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---
language: id
tags:
- indonesian-roberta-base-indolem-sentiment-classifier-fold-0
license: mit
datasets:
- indolem
widget:
- text: "Pelayanan hotel ini sangat baik."
---
## Indonesian RoBERTa Base IndoLEM Sentiment Classifier
Indonesian RoBERTa Base IndoLEM 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 [`indolem`](https://indolem.github.io/)'s [Sentiment Analysis](https://github.com/indolem/indolem/tree/main/sentiment) dataset consisting of Indonesian tweets and hotel reviews (Koto et al., 2020).
A 5-fold cross-validation experiment was performed, with splits provided by the original dataset authors. This model was trained on fold 0. You can find models trained on [fold 0](https://huggingface.co/w11wo/indonesian-roberta-base-indolem-sentiment-classifier-fold-0), [fold 1](https://huggingface.co/w11wo/indonesian-roberta-base-indolem-sentiment-classifier-fold-1), [fold 2](https://huggingface.co/w11wo/indonesian-roberta-base-indolem-sentiment-classifier-fold-2), [fold 3](https://huggingface.co/w11wo/indonesian-roberta-base-indolem-sentiment-classifier-fold-3), and [fold 4](https://huggingface.co/w11wo/indonesian-roberta-base-indolem-sentiment-classifier-fold-4), in their respective links.
On **fold 0**, the model achieved an F1 of 86.42% on dev/validation and 83.12% on test. On all **5 folds**, the models achieved an average F1 of 84.14% on dev/validation and 84.64% on test.
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.
## Model
| Model | #params | Arch. | Training/Validation data (text) |
| ------------------------------------------------------------- | ------- | ------------ | ------------------------------- |
| `indonesian-roberta-base-indolem-sentiment-classifier-fold-0` | 124M | RoBERTa Base | `IndoLEM`'s Sentiment Analysis |
## Evaluation Results
The model was trained for 10 epochs and the best model was loaded at the end.
| Epoch | Training Loss | Validation Loss | Accuracy | F1 | Precision | Recall |
| ----- | ------------- | --------------- | -------- | -------- | --------- | -------- |
| 1 | 0.563500 | 0.420457 | 0.796992 | 0.626728 | 0.680000 | 0.581197 |
| 2 | 0.293600 | 0.281360 | 0.884712 | 0.811475 | 0.779528 | 0.846154 |
| 3 | 0.163000 | 0.267922 | 0.904762 | 0.844262 | 0.811024 | 0.880342 |
| 4 | 0.090200 | 0.335411 | 0.899749 | 0.838710 | 0.793893 | 0.888889 |
| 5 | 0.065200 | 0.462526 | 0.897243 | 0.835341 | 0.787879 | 0.888889 |
| 6 | 0.039200 | 0.423001 | 0.912281 | 0.859438 | 0.810606 | 0.914530 |
| 7 | 0.025300 | 0.452100 | 0.912281 | 0.859438 | 0.810606 | 0.914530 |
| 8 | 0.010400 | 0.525200 | 0.914787 | 0.855932 | 0.848739 | 0.863248 |
| 9 | 0.007100 | 0.513585 | 0.909774 | 0.850000 | 0.829268 | 0.871795 |
| 10 | 0.007200 | 0.537254 | 0.917293 | 0.864198 | 0.833333 | 0.897436 |
## How to Use
### As Text Classifier
```python
from transformers import pipeline
pretrained_name = "w11wo/indonesian-roberta-base-indolem-sentiment-classifier-fold-0"
nlp = pipeline(
"sentiment-analysis",
model=pretrained_name,
tokenizer=pretrained_name
)
nlp("Pelayanan hotel ini sangat baik.")
```
## Disclaimer
Do consider the biases which come from both the pre-trained RoBERTa model and `IndoLEM`'s Sentiment Analysis dataset that may be carried over into the results of this model.
## Author
Indonesian RoBERTa Base IndoLEM 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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