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text_shortening_model_v43
This model is a fine-tuned version of facebook/bart-large-xsum on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.8362
- Rouge1: 0.4977
- Rouge2: 0.2645
- Rougel: 0.4429
- Rougelsum: 0.4422
- Bert precision: 0.8744
- Bert recall: 0.8788
- Average word count: 8.5344
- Max word count: 18
- Min word count: 4
- Average token count: 15.9365
- % shortened texts with length > 12: 8.4656
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: 0.0001
- 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: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bert precision | Bert recall | Average word count | Max word count | Min word count | Average token count | % shortened texts with length > 12 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.5902 | 1.0 | 83 | 1.5909 | 0.4855 | 0.2475 | 0.4202 | 0.4201 | 0.8682 | 0.8736 | 8.5661 | 15 | 4 | 16.0635 | 3.9683 |
| 0.383 | 2.0 | 166 | 1.4957 | 0.516 | 0.2977 | 0.4569 | 0.4567 | 0.8751 | 0.881 | 8.8016 | 17 | 4 | 16.3519 | 8.4656 |
| 0.3301 | 3.0 | 249 | 1.6999 | 0.5073 | 0.2678 | 0.4401 | 0.4402 | 0.8662 | 0.8856 | 10.4233 | 22 | 5 | 17.9286 | 24.6032 |
| 0.3264 | 4.0 | 332 | 1.5703 | 0.5121 | 0.2818 | 0.4525 | 0.4527 | 0.8716 | 0.8844 | 9.1561 | 19 | 4 | 15.8704 | 12.4339 |
| 0.3901 | 5.0 | 415 | 1.6559 | 0.4875 | 0.2629 | 0.4362 | 0.4365 | 0.8661 | 0.8772 | 9.1111 | 16 | 5 | 15.2275 | 5.0265 |
| 0.2982 | 6.0 | 498 | 1.8927 | 0.499 | 0.267 | 0.4479 | 0.4476 | 0.8724 | 0.8824 | 9.0185 | 17 | 5 | 16.6376 | 10.0529 |
| 0.2864 | 7.0 | 581 | 1.8092 | 0.4961 | 0.2673 | 0.4377 | 0.4372 | 0.8705 | 0.8789 | 8.6614 | 17 | 5 | 14.4656 | 5.291 |
| 0.2059 | 8.0 | 664 | 2.0127 | 0.4921 | 0.2652 | 0.4408 | 0.4408 | 0.8729 | 0.8778 | 8.5899 | 16 | 4 | 15.2725 | 6.8783 |
| 0.1655 | 9.0 | 747 | 2.1199 | 0.4886 | 0.2697 | 0.4392 | 0.4391 | 0.8713 | 0.8777 | 8.7011 | 16 | 4 | 16.0132 | 7.4074 |
| 0.2361 | 10.0 | 830 | 2.0002 | 0.4814 | 0.2536 | 0.427 | 0.4257 | 0.8666 | 0.8769 | 8.9921 | 19 | 4 | 15.037 | 6.0847 |
| 0.2329 | 11.0 | 913 | 2.3033 | 0.4961 | 0.2725 | 0.4441 | 0.4426 | 0.8722 | 0.8775 | 8.6958 | 17 | 5 | 16.2619 | 10.582 |
| 0.1743 | 12.0 | 996 | 2.4562 | 0.499 | 0.275 | 0.4474 | 0.4477 | 0.8745 | 0.878 | 8.4127 | 17 | 4 | 15.873 | 9.2593 |
| 0.1716 | 13.0 | 1079 | 2.4160 | 0.4811 | 0.2528 | 0.4299 | 0.4297 | 0.8708 | 0.8751 | 8.4735 | 16 | 4 | 16.0873 | 6.0847 |
| 0.1394 | 14.0 | 1162 | 2.3996 | 0.4783 | 0.2445 | 0.4214 | 0.4205 | 0.8686 | 0.8735 | 8.6587 | 19 | 5 | 15.6376 | 8.9947 |
| 0.0769 | 15.0 | 1245 | 2.8364 | 0.4902 | 0.258 | 0.4369 | 0.4362 | 0.8697 | 0.8767 | 8.7222 | 18 | 4 | 16.4286 | 9.5238 |
| 0.1039 | 16.0 | 1328 | 2.5845 | 0.5009 | 0.267 | 0.4473 | 0.4464 | 0.8757 | 0.88 | 8.5291 | 18 | 4 | 16.0688 | 8.7302 |
| 0.098 | 17.0 | 1411 | 2.7602 | 0.491 | 0.2628 | 0.4379 | 0.4377 | 0.8711 | 0.8779 | 8.6587 | 18 | 4 | 16.2249 | 9.7884 |
| 0.0879 | 18.0 | 1494 | 2.6813 | 0.4987 | 0.2679 | 0.4468 | 0.4471 | 0.8761 | 0.8793 | 8.3862 | 18 | 4 | 15.4735 | 7.9365 |
| 0.0945 | 19.0 | 1577 | 2.8612 | 0.5034 | 0.2703 | 0.4489 | 0.449 | 0.8762 | 0.8806 | 8.5582 | 19 | 4 | 16.0873 | 8.4656 |
| 0.0702 | 20.0 | 1660 | 2.8362 | 0.4977 | 0.2645 | 0.4429 | 0.4422 | 0.8744 | 0.8788 | 8.5344 | 18 | 4 | 15.9365 | 8.4656 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
