---
license: apache-2.0
language:
- id
- en
---

# Fine-tuned IndoBERT

This model is a fine-tuned version of IndoBERT for sentiment analysis.

## Model Details

- **Model Architecture:** BERT (Bidirectional Encoder Representations from Transformers)
- **Fine-tuning Objective:** Sentiment Analysis
- **Dataset:** DANA Sentiment Analysis from Playstore Indonesia from Kaggle
  
https://www.kaggle.com/datasets/alexmariosimanjuntak/dana-app-sentiment-review-on-playstore-indonesia/code

## Usage

```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("your-username/fine-tuned-indobert")
tokenizer = AutoTokenizer.from_pretrained("your-username/fine-tuned-indobert")

inputs = tokenizer("Your input text", return_tensors="pt")
outputs = model(**inputs)
```
## Training data
The model was trained on a custom dataset for sentiment analysis.

## Hyperparameters
- Learning rate: 2e-05
- Train batch size: 6
- Eval batch size: 6
- Epochs: 5
- Optimizer: Adam with betas=(0.9, 0.999) and epsilon=1e-08
- LR scheduler type: Linear
- Seed: 42
- Accuracy: 0.8578