reward_model
This model is a fine-tuned version of indobenchmark/indobert-base-p2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6126
- Accuracy: 0.8927
- F1: 0.8906
- Precision: 0.8964
- Recall: 0.8878
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-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
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| No log | 0.56 | 50 | 0.3455 | 0.8757 | 0.8736 | 0.8780 | 0.8713 |
| No log | 1.12 | 100 | 0.3013 | 0.8701 | 0.8687 | 0.8692 | 0.8683 |
| No log | 1.69 | 150 | 0.3773 | 0.8644 | 0.8616 | 0.8683 | 0.8588 |
| No log | 2.25 | 200 | 0.3923 | 0.8927 | 0.8906 | 0.8964 | 0.8878 |
| No log | 2.81 | 250 | 0.3634 | 0.8927 | 0.8913 | 0.8931 | 0.8900 |
| No log | 3.37 | 300 | 0.4554 | 0.8983 | 0.8971 | 0.8982 | 0.8963 |
| No log | 3.93 | 350 | 0.5317 | 0.8870 | 0.8851 | 0.8896 | 0.8827 |
| No log | 4.49 | 400 | 0.5834 | 0.8870 | 0.8851 | 0.8896 | 0.8827 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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Base model
indobenchmark/indobert-base-p2