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T5 (base) finetuned-turk-text-simplification
This model is a fine-tuned version of t5-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0913
- Rouge2 Precision: 0.6559
- Rouge2 Recall: 0.4369
- Rouge2 Fmeasure: 0.5034
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|---|---|---|---|---|---|---|
| 0.0955 | 1.0 | 2000 | 0.0948 | 0.656 | 0.4404 | 0.5063 |
| 0.0872 | 2.0 | 4000 | 0.0913 | 0.6559 | 0.4369 | 0.5034 |
| 0.0877 | 3.0 | 6000 | 0.0913 | 0.6559 | 0.4369 | 0.5034 |
| 0.0881 | 4.0 | 8000 | 0.0913 | 0.6559 | 0.4369 | 0.5034 |
| 0.0867 | 5.0 | 10000 | 0.0913 | 0.6559 | 0.4369 | 0.5034 |
Framework versions
- Transformers 4.22.0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
