---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: madatnlp/gamza-bart-for-kormath
  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/gamza-bart-for-kormath

This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co/gogamza/kobart-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0460
- Validation Loss: 0.3173
- Epoch: 52

## 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': 'Adam', 'learning_rate': 1e-04, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32

### Training results

| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 4.3868     | 1.8720          | 0     |
| 1.5091     | 1.0768          | 1     |
| 1.0238     | 0.8972          | 2     |
| 0.8960     | 0.8375          | 3     |
| 0.8087     | 0.7618          | 4     |
| 0.7656     | 0.7196          | 5     |
| 0.7433     | 0.7023          | 6     |
| 0.7309     | 0.6992          | 7     |
| 0.6773     | 0.7008          | 8     |
| 0.6659     | 0.6752          | 9     |
| 0.6607     | 0.7347          | 10    |
| 0.6370     | 0.6033          | 11    |
| 0.6037     | 0.6806          | 12    |
| 0.5957     | 0.6370          | 13    |
| 0.5740     | 0.6413          | 14    |
| 0.5005     | 0.5002          | 15    |
| 0.4385     | 0.4821          | 16    |
| 0.5042     | 0.4898          | 17    |
| 0.4445     | 0.5218          | 18    |
| 0.3990     | 0.4771          | 19    |
| 0.3497     | 0.4173          | 20    |
| 0.3294     | 0.4232          | 21    |
| 0.3019     | 0.3936          | 22    |
| 0.2868     | 0.4212          | 23    |
| 0.2565     | 0.3401          | 24    |
| 0.2404     | 0.3912          | 25    |
| 0.2235     | 0.3484          | 26    |
| 0.1976     | 0.3357          | 27    |
| 0.1998     | 0.3392          | 28    |
| 0.1788     | 0.3075          | 29    |
| 0.1705     | 0.3615          | 30    |
| 0.1642     | 0.3466          | 31    |
| 0.1510     | 0.3325          | 32    |
| 0.1402     | 0.3729          | 33    |
| 0.1342     | 0.3425          | 34    |
| 0.1276     | 0.3039          | 35    |
| 0.1093     | 0.3276          | 36    |
| 0.1048     | 0.3034          | 37    |
| 0.0951     | 0.3472          | 38    |
| 0.0971     | 0.3407          | 39    |
| 0.0849     | 0.3516          | 40    |
| 0.0868     | 0.2757          | 41    |
| 0.0868     | 0.2383          | 42    |
| 0.0766     | 0.3127          | 43    |
| 0.0734     | 0.3324          | 44    |
| 0.0694     | 0.3654          | 45    |
| 0.0626     | 0.2692          | 46    |
| 0.0584     | 0.2403          | 47    |
| 0.0565     | 0.3741          | 48    |
| 0.0567     | 0.2839          | 49    |
| 0.0523     | 0.2387          | 50    |
| 0.0525     | 0.2629          | 51    |
| 0.0460     | 0.3173          | 52    |


### Framework versions

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