metadata

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

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: 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