t5Indo2Jawa / README.md
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---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_keras_callback
model-index:
- name: pijarcandra22/t5Indo2Jawa
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/t5Indo2Jawa
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: 2.2920
- Validation Loss: 2.0768
- Epoch: 35
## 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 |
|:----------:|:---------------:|:-----:|
| 3.5149 | 3.1567 | 0 |
| 3.3816 | 3.0397 | 1 |
| 3.2812 | 2.9518 | 2 |
| 3.1977 | 2.8751 | 3 |
| 3.1223 | 2.8078 | 4 |
| 3.0599 | 2.7507 | 5 |
| 3.0019 | 2.6979 | 6 |
| 2.9517 | 2.6513 | 7 |
| 2.9034 | 2.6121 | 8 |
| 2.8638 | 2.5756 | 9 |
| 2.8232 | 2.5391 | 10 |
| 2.7856 | 2.5089 | 11 |
| 2.7541 | 2.4786 | 12 |
| 2.7219 | 2.4499 | 13 |
| 2.6935 | 2.4256 | 14 |
| 2.6658 | 2.4010 | 15 |
| 2.6389 | 2.3762 | 16 |
| 2.6143 | 2.3550 | 17 |
| 2.5899 | 2.3313 | 18 |
| 2.5665 | 2.3156 | 19 |
| 2.5445 | 2.2939 | 20 |
| 2.5224 | 2.2750 | 21 |
| 2.5022 | 2.2569 | 22 |
| 2.4834 | 2.2410 | 23 |
| 2.4641 | 2.2220 | 24 |
| 2.4443 | 2.2091 | 25 |
| 2.4267 | 2.1948 | 26 |
| 2.4129 | 2.1796 | 27 |
| 2.3937 | 2.1657 | 28 |
| 2.3782 | 2.1523 | 29 |
| 2.3616 | 2.1385 | 30 |
| 2.3471 | 2.1267 | 31 |
| 2.3351 | 2.1110 | 32 |
| 2.3184 | 2.0988 | 33 |
| 2.3047 | 2.0871 | 34 |
| 2.2920 | 2.0768 | 35 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.14.0
- Datasets 2.15.0
- Tokenizers 0.15.0