Indonesian Sentiment

🚀 Indonesian Sentiment Analysis Model

This model is a fine - tuned version of an Indonesian pre - trained BERT model, designed to perform sentiment analysis on Indonesian comments and reviews, offering valuable insights into public opinion.

🚀 Quick Start

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)

✨ Features

• Fine - tuned BERT: Based on IndoBERT Base Uncased, a BERT model pre - trained on Indonesian text data.
• Multi - class Classification: Classifies Indonesian review text into three sentiment categories: Negative, Neutral, and Positive.

📦 Installation

No specific installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

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)

📚 Documentation

Model Details

This model is a fine - tuned version of 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 (SmSA) and indonesian_sentiment 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:

How to Use

You can load the model and perform inference as shown in the Usage Examples section.

🔧 Technical Details

The model is a fine - tuned BERT model, which is a powerful architecture for natural language processing tasks. It leverages the pre - trained knowledge on Indonesian text data from IndoBERT Base Uncased and further fine - tunes it on specific sentiment analysis datasets. The training hyperparameters are carefully selected to optimize the model's performance on sentiment classification.

📄 License

This model is released under the MIT license.

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}
}
@inproceedings{purwarianti2019improving,
  title={Improving Bi - LSTM Performance for Indonesian Sentiment Analysis Using Paragraph Vector},
  author={Ayu Purwarianti and Ida Ayu Putu Ari Crisdayanti},
  booktitle={Proceedings of the 2019 International Conference of Advanced Informatics: Concepts, Theory and Applications (ICAICTA)},
  pages={1--5},
  year={2019},
  organization={IEEE}
}

Additional Information