--- language: id tags: - indonesian-roberta-base-sentiment-classifier license: mit datasets: - indonlu widget: - text: "tidak jelek tapi keren" --- ## 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.