
## Indonesian RoBERTa Base Sentiment Classifier

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.

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%.

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-sentiment-classifier` | 124M    | RoBERTa Base | `SmSA`                          |

## Evaluation Results

The model was trained for 5 epochs and the best model was loaded at the end.

| Epoch | Training Loss | Validation Loss | Accuracy | F1       | Precision | Recall   |
| ----- | ------------- | --------------- | -------- | -------- | --------- | -------- |
| 1     | 0.342600      | 0.213551        | 0.928571 | 0.898539 | 0.909803  | 0.890694 |
| 2     | 0.190700      | 0.213466        | 0.934127 | 0.901135 | 0.925297  | 0.882757 |
| 3     | 0.125500      | 0.219539        | 0.942857 | 0.920901 | 0.927511  | 0.915193 |
| 4     | 0.083600      | 0.235232        | 0.943651 | 0.924227 | 0.926494  | 0.922048 |
| 5     | 0.059200      | 0.262473        | 0.942063 | 0.920583 | 0.924084  | 0.917351 |

## How to Use

### As Text Classifier

```python
from transformers import pipeline

pretrained_name = "sahri/sentiment"

nlp = pipeline(
    "sentiment-analysis",
    model=pretrained_name,
    tokenizer=pretrained_name
)

nlp("tidak jelek tapi keren")
```

## Disclaimer

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.

## Author

Indonesian RoBERTa Base Sentiment Classifier was trained and evaluated by [sahri ramadhan] All computation and development are done on Google Colaboratory using their free GPU access.

{"language": "id", "license": "mit", "tags": ["indonesian-roberta-base-sentiment-classifier"], "datasets": ["indonlu"], "widget": [{"text": "tidak jelek tapi keren"}]}
transformers pytorch tf roberta text-classification indonesian-roberta-base-sentiment-classifier id dataset:indonlu arxiv:1907.11692 license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tf #roberta #text-classification #indonesian-roberta-base-sentiment-classifier #id #dataset-indonlu #arxiv-1907.11692 #license-mit #autotrain_compatible #endpoints_compatible #region-us 

Indonesian RoBERTa Base Sentiment Classifier
--------------------------------------------


Indonesian RoBERTa Base Sentiment Classifier is a sentiment-text-classification model based on the RoBERTa model. The model was originally the pre-trained Indonesian RoBERTa Base model, which is then fine-tuned on 'indonlu''s 'SmSA' dataset consisting of Indonesian comments and reviews.


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%.


Hugging Face's 'Trainer' class from the 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
-----



Evaluation Results
------------------


The model was trained for 5 epochs and the best model was loaded at the end.



How to Use
----------


### As Text Classifier


Disclaimer
----------


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.


Author
------


Indonesian RoBERTa Base Sentiment Classifier was trained and evaluated by [sahri ramadhan] All computation and development are done on Google Colaboratory using their free GPU access.



### As Text Classifier


Disclaimer
----------


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.


Author
------


Indonesian RoBERTa Base Sentiment Classifier was trained and evaluated by [sahri ramadhan] All computation and development are done on Google Colaboratory using their free GPU access.
TAGS
#transformers #pytorch #tf #roberta #text-classification #indonesian-roberta-base-sentiment-classifier #id #dataset-indonlu #arxiv-1907.11692 #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### As Text Classifier


Disclaimer
----------


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.


Author
------


Indonesian RoBERTa Base Sentiment Classifier was trained and evaluated by [sahri ramadhan] All computation and development are done on Google Colaboratory using their free GPU access.
passage: TAGS
#transformers #pytorch #tf #roberta #text-classification #indonesian-roberta-base-sentiment-classifier #id #dataset-indonlu #arxiv-1907.11692 #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### As Text Classifier


Disclaimer
----------


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.


Author
------


Indonesian RoBERTa Base Sentiment Classifier was trained and evaluated by [sahri ramadhan] All computation and development are done on Google Colaboratory using their free GPU access.