plain-bart-on-presummarized-2-clusters-wcep
This model is a fine-tuned version of sshleifer/distilbart-cnn-6-6 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.0775
- Rouge1: 36.3774
- Rouge2: 15.2074
- Rougel: 25.7706
- Rougelsum: 29.2593
- Gen Len: 67.6608
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: 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 | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 2.2178 | 1.0 | 510 | 2.0873 | 36.3079 | 15.0162 | 25.5837 | 29.129 | 67.8461 |
| 1.8901 | 2.0 | 1020 | 2.0696 | 36.0914 | 15.0005 | 25.6729 | 29.2956 | 68.3451 |
| 1.7267 | 3.0 | 1530 | 2.0775 | 36.3774 | 15.2074 | 25.7706 | 29.2593 | 67.6608 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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Base model
sshleifer/distilbart-cnn-6-6