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---
license: apache-2.0
base_model: Buseak/md_mt5_1911_v16_deneme
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: md_mt5_1911_v18_retrain
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# md_mt5_1911_v18_retrain
This model is a fine-tuned version of [Buseak/md_mt5_1911_v16_deneme](https://huggingface.co/Buseak/md_mt5_1911_v16_deneme) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1754
- Bleu: 0.7623
- Gen Len: 18.7866
## 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:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|
| 0.6365 | 1.0 | 1250 | 0.3435 | 0.6788 | 18.7694 |
| 0.5732 | 2.0 | 2500 | 0.3064 | 0.7037 | 18.7644 |
| 0.5375 | 3.0 | 3750 | 0.2819 | 0.7114 | 18.7706 |
| 0.4912 | 4.0 | 5000 | 0.2549 | 0.7237 | 18.77 |
| 0.4648 | 5.0 | 6250 | 0.2394 | 0.7354 | 18.772 |
| 0.4321 | 6.0 | 7500 | 0.2245 | 0.7335 | 18.7762 |
| 0.4159 | 7.0 | 8750 | 0.2131 | 0.7446 | 18.778 |
| 0.4044 | 8.0 | 10000 | 0.2030 | 0.7478 | 18.7776 |
| 0.3889 | 9.0 | 11250 | 0.1963 | 0.7496 | 18.7852 |
| 0.3798 | 10.0 | 12500 | 0.1896 | 0.7524 | 18.7834 |
| 0.3733 | 11.0 | 13750 | 0.1836 | 0.757 | 18.7854 |
| 0.3623 | 12.0 | 15000 | 0.1803 | 0.7596 | 18.7852 |
| 0.3583 | 13.0 | 16250 | 0.1775 | 0.7618 | 18.7878 |
| 0.3643 | 14.0 | 17500 | 0.1758 | 0.7616 | 18.7828 |
| 0.3609 | 15.0 | 18750 | 0.1754 | 0.7623 | 18.7866 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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