---
tags:
- generated_from_keras_callback
model-index:
- name: madatnlp/kor-math-roberta-finetune
  results: []
---

<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->

# madatnlp/kor-math-roberta-finetune

This model is a fine-tuned version of [klue/roberta-base](https://huggingface.co/klue/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.7393
- Validation Loss: 0.7801
- Epoch: 45

## 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32

### Training results

| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.6637     | 2.2056          | 0     |
| 2.0866     | 1.6338          | 1     |
| 1.7565     | 1.4797          | 2     |
| 1.5604     | 1.4183          | 3     |
| 1.4554     | 1.2678          | 4     |
| 1.3784     | 1.2015          | 5     |
| 1.2908     | 0.9909          | 6     |
| 1.2265     | 1.1029          | 7     |
| 1.2524     | 1.0225          | 8     |
| 1.1722     | 1.1167          | 9     |
| 1.1396     | 0.9434          | 10    |
| 1.1002     | 1.0683          | 11    |
| 1.0693     | 1.0637          | 12    |
| 1.0896     | 0.9698          | 13    |
| 1.0590     | 0.9038          | 14    |
| 1.0357     | 0.9302          | 15    |
| 1.0557     | 0.8600          | 16    |
| 1.0036     | 0.7892          | 17    |
| 1.0283     | 0.9425          | 18    |
| 0.9883     | 0.7521          | 19    |
| 0.9797     | 0.7950          | 20    |
| 0.9511     | 0.8072          | 21    |
| 0.9023     | 0.8780          | 22    |
| 0.9074     | 0.8745          | 23    |
| 0.9324     | 0.7436          | 24    |
| 0.8921     | 0.9032          | 25    |
| 0.9098     | 0.8011          | 26    |
| 0.8843     | 0.8527          | 27    |
| 0.8756     | 0.7803          | 28    |
| 0.8759     | 0.8922          | 29    |
| 0.8472     | 0.8286          | 30    |
| 0.8156     | 0.7801          | 31    |
| 0.8401     | 0.7904          | 32    |
| 0.8400     | 0.9007          | 33    |
| 0.8368     | 0.6959          | 34    |
| 0.8429     | 0.6646          | 35    |
| 0.8496     | 0.7386          | 36    |
| 0.8168     | 0.7544          | 37    |
| 0.7927     | 0.8467          | 38    |
| 0.8025     | 0.7375          | 39    |
| 0.7893     | 0.7091          | 40    |
| 0.7762     | 0.6758          | 41    |
| 0.7516     | 0.8641          | 42    |
| 0.7645     | 0.7587          | 43    |
| 0.7790     | 0.7386          | 44    |
| 0.7393     | 0.7801          | 45    |


### Framework versions

- Transformers 4.18.0
- TensorFlow 2.8.0
- Datasets 2.1.0
- Tokenizers 0.12.1