metadata
license: mit
lang: id
widget:
- text: Pelayanan lama dan tidak ramah.
example_title: Sentiment analysis
datasets:
- indonlp/indonlu
- sepidmnorozy/Indonesian_sentiment
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:
| 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}
}