Model overview
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.
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
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Source prices & cost analysis
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Estimate monthly base token costs
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Public benchmarks & run configuration
暂无已采集的可核验公开评分。模型未上榜不代表能力较低。
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Sources & traceability
许可证：mit。模型介绍与第三方评测均以原来源的具体版本和方法为准。