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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_5 |
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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_5 |
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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: 1.1930 |
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- Accuracy: 0.8625 |
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- Precision: 0.8332 |
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- Recall: 0.8376 |
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- F1: 0.8353 |
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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 | 0.5996 | 0.8526 | 0.8428 | 0.7584 | 0.7817 | |
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| 0.4616 | 2.0 | 504 | 0.7400 | 0.8207 | 0.7814 | 0.7663 | 0.7684 | |
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| 0.4616 | 3.0 | 756 | 0.7731 | 0.8586 | 0.8327 | 0.8058 | 0.8173 | |
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| 0.1366 | 4.0 | 1008 | 0.9903 | 0.8446 | 0.8110 | 0.8439 | 0.8182 | |
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| 0.1366 | 5.0 | 1260 | 1.0818 | 0.8506 | 0.8198 | 0.8084 | 0.8133 | |
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| 0.0362 | 6.0 | 1512 | 1.1802 | 0.8486 | 0.8164 | 0.8199 | 0.8178 | |
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| 0.0362 | 7.0 | 1764 | 1.1920 | 0.8586 | 0.8333 | 0.8135 | 0.8224 | |
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| 0.0119 | 8.0 | 2016 | 1.2077 | 0.8546 | 0.8206 | 0.8323 | 0.8259 | |
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| 0.0119 | 9.0 | 2268 | 1.2426 | 0.8526 | 0.8178 | 0.8323 | 0.8244 | |
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| 0.0034 | 10.0 | 2520 | 1.1930 | 0.8625 | 0.8332 | 0.8376 | 0.8353 | |
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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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