---
license: mit
widget:
- text: "Pelayanan lama dan tidak ramah."
  example_title: "Sentiment analysis"
---
### Model Details
This model is a fine-tuned version of [IndoBERT Base Uncased](https://huggingface.co/indolem/indobert-base-uncased), a BERT model pre-trained on Indonesian text data. It was fine-tuned to perform sentiment analysis on Indonesian comments and reviews.

The model was trained on [indonlu](https://huggingface.co/datasets/indonlp/indonlu) (`SmSA`) datasets.

The model classifies a given Indonesian review text into one of three categories:

* Negative
* Neutral
* Positive

### Training hyperparameters
* train_batch_size: 32 
* eval_batch_size: 32
* learning_rate: 1e-4
* optimizer: AdamW with betas=(0.9, 0.999), eps=1e-8, and weight_decay=0.01
* epochs: 3
* learning_rate_scheduler: StepLR with step_size=592, gamma=0.1

### Training Results

The following table shows the training results for the model:

| Epoch | Loss | Accuracy |
|---|---|---|
| 1 | 0.2936 | 0.9310 |
| 2 | 0.1212 | 0.9526 |
| 3 | 0.0795 | 0.9569 |

### How to Use

You can load the model and perform inference as follows:
```
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("taufiqdp/indonesian-sentiment")
model = AutoModelForSequenceClassification.from_pretrained("taufiqdp/indonesian-sentiment")

class_names = ['negatif', 'netral', 'positif']

text = "Pelayanan lama dan tidak ramah"
tokenized_text = tokenizer(text, return_tensors='pt')

with torch.inference_mode():
    logits = model(**tokenized_text)['logits']

result = class_names[logits.argmax(dim=1)]
print(result)
```

### Citation

```
@misc{koto2020indolem,
      title={IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP}, 
      author={Fajri Koto and Afshin Rahimi and Jey Han Lau and Timothy Baldwin},
      year={2020},
      eprint={2011.00677},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```