bart-large-cnn-samsum-dc
This model is a fine-tuned version of philschmid/bart-large-cnn-samsum on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.7404
- Rouge1: 32.5028
- Rouge2: 13.6008
- Rougel: 23.6102
- Rougelsum: 25.0002
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: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| 1.9176 | 1.0 | 2676 | 1.7297 | 31.7614 | 13.0816 | 22.9243 | 24.6866 |
| 1.4492 | 2.0 | 5352 | 1.5775 | 32.2161 | 13.4673 | 23.7824 | 25.0772 |
| 1.1499 | 3.0 | 8028 | 1.5778 | 33.1269 | 14.0686 | 24.2058 | 25.39 |
| 0.8947 | 4.0 | 10704 | 1.6344 | 32.9016 | 13.9786 | 24.1741 | 25.5371 |
| 0.6905 | 5.0 | 13380 | 1.7404 | 32.5028 | 13.6008 | 23.6102 | 25.0002 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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Base model
philschmid/bart-large-cnn-samsum