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# reward_model

This model is a fine-tuned version of [indobenchmark/indobert-base-p2](https://huggingface.co/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

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "base_model": "indobenchmark/indobert-base-p2", "model-index": [{"name": "reward_model", "results": []}]}
transformers tensorboard safetensors bert text-classification generated_from_trainer base_model:indobenchmark/indobert-base-p2 license:mit autotrain_compatible endpoints_compatible region:us