readme: ---
  license: mit
  tags:
  - generated_from_keras_callback
  model-index:
  - name: madatnlp/gamza-bart-for-kormath128
    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-kormath128
  
  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: 0.1429
  - Validation Loss: 0.3575
  - Epoch: 42
  
  ## 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 |
  |:----------:|:---------------:|:-----:|
  | 5.9513     | 3.2241          | 0     |
  | 2.6808     | 1.8567          | 1     |
  | 1.6770     | 1.2966          | 2     |
  | 1.2253     | 1.0402          | 3     |
  | 1.0279     | 0.9159          | 4     |
  | 0.9241     | 0.8158          | 5     |
  | 0.8570     | 0.8047          | 6     |
  | 0.8130     | 0.7684          | 7     |
  | 0.7771     | 0.7817          | 8     |
  | 0.7522     | 0.7653          | 9     |
  | 0.7318     | 0.6813          | 10    |
  | 0.7111     | 0.6535          | 11    |
  | 0.6916     | 0.6719          | 12    |
  | 0.6901     | 0.7191          | 13    |
  | 0.6551     | 0.6330          | 14    |
  | 0.6495     | 0.6242          | 15    |
  | 0.6258     | 0.6048          | 16    |
  | 0.6184     | 0.6590          | 17    |
  | 0.6055     | 0.6622          | 18    |
  | 0.5946     | 0.6377          | 19    |
  | 0.5807     | 0.5994          | 20    |
  | 0.5781     | 0.5797          | 21    |
  | 0.5644     | 0.6154          | 22    |
  | 0.5466     | 0.5777          | 23    |
  | 0.5417     | 0.6324          | 24    |
  | 0.5204     | 0.5763          | 25    |
  | 0.5081     | 0.5751          | 26    |
  | 0.4923     | 0.5908          | 27    |
  | 0.4616     | 0.5433          | 28    |
  | 0.4238     | 0.4823          | 29    |
  | 0.3765     | 0.4474          | 30    |
  | 0.3447     | 0.4306          | 31    |
  | 0.3156     | 0.3817          | 32    |
  | 0.2832     | 0.3824          | 33    |
  | 0.2632     | 0.3204          | 34    |
  | 0.2365     | 0.3539          | 35    |
  | 0.2179     | 0.3162          | 36    |
  | 0.2024     | 0.3385          | 37    |
  | 0.1860     | 0.3367          | 38    |
  | 0.1801     | 0.3019          | 39    |
  | 0.1629     | 0.3045          | 40    |
  | 0.1533     | 0.2567          | 41    |
  | 0.1429     | 0.3575          | 42    |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - TensorFlow 2.8.0
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  