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--- |
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license: mit |
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base_model: indobenchmark/indobert-base-p1 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- indonlu |
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metrics: |
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- accuracy |
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model-index: |
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- name: IndoBERT-Sentiment-Analysis |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: indonlu |
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type: indonlu |
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config: smsa |
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split: validation |
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args: smsa |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9452380952380952 |
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language: |
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- id |
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- en |
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widget: |
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- text: "Doi asik bgt orangnya" |
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- example_title: "Example 1" |
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- text: "Ada pengumuman nih gaiss, besok liburr" |
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- example_title: "Example 2" |
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- text: "Kok gitu sih kelakuannya" |
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- example_title: "Example 3" |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# IndoBERT-Sentiment-Analysis |
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This model is a fine-tuned version of [indobenchmark/indobert-base-p1](https://huggingface.co/indobenchmark/indobert-base-p1) on the indonlu dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4221 |
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- Accuracy: 0.9452 |
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- F1 Score: 0.9451 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 6 |
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- eval_batch_size: 6 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:| |
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| 0.3499 | 0.27 | 500 | 0.2392 | 0.9310 | 0.9311 | |
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| 0.3181 | 0.55 | 1000 | 0.3354 | 0.9175 | 0.9158 | |
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| 0.3001 | 0.82 | 1500 | 0.2965 | 0.9238 | 0.9243 | |
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| 0.2534 | 1.09 | 2000 | 0.3513 | 0.9222 | 0.9218 | |
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| 0.1692 | 1.36 | 2500 | 0.2657 | 0.9405 | 0.9399 | |
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| 0.1543 | 1.64 | 3000 | 0.4046 | 0.9198 | 0.9191 | |
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| 0.1827 | 1.91 | 3500 | 0.2800 | 0.9317 | 0.9319 | |
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| 0.1061 | 2.18 | 4000 | 0.3352 | 0.9389 | 0.9389 | |
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| 0.0639 | 2.45 | 4500 | 0.4033 | 0.9373 | 0.9365 | |
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| 0.0709 | 2.73 | 5000 | 0.3508 | 0.9365 | 0.9360 | |
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| 0.0922 | 3.0 | 5500 | 0.3313 | 0.9397 | 0.9394 | |
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| 0.0274 | 3.27 | 6000 | 0.3635 | 0.9444 | 0.9440 | |
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| 0.0273 | 3.54 | 6500 | 0.4074 | 0.9389 | 0.9387 | |
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| 0.0414 | 3.82 | 7000 | 0.3863 | 0.9405 | 0.9405 | |
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| 0.0156 | 4.09 | 7500 | 0.4128 | 0.9413 | 0.9412 | |
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| 0.0067 | 4.36 | 8000 | 0.4469 | 0.9397 | 0.9399 | |
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| 0.0056 | 4.63 | 8500 | 0.4297 | 0.9444 | 0.9445 | |
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| 0.0124 | 4.91 | 9000 | 0.4227 | 0.9452 | 0.9451 | |
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### Framework versions |
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- Transformers 4.39.0.dev0 |
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- Pytorch 2.1.0.dev20230729 |
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- Datasets 2.14.0 |
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- Tokenizers 0.15.2 |
