metadata

language: id
license: mit
tags:
  - generated_from_trainer
datasets:
  - indonlu
metrics:
  - accuracy
  - f1
  - precision
  - recall
widget:
  - text: Entah mengapa saya merasakan ada sesuatu yang janggal di produk ini
base_model: indolem/indobert-base-uncased
model-index:
  - name: indobert-base-uncased-finetuned-indonlu-smsa
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: indonlu
          type: indonlu
          args: smsa
        metrics:
          - type: accuracy
            value: 0.9301587301587302
            name: Accuracy
          - type: f1
            value: 0.9066105299178986
            name: F1
          - type: precision
            value: 0.8992078788375845
            name: Precision
          - type: recall
            value: 0.9147307323234121
            name: Recall

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