t5-small-finetuned-summarization-billsum
This model is a fine-tuned version of google-t5/t5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.4994
- Rouge1: 0.1462
- Rouge2: 0.0513
- Rougel: 0.1219
- Rougelsum: 0.1216
- Gen Len: 20.0
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 OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 62 | 2.7903 | 0.1306 | 0.0364 | 0.1074 | 0.1072 | 20.0 |
| No log | 2.0 | 124 | 2.5808 | 0.1364 | 0.0426 | 0.112 | 0.1117 | 20.0 |
| No log | 3.0 | 186 | 2.5161 | 0.1445 | 0.0501 | 0.1203 | 0.1203 | 20.0 |
| No log | 4.0 | 248 | 2.4994 | 0.1462 | 0.0513 | 0.1219 | 0.1216 | 20.0 |
Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0