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--- |
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license: mit |
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base_model: ayameRushia/bert-base-indonesian-1.5G-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_bert_model_fold_3 |
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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_bert_model_fold_3 |
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This model is a fine-tuned version of [ayameRushia/bert-base-indonesian-1.5G-sentiment-analysis-smsa](https://huggingface.co./ayameRushia/bert-base-indonesian-1.5G-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: 1.2908 |
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- Accuracy: 0.8386 |
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- Precision: 0.8281 |
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- Recall: 0.7986 |
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- F1: 0.8101 |
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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 | 0.6290 | 0.8008 | 0.7903 | 0.7322 | 0.7457 | |
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| 0.5166 | 2.0 | 504 | 0.6945 | 0.8068 | 0.8131 | 0.7396 | 0.7568 | |
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| 0.5166 | 3.0 | 756 | 0.9795 | 0.8108 | 0.7953 | 0.7652 | 0.7721 | |
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| 0.1546 | 4.0 | 1008 | 1.1504 | 0.8187 | 0.8024 | 0.7829 | 0.7902 | |
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| 0.1546 | 5.0 | 1260 | 1.2908 | 0.8386 | 0.8281 | 0.7986 | 0.8101 | |
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| 0.0243 | 6.0 | 1512 | 1.2868 | 0.8247 | 0.8043 | 0.7947 | 0.7988 | |
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| 0.0243 | 7.0 | 1764 | 1.4339 | 0.8307 | 0.8214 | 0.7823 | 0.7949 | |
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| 0.0077 | 8.0 | 2016 | 1.4287 | 0.8327 | 0.8222 | 0.7845 | 0.7978 | |
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| 0.0077 | 9.0 | 2268 | 1.4630 | 0.8287 | 0.8098 | 0.7842 | 0.7941 | |
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| 0.0001 | 10.0 | 2520 | 1.4618 | 0.8307 | 0.8129 | 0.7863 | 0.7966 | |
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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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