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distilbart-cnn-12-6-finetuned-roundup-4-4
This model is a fine-tuned version of sshleifer/distilbart-cnn-12-6 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.9444
- Rouge1: 53.2401
- Rouge2: 33.8737
- Rougel: 36.4695
- Rougelsum: 50.8979
- Gen Len: 141.5185
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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- 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 | 398 | 1.1590 | 52.4465 | 33.664 | 35.2295 | 50.0326 | 141.6852 |
| 1.4068 | 2.0 | 796 | 1.0174 | 53.3143 | 34.1363 | 35.8354 | 51.2277 | 141.8889 |
| 0.9247 | 3.0 | 1194 | 0.9575 | 52.7672 | 33.1797 | 35.9617 | 50.3643 | 142.0 |
| 0.731 | 4.0 | 1592 | 0.9444 | 53.2401 | 33.8737 | 36.4695 | 50.8979 | 141.5185 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
