padilfm/fine-tuned-indobert

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

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