centralized-t5-small-billsum
This model is a fine-tuned version of t5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.9874
- Rouge1: 0.4956
- Rouge2: 0.2837
- Rougel: 0.3864
- Rougelsum: 0.4313
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| 2.5537 | 1.0 | 1125 | 2.1315 | 0.4851 | 0.2755 | 0.3751 | 0.4149 |
| 2.2928 | 2.0 | 2250 | 2.0491 | 0.4919 | 0.2806 | 0.3827 | 0.4267 |
| 2.2293 | 3.0 | 3375 | 2.0110 | 0.4919 | 0.2829 | 0.3845 | 0.4271 |
| 2.199 | 4.0 | 4500 | 1.9935 | 0.4937 | 0.2834 | 0.3841 | 0.4289 |
| 2.1853 | 5.0 | 5625 | 1.9874 | 0.4956 | 0.2837 | 0.3864 | 0.4313 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3