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 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 0.23523…
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