8443	0.9617
0.0064	11.0	1166	0.2675	0.7943	0.8905	0.8397	0.9602
0.0046	12.0	1272	0.2743	0.7828	0.8934	0.8345	0.9619
0.0034	13.0	1378	0.2666	0.7995	0.8963	0.8451	0.9619
0.0036	14.0	1484	0.2606	0.8117	0.8818	0.8453	0.9634
0.0027	15.0	1590	0.2862	0.7913	0.8963	0.8405	0.9627
0.0016	16.0	1696	0.2793	0.8021	0.8876	0.8427	0.9629
0.0012	17.0	1802	0.2951	0.7949	0.8934	0.8412	0.9622
0.0012	18.0	1908	0.2930	0.7938	0.8876	0.8381	0.9617
0.0014	19.0	2014	0.2953	0.7912	0.8847	0.8354	0.9612
0.0007	20.0	2120	0.2918	0.7974	0.8847	0.8388	0.9619

Framework versions

Transformers 4.39.3
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.15.2