readme: ---
  tags:
  - generated_from_keras_callback
  model-index:
  - name: madatnlp/prefix-ket5-scratch
    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/prefix-ket5-scratch
  
  This model is a fine-tuned version of [madatnlp/ke-t5-math-py](https://huggingface.co/madatnlp/ke-t5-math-py) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Train Loss: 0.7214
  - Validation Loss: 0.8747
  - Epoch: 98
  
  ## 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 |
  |:----------:|:---------------:|:-----:|
  | 8.0101     | 5.1280          | 0     |
  | 4.8040     | 3.6005          | 1     |
  | 3.7550     | 2.8108          | 2     |
  | 3.2740     | 2.6402          | 3     |
  | 2.9682     | 2.3173          | 4     |
  | 2.6871     | 2.1585          | 5     |
  | 2.4782     | 2.0828          | 6     |
  | 2.3507     | 1.9557          | 7     |
  | 2.2131     | 1.8513          | 8     |
  | 2.1235     | 1.6324          | 9     |
  | 2.0157     | 1.6270          | 10    |
  | 1.9722     | 1.6217          | 11    |
  | 1.8733     | 1.5436          | 12    |
  | 1.8680     | 1.5872          | 13    |
  | 1.8365     | 1.6040          | 14    |
  | 1.7528     | 1.5049          | 15    |
  | 1.7411     | 1.4754          | 16    |
  | 1.6733     | 1.4409          | 17    |
  | 1.6544     | 1.4230          | 18    |
  | 1.6271     | 1.4556          | 19    |
  | 1.5658     | 1.3797          | 20    |
  | 1.5774     | 1.3269          | 21    |
  | 1.5150     | 1.3108          | 22    |
  | 1.5057     | 1.3785          | 23    |
  | 1.4605     | 1.3114          | 24    |
  | 1.4702     | 1.2618          | 25    |
  | 1.4220     | 1.2164          | 26    |
  | 1.4194     | 1.2409          | 27    |
  | 1.3942     | 1.2603          | 28    |
  | 1.3921     | 1.3010          | 29    |
  | 1.3645     | 1.1850          | 30    |
  | 1.3336     | 1.1273          | 31    |
  | 1.3499     | 1.1533          | 32    |
  | 1.3022     | 1.1683          | 33    |
  | 1.2990     | 1.1403          | 34    |
  | 1.2876     | 1.1241          | 35    |
  | 1.2479     | 1.0957          | 36    |
  | 1.2441     | 1.1989          | 37    |
  | 1.2464     | 1.1416          | 38    |
  | 1.2353     | 1.0636          | 39    |
  | 1.2152     | 1.1136          | 40    |
  | 1.2212     | 1.0635          | 41    |
  | 1.1892     | 1.0818          | 42    |
  | 1.1959     | 1.1041          | 43    |
  | 1.1957     | 1.0912          | 44    |
  | 1.1542     | 1.0949          | 45    |
  | 1.1403     | 1.1272          | 46    |
  | 1.1396     | 1.1169          | 47    |
  | 1.1149     | 1.0606          | 48    |
  | 1.1238     | 1.0610          | 49    |
  | 1.1246     | 1.0234          | 50    |
  | 1.0971     | 0.9865          | 51    |
  | 1.0883     | 1.0568          | 52    |
  | 1.0774     | 1.0099          | 53    |
  | 1.0581     | 1.0023          | 54    |
  | 1.0680     | 1.0197          | 55    |
  | 1.0682     | 0.9835          | 56    |
  | 1.0390     | 0.9789          | 57    |
  | 1.0480     | 1.0217          | 58    |
  | 1.0273     | 0.9622          | 59    |
  | 1.0062     | 1.0174          | 60    |
  | 1.0088     | 0.9612          | 61    |
  | 0.9909     | 0.9998          | 62    |
  | 0.9821     | 1.0115          | 63    |
  | 0.9752     | 0.9712          | 64    |
  | 0.9816     | 0.9677          | 65    |
  | 0.9569     | 0.9503          | 66    |
  | 0.9521     | 1.0052          | 67    |
  | 0.9384     | 0.9752          | 68    |
  | 0.9468     | 0.9767          | 69    |
  | 0.9241     | 1.0076          | 70    |
  | 0.9211     | 0.9414          | 71    |
  | 0.9166     | 1.0294          | 72    |
  | 0.9044     | 0.9772          | 73    |
  | 0.9025     | 0.9273          | 74    |
  | 0.8909     | 1.0077          | 75    |
  | 0.8831     | 0.9292          | 76    |
  | 0.8702     | 0.9320          | 77    |
  | 0.8644     | 0.9879          | 78    |
  | 0.8599     | 0.9027          | 79    |
  | 0.8434     | 0.9197          | 80    |
  | 0.8561     | 0.9447          | 81    |
  | 0.8330     | 0.9730          | 82    |
  | 0.8328     | 0.9137          | 83    |
  | 0.8221     | 0.9232          | 84    |
  | 0.8166     | 0.9115          | 85    |
  | 0.8025     | 0.9530          | 86    |
  | 0.8070     | 0.9270          | 87    |
  | 0.7968     | 0.8474          | 88    |
  | 0.7880     | 0.9171          | 89    |
  | 0.7834     | 0.8668          | 90    |
  | 0.7786     | 0.9049          | 91    |
  | 0.7595     | 0.9348          | 92    |
  | 0.7573     | 0.8826          | 93    |
  | 0.7505     | 0.8765       