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bart-base-finetuned-xsum
This model is a fine-tuned version of facebook/bart-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5585
- Rouge1: 0.8859
- Rouge2: 0.8467
- Rougel: 0.8883
- Rougelsum: 0.8879
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: 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: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 41 | 0.4596 | 0.8845 | 0.849 | 0.8874 | 0.8865 |
| No log | 2.0 | 82 | 0.4047 | 0.8839 | 0.8466 | 0.8852 | 0.8836 |
| No log | 3.0 | 123 | 0.4587 | 0.8765 | 0.836 | 0.8783 | 0.8755 |
| No log | 4.0 | 164 | 0.4488 | 0.8785 | 0.8389 | 0.8811 | 0.8784 |
| No log | 5.0 | 205 | 0.4443 | 0.8564 | 0.8084 | 0.855 | 0.8543 |
| No log | 6.0 | 246 | 0.4643 | 0.8965 | 0.8614 | 0.8981 | 0.8965 |
| No log | 7.0 | 287 | 0.4782 | 0.8831 | 0.8468 | 0.885 | 0.8836 |
| No log | 8.0 | 328 | 0.4870 | 0.853 | 0.8051 | 0.8554 | 0.8541 |
| No log | 9.0 | 369 | 0.4766 | 0.9029 | 0.8659 | 0.9052 | 0.9027 |
| No log | 10.0 | 410 | 0.5023 | 0.8924 | 0.8528 | 0.895 | 0.8926 |
| No log | 11.0 | 451 | 0.5254 | 0.8689 | 0.8234 | 0.8699 | 0.8692 |
| No log | 12.0 | 492 | 0.4996 | 0.8833 | 0.8424 | 0.8851 | 0.8843 |
| 0.1489 | 13.0 | 533 | 0.5095 | 0.8747 | 0.8345 | 0.8762 | 0.8749 |
| 0.1489 | 14.0 | 574 | 0.5034 | 0.868 | 0.8226 | 0.8699 | 0.8689 |
| 0.1489 | 15.0 | 615 | 0.4976 | 0.8609 | 0.8112 | 0.8632 | 0.8617 |
| 0.1489 | 16.0 | 656 | 0.5122 | 0.9055 | 0.8722 | 0.9068 | 0.9069 |
| 0.1489 | 17.0 | 697 | 0.5204 | 0.845 | 0.7954 | 0.8482 | 0.8461 |
| 0.1489 | 18.0 | 738 | 0.5363 | 0.8911 | 0.8528 | 0.8934 | 0.8919 |
| 0.1489 | 19.0 | 779 | 0.5572 | 0.8943 | 0.8594 | 0.8963 | 0.8956 |
| 0.1489 | 20.0 | 820 | 0.5469 | 0.9031 | 0.8688 | 0.9047 | 0.9047 |
| 0.1489 | 21.0 | 861 | 0.5508 | 0.8848 | 0.8472 | 0.887 | 0.8869 |
| 0.1489 | 22.0 | 902 | 0.5579 | 0.8724 | 0.8306 | 0.8747 | 0.8737 |
| 0.1489 | 23.0 | 943 | 0.5508 | 0.8772 | 0.8397 | 0.8808 | 0.8803 |
| 0.1489 | 24.0 | 984 | 0.5658 | 0.8627 | 0.8153 | 0.8645 | 0.8637 |
| 0.0336 | 25.0 | 1025 | 0.5539 | 0.904 | 0.8702 | 0.9052 | 0.9058 |
| 0.0336 | 26.0 | 1066 | 0.5605 | 0.9004 | 0.8659 | 0.9026 | 0.9017 |
| 0.0336 | 27.0 | 1107 | 0.5589 | 0.899 | 0.8644 | 0.9012 | 0.9005 |
| 0.0336 | 28.0 | 1148 | 0.5558 | 0.8872 | 0.8488 | 0.8894 | 0.889 |
| 0.0336 | 29.0 | 1189 | 0.5570 | 0.8859 | 0.8467 | 0.8883 | 0.8879 |
| 0.0336 | 30.0 | 1230 | 0.5585 | 0.8859 | 0.8467 | 0.8883 | 0.8879 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0
- Datasets 2.8.0
- Tokenizers 0.13.2
