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