metadata

license: mit
base_model: indobenchmark/indobert-base-p1
tags:
  - generated_from_trainer
datasets:
  - indonlu
metrics:
  - accuracy
model-index:
  - name: IndoBERT-Sentiment-Analysis
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: indonlu
          type: indonlu
          config: smsa
          split: validation
          args: smsa
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9452380952380952
language:
  - id
  - en
widget:
  - text: Doi asik bgt orangnya
  - example_title: Example 1
  - text: Ada pengumuman nih gaiss, besok liburr
  - example_title: Example 2
  - text: Kok gitu sih kelakuannya
  - example_title: Example 3

IndoBERT-Sentiment-Analysis

This model is a fine-tuned version of indobenchmark/indobert-base-p1 on the indonlu dataset. It achieves the following results on the evaluation set:

Loss: 0.4221
Accuracy: 0.9452
F1 Score: 0.9451

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: 2e-05
train_batch_size: 6
eval_batch_size: 6
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Score
0.3499	0.27	500	0.2392	0.9310	0.9311
0.3181	0.55	1000	0.3354	0.9175	0.9158
0.3001	0.82	1500	0.2965	0.9238	0.9243
0.2534	1.09	2000	0.3513	0.9222	0.9218
0.1692	1.36	2500	0.2657	0.9405	0.9399
0.1543	1.64	3000	0.4046	0.9198	0.9191
0.1827	1.91	3500	0.2800	0.9317	0.9319
0.1061	2.18	4000	0.3352	0.9389	0.9389
0.0639	2.45	4500	0.4033	0.9373	0.9365
0.0709	2.73	5000	0.3508	0.9365	0.9360
0.0922	3.0	5500	0.3313	0.9397	0.9394
0.0274	3.27	6000	0.3635	0.9444	0.9440
0.0273	3.54	6500	0.4074	0.9389	0.9387
0.0414	3.82	7000	0.3863	0.9405	0.9405
0.0156	4.09	7500	0.4128	0.9413	0.9412
0.0067	4.36	8000	0.4469	0.9397	0.9399
0.0056	4.63	8500	0.4297	0.9444	0.9445
0.0124	4.91	9000	0.4227	0.9452	0.9451

Framework versions

Transformers 4.39.0.dev0
Pytorch 2.1.0.dev20230729
Datasets 2.14.0
Tokenizers 0.15.2