distilbart-cnn-12-6-sec
This model is a fine-tuned version of sshleifer/distilbart-cnn-12-6 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0798
- Rouge1: 72.1665
- Rouge2: 62.2601
- Rougel: 67.8376
- Rougelsum: 71.1407
- Gen Len: 121.62
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: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 99 | 0.3526 | 53.3978 | 38.6395 | 45.6271 | 51.0477 | 111.48 |
| No log | 2.0 | 198 | 0.1961 | 55.7397 | 43.6293 | 50.9595 | 54.0764 | 111.46 |
| No log | 3.0 | 297 | 0.1483 | 66.9443 | 54.8966 | 62.6678 | 65.6787 | 118.64 |
| No log | 4.0 | 396 | 0.1218 | 67.2661 | 56.1852 | 63.1339 | 65.8066 | 124.92 |
| No log | 5.0 | 495 | 0.1139 | 67.2097 | 55.8694 | 62.7508 | 65.9706 | 123.02 |
| 0.4156 | 6.0 | 594 | 0.0940 | 71.607 | 60.6697 | 66.7873 | 70.339 | 122.84 |
| 0.4156 | 7.0 | 693 | 0.0888 | 71.3792 | 61.8326 | 68.25 | 70.5113 | 124.4 |
| 0.4156 | 8.0 | 792 | 0.0870 | 72.7472 | 62.6968 | 68.2853 | 71.5789 | 124.34 |
| 0.4156 | 9.0 | 891 | 0.0799 | 73.4438 | 63.5966 | 68.8737 | 72.3014 | 119.88 |
| 0.4156 | 10.0 | 990 | 0.0798 | 72.1665 | 62.2601 | 67.8376 | 71.1407 | 121.62 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
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