--- language: - id tags: - generated_from_trainer datasets: - indonlp/indonlu metrics: - accuracy widget: - text: Entah mengapa saya merasakan ada sesuatu yang janggal di produk ini base_model: cahya/roberta-base-indonesian-1.5G model-index: - name: roberta-base-indonesian-1.5G-sentiment-analysis-smsa results: - task: type: text-classification name: Text Classification dataset: name: indonlu type: indonlu args: smsa metrics: - type: accuracy value: 0.9261904761904762 name: Accuracy --- # roberta-base-indonesian-1.5G-sentiment-analysis-smsa This model is a fine-tuned version of [cahya/roberta-base-indonesian-1.5G](https://huggingface.co/cahya/roberta-base-indonesian-1.5G) on the indonlu dataset. It achieves the following results on the evaluation set: - Loss: 0.4294 - Accuracy: 0.9262 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1500 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6461 | 1.0 | 688 | 0.2620 | 0.9087 | | 0.2627 | 2.0 | 1376 | 0.2291 | 0.9151 | | 0.1784 | 3.0 | 2064 | 0.2891 | 0.9167 | | 0.1099 | 4.0 | 2752 | 0.3317 | 0.9230 | | 0.0857 | 5.0 | 3440 | 0.4294 | 0.9262 | | 0.0346 | 6.0 | 4128 | 0.4759 | 0.9246 | | 0.0221 | 7.0 | 4816 | 0.4946 | 0.9206 | | 0.006 | 8.0 | 5504 | 0.5823 | 0.9175 | | 0.0047 | 9.0 | 6192 | 0.5777 | 0.9159 | | 0.004 | 10.0 | 6880 | 0.5800 | 0.9175 | ### How to use this model in Transformers Library ```python from transformers import pipeline pipe = pipeline( "text-classification", model="ayameRushia/roberta-base-indonesian-1.5G-sentiment-analysis-smsa" ) pipe("Terima kasih atas bantuannya ya!") ``` ### Framework versions - Transformers 4.14.1 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3