metadata

language: id
widget:
  - text: Entah mengapa saya merasakan ada sesuatu yang janggal di produk ini
datasets:
  - indonlp/indonlu
metrics:
  - accuracy

license: mit tags: - generated_from_trainer datasets: - indonlu metrics: - accuracy - f1 - precision - recall model-index: - name: indobert-base-uncased-finetuned-indonlu-smsa results: - task: name: Text Classification type: text-classification dataset: name: indonlu type: indonlu args: smsa metrics: - name: Accuracy type: accuracy value: 0.9301587301587302 - name: F1 type: f1 value: 0.9066105299178986 - name: Precision type: precision value: 0.8992078788375845 - name: Recall type: recall value: 0.9147307323234121

indobert-base-uncased-finetuned-indonlu-smsa

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

Loss: 0.2277
Accuracy: 0.9302
F1: 0.9066
Precision: 0.8992
Recall: 0.9147

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 1500
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
No log	1.0	344	0.3831	0.8476	0.7715	0.7817	0.7627
0.4167	2.0	688	0.2809	0.8905	0.8406	0.8699	0.8185
0.2624	3.0	1032	0.2254	0.9230	0.8842	0.9004	0.8714
0.2624	4.0	1376	0.2378	0.9238	0.8797	0.9180	0.8594
0.1865	5.0	1720	0.2277	0.9302	0.9066	0.8992	0.9147
0.1217	6.0	2064	0.2444	0.9262	0.8981	0.9013	0.8957
0.1217	7.0	2408	0.2985	0.9286	0.8999	0.9035	0.8971
0.0847	8.0	2752	0.3397	0.9278	0.8969	0.9090	0.8871
0.0551	9.0	3096	0.3542	0.9270	0.8961	0.9010	0.8924
0.0551	10.0	3440	0.3862	0.9222	0.8895	0.8970	0.8846

Framework versions

Transformers 4.14.1
Pytorch 1.10.0+cu111
Datasets 1.17.0
Tokenizers 0.10.3