T5 (small) finetuned-turk-text-simplification
This model is a fine-tuned version of t5-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1001
- Rouge2 Precision: 0.6825
- Rouge2 Recall: 0.4542
- Rouge2 Fmeasure: 0.5221
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: 32
- eval_batch_size: 32
- 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 |
|---|---|---|---|---|---|---|
| 0.4318 | 1.0 | 500 | 0.1053 | 0.682 | 0.4533 | 0.5214 |
| 0.0977 | 2.0 | 1000 | 0.1019 | 0.683 | 0.4545 | 0.5225 |
| 0.0938 | 3.0 | 1500 | 0.1010 | 0.6828 | 0.4547 | 0.5226 |
| 0.0916 | 4.0 | 2000 | 0.1003 | 0.6829 | 0.4545 | 0.5225 |
| 0.0906 | 5.0 | 2500 | 0.1001 | 0.6825 | 0.4542 | 0.5221 |
Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
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