Fine-Tuned-billsum-Summarization
This model is a fine-tuned version of google/pegasus-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.8618
- Rouge1: 0.2169
- Rouge2: 0.0916
- Rougel: 0.1809
- Rougelsum: 0.1799
- Generated Length: 104.8158
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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Generated Length |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 19 | 2.3003 | 0.2218 | 0.0946 | 0.1807 | 0.1792 | 106.5789 |
| No log | 2.0 | 38 | 1.9734 | 0.2164 | 0.0922 | 0.1799 | 0.1789 | 104.4474 |
| No log | 3.0 | 57 | 1.8618 | 0.2169 | 0.0916 | 0.1809 | 0.1799 | 104.8158 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
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Base model
google/pegasus-large