metadata

language:
  - id
license: mit
base_model: indolem/indobert-base-uncased
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: nerugm-base-4
    results: []

nerugm-base-4

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: 0.2607
Precision: 0.8198
Recall: 0.8946
F1: 0.8556
Accuracy: 0.9651

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
0.3182	1.0	106	0.1284	0.7463	0.8547	0.7968	0.9572
0.1137	2.0	212	0.1302	0.7230	0.8775	0.7928	0.9562
0.0683	3.0	318	0.1249	0.7833	0.8547	0.8174	0.9606
0.0454	4.0	424	0.1464	0.7711	0.8832	0.8234	0.9591
0.0325	5.0	530	0.1557	0.8010	0.8832	0.8401	0.9641
0.0211	6.0	636	0.2112	0.7915	0.8974	0.8411	0.9599
0.015	7.0	742	0.1944	0.7734	0.8946	0.8296	0.9606
0.0113	8.0	848	0.2151	0.8140	0.8974	0.8537	0.9665
0.0075	9.0	954	0.1996	0.8140	0.8974	0.8537	0.9685
0.0067	10.0	1060	0.2077	0.8470	0.8832	0.8647	0.9685
0.0039	11.0	1166	0.2609	0.7698	0.8860	0.8238	0.9579
0.0028	12.0	1272	0.2498	0.8263	0.8946	0.8591	0.9648
0.0035	13.0	1378	0.2407	0.8179	0.8832	0.8493	0.9643
0.003	14.0	1484	0.2475	0.7919	0.8889	0.8376	0.9631
0.0016	15.0	1590	0.2552	0.7975	0.8974	0.8445	0.9641
0.0016	16.0	1696	0.2463	0.8268	0.8974	0.8607	0.9665
0.0012	17.0	1802	0.2500	0.8324	0.8917	0.8611	0.9665
0.0009	18.0	1908	0.2629	0.8208	0.9003	0.8587	0.9653
0.0014	19.0	2014	0.2619	0.8182	0.8974	0.8560	0.9651
0.0006	20.0	2120	0.2607	0.8198	0.8946	0.8556	0.9651

Framework versions

Transformers 4.39.3
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.15.2