w11wo
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indonesian-roberta-base-sentiment-classifier

@@ -13,7 +13,7 @@ widget:
 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 93.88% and F1-macro of 91.57%. On the benchmark test set, the model achieved an accuracy of 90.00% and F1-macro of 85.97%.
 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.
@@ -29,11 +29,11 @@ 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     | 0.346100      | 0.263456        | 0.915079 | 0.888680 | 0.877023  | 0.903502 |
-| 2     | 0.175200      | 0.215166        | 0.930952 | 0.908246 | 0.918557  | 0.898842 |
-| 3     | 0.111700      | 0.227525        | 0.932540 | 0.901823 | 0.916049  | 0.891263 |
-| 4     | 0.071800      | 0.244867        | 0.938889 | 0.915714 | 0.923105  | 0.909921 |
-| 5     | 0.055000      | 0.262004        | 0.935714 | 0.906755 | 0.918607  | 0.898044 |
 ## How to Use

 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.
 | 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