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text_shortening_model_v67
This model is a fine-tuned version of t5-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.2666
- Bert precision: 0.8838
- Bert recall: 0.884
- Bert f1-score: 0.8833
- Average word count: 6.5736
- Max word count: 15
- Min word count: 2
- Average token count: 10.3764
- % shortened texts with length > 12: 1.1011
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: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
Training results
| Training Loss | Epoch | Step | Validation Loss | Bert precision | Bert recall | Bert f1-score | Average word count | Max word count | Min word count | Average token count | % shortened texts with length > 12 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2.9368 | 1.0 | 37 | 2.3418 | 0.7561 | 0.7986 | 0.7754 | 9.2703 | 19 | 0 | 15.1782 | 20.1201 |
| 2.5152 | 2.0 | 74 | 2.0436 | 0.7442 | 0.7935 | 0.767 | 9.2282 | 18 | 0 | 15.5385 | 16.7167 |
| 2.2465 | 3.0 | 111 | 1.8345 | 0.7787 | 0.8112 | 0.7934 | 8.2032 | 18 | 0 | 14.022 | 11.9119 |
| 2.0439 | 4.0 | 148 | 1.6984 | 0.8124 | 0.8235 | 0.8169 | 7.1001 | 18 | 0 | 12.2923 | 9.1091 |
| 1.9424 | 5.0 | 185 | 1.6054 | 0.8413 | 0.8423 | 0.8409 | 6.7027 | 18 | 0 | 11.2633 | 6.8068 |
| 1.8546 | 6.0 | 222 | 1.5450 | 0.8565 | 0.856 | 0.8554 | 6.5696 | 18 | 0 | 10.8759 | 4.7047 |
| 1.7857 | 7.0 | 259 | 1.5035 | 0.8661 | 0.8646 | 0.8646 | 6.5095 | 18 | 0 | 10.6176 | 3.1031 |
| 1.7348 | 8.0 | 296 | 1.4694 | 0.8718 | 0.8699 | 0.8702 | 6.4995 | 18 | 0 | 10.4224 | 2.6026 |
| 1.6884 | 9.0 | 333 | 1.4427 | 0.8756 | 0.8723 | 0.8733 | 6.4284 | 15 | 0 | 10.2823 | 2.2022 |
| 1.6823 | 10.0 | 370 | 1.4227 | 0.878 | 0.8753 | 0.8761 | 6.4655 | 15 | 0 | 10.3003 | 1.7017 |
| 1.6475 | 11.0 | 407 | 1.4069 | 0.8799 | 0.8782 | 0.8784 | 6.5375 | 15 | 1 | 10.3634 | 1.8018 |
| 1.6363 | 12.0 | 444 | 1.3919 | 0.8812 | 0.8797 | 0.8798 | 6.5225 | 15 | 1 | 10.3554 | 1.7017 |
| 1.6086 | 13.0 | 481 | 1.3784 | 0.8815 | 0.8803 | 0.8803 | 6.5105 | 15 | 1 | 10.3463 | 1.6016 |
| 1.5953 | 14.0 | 518 | 1.3670 | 0.8814 | 0.8802 | 0.8802 | 6.5125 | 15 | 1 | 10.3333 | 1.6016 |
| 1.5812 | 15.0 | 555 | 1.3569 | 0.8814 | 0.8802 | 0.8802 | 6.4955 | 15 | 1 | 10.3113 | 1.5015 |
| 1.562 | 16.0 | 592 | 1.3480 | 0.8813 | 0.8803 | 0.8802 | 6.5205 | 15 | 1 | 10.3463 | 1.6016 |
| 1.5541 | 17.0 | 629 | 1.3396 | 0.8817 | 0.8812 | 0.8808 | 6.5576 | 15 | 1 | 10.3764 | 1.4014 |
| 1.5428 | 18.0 | 666 | 1.3316 | 0.8829 | 0.8823 | 0.882 | 6.5495 | 15 | 1 | 10.3754 | 1.3013 |
| 1.5476 | 19.0 | 703 | 1.3246 | 0.8829 | 0.8821 | 0.8819 | 6.5566 | 15 | 1 | 10.3654 | 1.5015 |
| 1.5234 | 20.0 | 740 | 1.3169 | 0.8831 | 0.8822 | 0.882 | 6.5576 | 15 | 1 | 10.3794 | 1.4014 |
| 1.5053 | 21.0 | 777 | 1.3120 | 0.8839 | 0.8828 | 0.8827 | 6.5576 | 15 | 2 | 10.3574 | 1.4014 |
| 1.5 | 22.0 | 814 | 1.3065 | 0.884 | 0.8831 | 0.883 | 6.5606 | 15 | 2 | 10.3574 | 1.4014 |
| 1.4954 | 23.0 | 851 | 1.3014 | 0.8839 | 0.8833 | 0.883 | 6.5696 | 15 | 2 | 10.3694 | 1.3013 |
| 1.4875 | 24.0 | 888 | 1.2974 | 0.8838 | 0.8834 | 0.883 | 6.5626 | 15 | 2 | 10.3634 | 1.3013 |
| 1.4896 | 25.0 | 925 | 1.2941 | 0.8842 | 0.8843 | 0.8836 | 6.5826 | 15 | 2 | 10.3874 | 1.3013 |
| 1.4769 | 26.0 | 962 | 1.2905 | 0.8845 | 0.8844 | 0.8839 | 6.5696 | 15 | 2 | 10.3684 | 1.2012 |
| 1.4684 | 27.0 | 999 | 1.2864 | 0.8845 | 0.8849 | 0.8841 | 6.5886 | 15 | 2 | 10.3854 | 1.1011 |
| 1.4721 | 28.0 | 1036 | 1.2830 | 0.8843 | 0.8845 | 0.8838 | 6.5766 | 15 | 2 | 10.3654 | 1.1011 |
| 1.4692 | 29.0 | 1073 | 1.2804 | 0.8842 | 0.8844 | 0.8837 | 6.5686 | 15 | 2 | 10.3604 | 1.1011 |
| 1.4732 | 30.0 | 1110 | 1.2778 | 0.8844 | 0.8846 | 0.8839 | 6.5796 | 15 | 2 | 10.3724 | 1.1011 |
| 1.4592 | 31.0 | 1147 | 1.2754 | 0.8843 | 0.8844 | 0.8838 | 6.5646 | 15 | 2 | 10.3664 | 1.1011 |
| 1.4381 | 32.0 | 1184 | 1.2735 | 0.8844 | 0.8844 | 0.8838 | 6.5536 | 15 | 2 | 10.3524 | 1.1011 |
| 1.4516 | 33.0 | 1221 | 1.2718 | 0.8842 | 0.8842 | 0.8836 | 6.5716 | 15 | 2 | 10.3724 | 1.1011 |
| 1.4459 | 34.0 | 1258 | 1.2705 | 0.884 | 0.8841 | 0.8834 | 6.5746 | 15 | 2 | 10.3814 | 1.1011 |
| 1.4393 | 35.0 | 1295 | 1.2695 | 0.8838 | 0.8839 | 0.8833 | 6.5706 | 15 | 2 | 10.3784 | 1.1011 |
| 1.4532 | 36.0 | 1332 | 1.2685 | 0.8837 | 0.8839 | 0.8832 | 6.5736 | 15 | 2 | 10.3814 | 1.1011 |
| 1.4327 | 37.0 | 1369 | 1.2675 | 0.8838 | 0.8839 | 0.8833 | 6.5756 | 15 | 2 | 10.3804 | 1.1011 |
| 1.447 | 38.0 | 1406 | 1.2671 | 0.8838 | 0.884 | 0.8833 | 6.5726 | 15 | 2 | 10.3754 | 1.1011 |
| 1.4416 | 39.0 | 1443 | 1.2667 | 0.8839 | 0.884 | 0.8834 | 6.5756 | 15 | 2 | 10.3784 | 1.1011 |
| 1.4337 | 40.0 | 1480 | 1.2666 | 0.8838 | 0.884 | 0.8833 | 6.5736 | 15 | 2 | 10.3764 | 1.1011 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
