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--- |
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license: mit |
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base_model: ayameRushia/roberta-base-indonesian-sentiment-analysis-smsa |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: best_roberta_model_fold_4 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# best_roberta_model_fold_4 |
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This model is a fine-tuned version of [ayameRushia/roberta-base-indonesian-sentiment-analysis-smsa](https://huggingface.co/ayameRushia/roberta-base-indonesian-sentiment-analysis-smsa) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2676 |
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- Accuracy: 0.8546 |
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- Precision: 0.8334 |
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- Recall: 0.8336 |
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- F1: 0.8334 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| No log | 1.0 | 252 | 0.4839 | 0.8446 | 0.8238 | 0.8057 | 0.8132 | |
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| 0.4291 | 2.0 | 504 | 0.8905 | 0.8287 | 0.8060 | 0.8194 | 0.8089 | |
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| 0.4291 | 3.0 | 756 | 0.9640 | 0.8426 | 0.8142 | 0.8170 | 0.8156 | |
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| 0.1372 | 4.0 | 1008 | 1.0182 | 0.8406 | 0.8198 | 0.8103 | 0.8139 | |
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| 0.1372 | 5.0 | 1260 | 1.1472 | 0.8386 | 0.8238 | 0.8051 | 0.8134 | |
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| 0.0264 | 6.0 | 1512 | 1.2455 | 0.8367 | 0.8145 | 0.8022 | 0.8078 | |
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| 0.0264 | 7.0 | 1764 | 1.3009 | 0.8406 | 0.8221 | 0.8074 | 0.8139 | |
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| 0.0072 | 8.0 | 2016 | 1.3084 | 0.8486 | 0.8314 | 0.8187 | 0.8238 | |
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| 0.0072 | 9.0 | 2268 | 1.2815 | 0.8486 | 0.8272 | 0.8258 | 0.8265 | |
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| 0.0063 | 10.0 | 2520 | 1.2676 | 0.8546 | 0.8334 | 0.8336 | 0.8334 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.1.2 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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