---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_keras_callback
model-index:
- name: pijarcandra22/t5Sunda2Indo
  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. -->

# pijarcandra22/t5Sunda2Indo

This model is a fine-tuned version of [t5-small](https://huggingface.co./t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.6406
- Validation Loss: 1.5932
- Epoch: 140

## 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 |
|:----------:|:---------------:|:-----:|
| 3.9668     | 3.4054          | 0     |
| 3.5451     | 3.1460          | 1     |
| 3.3386     | 2.9773          | 2     |
| 3.1917     | 2.8549          | 3     |
| 3.0808     | 2.7568          | 4     |
| 2.9921     | 2.6780          | 5     |
| 2.9187     | 2.6135          | 6     |
| 2.8536     | 2.5547          | 7     |
| 2.8010     | 2.5040          | 8     |
| 2.7482     | 2.4601          | 9     |
| 2.7056     | 2.4227          | 10    |
| 2.6709     | 2.3870          | 11    |
| 2.6325     | 2.3554          | 12    |
| 2.6016     | 2.3233          | 13    |
| 2.5685     | 2.2965          | 14    |
| 2.5407     | 2.2710          | 15    |
| 2.5158     | 2.2486          | 16    |
| 2.4905     | 2.2248          | 17    |
| 2.4680     | 2.2037          | 18    |
| 2.4459     | 2.1856          | 19    |
| 2.4257     | 2.1684          | 20    |
| 2.4045     | 2.1495          | 21    |
| 2.3861     | 2.1325          | 22    |
| 2.3676     | 2.1189          | 23    |
| 2.3479     | 2.1028          | 24    |
| 2.3333     | 2.0873          | 25    |
| 2.3192     | 2.0743          | 26    |
| 2.3035     | 2.0647          | 27    |
| 2.2889     | 2.0504          | 28    |
| 2.2742     | 2.0395          | 29    |
| 2.2608     | 2.0285          | 30    |
| 2.2471     | 2.0166          | 31    |
| 2.2344     | 2.0078          | 32    |
| 2.2211     | 1.9999          | 33    |
| 2.2086     | 1.9893          | 34    |
| 2.1965     | 1.9790          | 35    |
| 2.1879     | 1.9724          | 36    |
| 2.1774     | 1.9637          | 37    |
| 2.1663     | 1.9537          | 38    |
| 2.1573     | 1.9461          | 39    |
| 2.1470     | 1.9389          | 40    |
| 2.1344     | 1.9329          | 41    |
| 2.1259     | 1.9257          | 42    |
| 2.1192     | 1.9158          | 43    |
| 2.1098     | 1.9092          | 44    |
| 2.0993     | 1.9021          | 45    |
| 2.0930     | 1.8970          | 46    |
| 2.0831     | 1.8909          | 47    |
| 2.0729     | 1.8845          | 48    |
| 2.0669     | 1.8799          | 49    |
| 2.0587     | 1.8746          | 50    |
| 2.0519     | 1.8662          | 51    |
| 2.0446     | 1.8605          | 52    |
| 2.0338     | 1.8552          | 53    |
| 2.0297     | 1.8494          | 54    |
| 2.0207     | 1.8441          | 55    |
| 2.0151     | 1.8404          | 56    |
| 2.0116     | 1.8346          | 57    |
| 2.0029     | 1.8286          | 58    |
| 1.9942     | 1.8243          | 59    |
| 1.9894     | 1.8177          | 60    |
| 1.9820     | 1.8145          | 61    |
| 1.9753     | 1.8100          | 62    |
| 1.9698     | 1.8054          | 63    |
| 1.9635     | 1.8001          | 64    |
| 1.9588     | 1.7963          | 65    |
| 1.9533     | 1.7895          | 66    |
| 1.9443     | 1.7888          | 67    |
| 1.9381     | 1.7846          | 68    |
| 1.9320     | 1.7806          | 69    |
| 1.9281     | 1.7755          | 70    |
| 1.9232     | 1.7697          | 71    |
| 1.9141     | 1.7672          | 72    |
| 1.9128     | 1.7655          | 73    |
| 1.9041     | 1.7611          | 74    |
| 1.8987     | 1.7558          | 75    |
| 1.8955     | 1.7498          | 76    |
| 1.8874     | 1.7493          | 77    |
| 1.8845     | 1.7433          | 78    |
| 1.8793     | 1.7403          | 79    |
| 1.8723     | 1.7370          | 80    |
| 1.8669     | 1.7319          | 81    |
| 1.8626     | 1.7323          | 82    |
| 1.8593     | 1.7268          | 83    |
| 1.8540     | 1.7235          | 84    |
| 1.8504     | 1.7204          | 85    |
| 1.8468     | 1.7180          | 86    |
| 1.8398     | 1.7130          | 87    |
| 1.8358     | 1.7088          | 88    |
| 1.8321     | 1.7081          | 89    |
| 1.8271     | 1.7042          | 90    |
| 1.8203     | 1.7016          | 91    |
| 1.8189     | 1.6985          | 92    |
| 1.8117     | 1.6967          | 93    |
| 1.8089     | 1.6924          | 94    |
| 1.8035     | 1.6898          | 95    |
| 1.7984     | 1.6904          | 96    |
| 1.7957     | 1.6836          | 97    |
| 1.7925     | 1.6833          | 98    |
| 1.7880     | 1.6786          | 99    |
| 1.7816     | 1.6770          | 100   |
| 1.7772     | 1.6739          | 101   |
| 1.7754     | 1.6733          | 102   |
| 1.7712     | 1.6678          | 103   |
| 1.7677     | 1.6638          | 104   |
| 1.7641     | 1.6627          | 105   |
| 1.7601     | 1.6609          | 106   |
| 1.7573     | 1.6585          | 107   |
| 1.7516     | 1.6559          | 108   |
| 1.7478     | 1.6523          | 109   |
| 1.7443     | 1.6523          | 110   |
| 1.7406     | 1.6498          | 111   |
| 1.7361     | 1.6475          | 112   |
| 1.7326     | 1.6435          | 113   |
| 1.7285     | 1.6422          | 114   |
| 1.7244     | 1.6398          | 115   |
| 1.7205     | 1.6386          | 116   |
| 1.7187     | 1.6347          | 117   |
| 1.7157     | 1.6335          | 118   |
| 1.7113     | 1.6317          | 119   |
| 1.7094     | 1.6308          | 120   |
| 1.7074     | 1.6267          | 121   |
| 1.7007     | 1.6252          | 122   |
| 1.6965     | 1.6241          | 123   |
| 1.6931     | 1.6231          | 124   |
| 1.6913     | 1.6201          | 125   |
| 1.6862     | 1.6174          | 126   |
| 1.6833     | 1.6176          | 127   |
| 1.6827     | 1.6122          | 128   |
| 1.6774     | 1.6127          | 129   |
| 1.6726     | 1.6119          | 130   |
| 1.6698     | 1.6079          | 131   |
| 1.6663     | 1.6077          | 132   |
| 1.6631     | 1.6055          | 133   |
| 1.6617     | 1.6043          | 134   |
| 1.6573     | 1.6019          | 135   |
| 1.6549     | 1.5994          | 136   |
| 1.6514     | 1.5990          | 137   |
| 1.6484     | 1.5965          | 138   |
| 1.6490     | 1.5942          | 139   |
| 1.6406     | 1.5932          | 140   |


### Framework versions

- Transformers 4.35.2
- TensorFlow 2.14.0
- Datasets 2.15.0
- Tokenizers 0.15.0