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---
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"
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# IndoBERT-Sentiment-Analysis
This model is a fine-tuned version of [indobenchmark/indobert-base-p1](https://huggingface.co/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 |