---
license: apache-2.0
base_model: mBart
tags:
- generated_from_keras_callback
model-index:
- name: bedus-creation/t5-small-dataset-i-eng-lim
  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. -->

# bedus-creation/t5-small-dataset-i-eng-lim

This model is a fine-tuned version of [mBart](https://huggingface.co/mBart) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.0827
- Validation Loss: 3.6942
- 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': 'AdamWeightDecay', 'learning_rate': 2e-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 |
|:----------:|:---------------:|:-----:|
| 6.7704     | 5.9134          | 0     |
| 5.9151     | 5.3799          | 1     |
| 5.4499     | 5.1064          | 2     |
| 5.1740     | 4.9542          | 3     |
| 4.9818     | 4.8383          | 4     |
| 4.8642     | 4.7334          | 5     |
| 4.7371     | 4.6535          | 6     |
| 4.6666     | 4.5845          | 7     |
| 4.5665     | 4.5088          | 8     |
| 4.5159     | 4.4424          | 9     |
| 4.4477     | 4.4099          | 10    |
| 4.3651     | 4.3525          | 11    |
| 4.3303     | 4.3177          | 12    |
| 4.2885     | 4.2668          | 13    |
| 4.2273     | 4.2247          | 14    |
| 4.2048     | 4.1953          | 15    |
| 4.1743     | 4.1945          | 16    |
| 4.1337     | 4.1519          | 17    |
| 4.1091     | 4.1306          | 18    |
| 4.0812     | 4.1167          | 19    |
| 4.0489     | 4.1000          | 20    |
| 4.0184     | 4.0721          | 21    |
| 4.0134     | 4.0486          | 22    |
| 3.9739     | 4.0406          | 23    |
| 3.9381     | 4.0355          | 24    |
| 3.9363     | 4.0174          | 25    |
| 3.9039     | 4.0123          | 26    |
| 3.8887     | 3.9893          | 27    |
| 3.8742     | 3.9748          | 28    |
| 3.8520     | 3.9935          | 29    |
| 3.8403     | 3.9554          | 30    |
| 3.8126     | 3.9550          | 31    |
| 3.7920     | 3.9503          | 32    |
| 3.7767     | 3.9482          | 33    |
| 3.7509     | 3.9106          | 34    |
| 3.7589     | 3.9050          | 35    |
| 3.7469     | 3.8956          | 36    |
| 3.7217     | 3.8912          | 37    |
| 3.7002     | 3.8869          | 38    |
| 3.6859     | 3.8909          | 39    |
| 3.6904     | 3.8719          | 40    |
| 3.6422     | 3.8643          | 41    |
| 3.6361     | 3.8637          | 42    |
| 3.6395     | 3.8547          | 43    |
| 3.6267     | 3.8349          | 44    |
| 3.6040     | 3.8333          | 45    |
| 3.5906     | 3.8254          | 46    |
| 3.6037     | 3.8258          | 47    |
| 3.5775     | 3.8237          | 48    |
| 3.5683     | 3.8197          | 49    |
| 3.5499     | 3.8086          | 50    |
| 3.5351     | 3.7988          | 51    |
| 3.5217     | 3.8263          | 52    |
| 3.5196     | 3.7971          | 53    |
| 3.4942     | 3.7985          | 54    |
| 3.4878     | 3.7955          | 55    |
| 3.4725     | 3.7823          | 56    |
| 3.4716     | 3.7667          | 57    |
| 3.4676     | 3.7688          | 58    |
| 3.4488     | 3.7423          | 59    |
| 3.4474     | 3.7587          | 60    |
| 3.4346     | 3.7488          | 61    |
| 3.4313     | 3.7616          | 62    |
| 3.4023     | 3.7542          | 63    |
| 3.3851     | 3.7517          | 64    |
| 3.4024     | 3.7343          | 65    |
| 3.3738     | 3.7339          | 66    |
| 3.3656     | 3.7446          | 67    |
| 3.3645     | 3.7267          | 68    |
| 3.3614     | 3.7265          | 69    |
| 3.3399     | 3.7409          | 70    |
| 3.3287     | 3.7133          | 71    |
| 3.3140     | 3.7288          | 72    |
| 3.2964     | 3.7047          | 73    |
| 3.2872     | 3.7173          | 74    |
| 3.2904     | 3.7150          | 75    |
| 3.2749     | 3.7100          | 76    |
| 3.2713     | 3.7086          | 77    |
| 3.2675     | 3.7073          | 78    |
| 3.2569     | 3.6901          | 79    |
| 3.2469     | 3.6959          | 80    |
| 3.2353     | 3.7033          | 81    |
| 3.2394     | 3.7201          | 82    |
| 3.2163     | 3.7068          | 83    |
| 3.2121     | 3.6795          | 84    |
| 3.1908     | 3.7045          | 85    |
| 3.1841     | 3.7177          | 86    |
| 3.1706     | 3.7030          | 87    |
| 3.1591     | 3.6963          | 88    |
| 3.1646     | 3.6930          | 89    |
| 3.1293     | 3.7010          | 90    |
| 3.1635     | 3.6928          | 91    |
| 3.1310     | 3.6846          | 92    |
| 3.1286     | 3.6802          | 93    |
| 3.1235     | 3.6716          | 94    |
| 3.1133     | 3.6609          | 95    |
| 3.1135     | 3.6744          | 96    |
| 3.0875     | 3.6750          | 97    |
| 3.0827     | 3.6942          | 98    |


### Framework versions

- Transformers 4.33.2
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.13.3