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---
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language: id
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tags:
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- indonesian-roberta-base-sentiment-classifier
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license: mit
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datasets:
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- indonlu
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widget:
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- text: "Jangan sampai saya telpon bos saya ya!"
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---
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## Indonesian RoBERTa Base Sentiment Classifier
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Indonesian RoBERTa Base Sentiment Classifier is a sentiment-text-classification model based on the [RoBERTa](https://arxiv.org/abs/1907.11692) model. The model was originally the pre-trained [Indonesian RoBERTa Base](https://hf.co/flax-community/indonesian-roberta-base) model, which is then fine-tuned on [`indonlu`](https://hf.co/datasets/indonlu)'s `SmSA` dataset consisting of Indonesian comments and reviews.
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After training, the model achieved an evaluation accuracy of 94.36% and F1-macro of 92.42%. On the benchmark test set, the model achieved an accuracy of 93.2% and F1-macro of 91.02%.
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Hugging Face's `Trainer` class from the [Transformers](https://huggingface.co/transformers) library was used to train the model. PyTorch was used as the backend framework during training, but the model remains compatible with other frameworks nonetheless.
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## Model
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| Model | #params | Arch. | Training/Validation data (text) |
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| ---------------------------------------------- | ------- | ------------ | ------------------------------- |
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| `indonesian-roberta-base-sentiment-classifier` | 124M | RoBERTa Base | `SmSA` |
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## Evaluation Results
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The model was trained for 5 epochs and the best model was loaded at the end.
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| Epoch | Training Loss | Validation Loss | Accuracy | F1 | Precision | Recall |
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| ----- | ------------- | --------------- | -------- | -------- | --------- | -------- |
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| 1 | 0.342600 | 0.213551 | 0.928571 | 0.898539 | 0.909803 | 0.890694 |
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| 2 | 0.190700 | 0.213466 | 0.934127 | 0.901135 | 0.925297 | 0.882757 |
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| 3 | 0.125500 | 0.219539 | 0.942857 | 0.920901 | 0.927511 | 0.915193 |
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| 4 | 0.083600 | 0.235232 | 0.943651 | 0.924227 | 0.926494 | 0.922048 |
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| 5 | 0.059200 | 0.262473 | 0.942063 | 0.920583 | 0.924084 | 0.917351 |
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## How to Use
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### As Text Classifier
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```python
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from transformers import pipeline
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pretrained_name = "w11wo/indonesian-roberta-base-sentiment-classifier"
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nlp = pipeline(
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"sentiment-analysis",
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model=pretrained_name,
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tokenizer=pretrained_name
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)
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nlp("Jangan sampai saya telpon bos saya ya!")
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```
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## Disclaimer
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Do consider the biases which come from both the pre-trained RoBERTa model and the `SmSA` dataset that may be carried over into the results of this model.
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## Author
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Indonesian RoBERTa Base Sentiment Classifier was trained and evaluated by [Wilson Wongso](https://w11wo.github.io/). All computation and development are done on Google Colaboratory using their free GPU access.
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