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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_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_bert_model_fold_5 |
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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: 1.0532 |
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- Accuracy: 0.8586 |
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- Precision: 0.8342 |
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- Recall: 0.8005 |
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- F1: 0.8134 |
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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 | 0.5880 | 0.8048 | 0.8281 | 0.6620 | 0.6694 | |
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| 0.5304 | 2.0 | 504 | 0.8974 | 0.7928 | 0.7497 | 0.7582 | 0.7525 | |
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| 0.5304 | 3.0 | 756 | 1.0145 | 0.7928 | 0.7483 | 0.7455 | 0.7418 | |
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| 0.1872 | 4.0 | 1008 | 1.0229 | 0.8227 | 0.7856 | 0.7561 | 0.7678 | |
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| 0.1872 | 5.0 | 1260 | 1.0532 | 0.8586 | 0.8342 | 0.8005 | 0.8134 | |
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| 0.037 | 6.0 | 1512 | 1.2624 | 0.8347 | 0.7927 | 0.7997 | 0.7957 | |
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| 0.037 | 7.0 | 1764 | 1.3287 | 0.8227 | 0.7806 | 0.7951 | 0.7870 | |
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| 0.0076 | 8.0 | 2016 | 1.3369 | 0.8347 | 0.8064 | 0.7747 | 0.7863 | |
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| 0.0076 | 9.0 | 2268 | 1.3292 | 0.8406 | 0.8093 | 0.7883 | 0.7974 | |
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| 0.0002 | 10.0 | 2520 | 1.3507 | 0.8347 | 0.7970 | 0.7844 | 0.7901 | |
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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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